Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-06T01:15:05.177Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 September 2013

David J. Stensrud
Affiliation:
National Oceanic and Atmospheric Administration, Norman, Oklahoma
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Parameterization Schemes
Keys to Understanding Numerical Weather Prediction Models
, pp. 408 - 448
Publisher: Cambridge University Press
Print publication year: 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adamec, D. and Elsberry, R. L. (1985). Numerical simulations of the response of intense ocean currents to atmospheric forcing. J. Phys. Oceanogr., 15, 273–287.2.0.CO;2>CrossRefGoogle Scholar
Adlerman, E. J. and Droegemeier, K. K. (2002). The sensitivity of numerically simulated cyclic mesocyclogenesis to variations in model physical and computational parameters. Mon. Wea. Rev., 130, 2671–2691.2.0.CO;2>CrossRefGoogle Scholar
Agee, E. and Gluhovsky, A. (1999). LES model sensitivities to domain, grids, and large-eddy timescales. J. Atmos. Sci., 56, 599–604.2.0.CO;2>CrossRefGoogle Scholar
Al Nakshabandi, G. and Kohnke, H. (1965). Thermal conductivity and diffusivity of soils as related to moisture tension and other physical properties. Agric. Meteor., 2, 271–279.CrossRefGoogle Scholar
Al-Saadi, J., Szykman, J., Pierce, R. B., et al. (2005). Improving national air quality forecasts with satellite aerosol observations. Bull. Amer. Meteor. Soc., 86, 1249–1261.CrossRefGoogle Scholar
Albrecht, B. A. (1989). Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 1227–1230.CrossRefGoogle ScholarPubMed
Albrecht, B. A., Ramanathan, V., and Boville, B. A. (1986). The effects of cumulus moisture transports on the simulation of climate with a general circulation model. J. Atmos. Sci., 43, 2443–2462.2.0.CO;2>CrossRefGoogle Scholar
Alexander, M. J. and Holton, J. R. (1997). A model study of zonal forcing in the equatorial stratosphere by convectively induced gravity waves. J. Atmos. Sci., 54, 408–419.2.0.CO;2>CrossRefGoogle Scholar
Alhamed, A., Lakshmivarahan, S., and Stensrud, D. J. (2002). Cluster analysis of multi-model ensemble data from SAMEX. Mon. Wea. Rev., 130, 226–256.2.0.CO;2>CrossRefGoogle Scholar
Alpaty, K., Pleim, J. E., Raman, S., Niyogi, D. S., and Byun, D. W. (1997). Simulation of atmospheric boundary layer processes using local- and nonlocal-closure schemes. J. Appl. Meteor., 36, 214–233.2.0.CO;2>CrossRefGoogle Scholar
Alpert, J. C., M. Kanamitsu, P. M. Caplan, et al. (1988). Mountain induced gravity wave drag parameterization in the NMC medium-range forecast model. In Eighth Conf. on Numerical Weather Prediction, Baltimore, MD. American Meteorological Society, pp. 726–733.
Andreas, E. L. (1992). Sea spray and the turbulent air–sea heat fluxes. J. Geophys. Res., 97, 11 429–11 441.CrossRefGoogle Scholar
Andreas, E. L.(1998). A new sea spray generation function for wind speed up to 32 m s− 1. J. Phys. Oceanogr., 28, 2175–2184.2.0.CO;2>CrossRefGoogle Scholar
Andreas, E. L. and J. DeCosmo (1999). Sea spray production and influence on air–sea heat and moisture fluxes over the open ocean. In Air–Sea Exchange: Physics, Chemistry, and Dynamics, ed. Geernaert, G. L.. Kluwer Academic Publishers, pp. 327–362.CrossRefGoogle Scholar
Andreas, E. L. and Emanuel, K. A. (2001). Effects of sea spray on tropical cyclone intensity. J. Atmos. Sci., 58, 3741–3751.2.0.CO;2>CrossRefGoogle Scholar
Andreas, E. L., Elson, J. B., Monahan, E. C., Rouault, M. P., and Smith, S. D. (1995). The sea spray contribution to net evaporation from the sea: a review of recent progress. Bound.-Layer Meteor., 72, 3–52.CrossRefGoogle Scholar
Anthes, R. A. (1977). A cumulus parameterization scheme utilizing a one-dimensional cloud model. Mon. Wea. Rev., 105, 270–286.2.0.CO;2>CrossRefGoogle Scholar
Anthes, R. A.(1982). Tropical Cyclones, Their Evolution, Structure and Effects. Meteorology Monographs, No. 41. American Meteorological Society.
Anthes, R. A.(1984). Enhancement of convective precipitation by mesoscale variations in vegetative covering in semiarid regions. J. Clim. Appl. Meteor., 23, 541–554.2.0.CO;2>CrossRefGoogle Scholar
Anthes, R. A. and Warner, T. T. (1978). Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev., 106, 1045–1078.2.0.CO;2>CrossRefGoogle Scholar
Anthes, R. A., E.-Y. Hsie, and Y.-H. Kuo (1987). Description of the Penn State/NCAR mesoscale model version 4 (MM4). NCAR Tech. Note NCAR/TN-282 + STR.
Arakawa, A. (1993). Closure assumptions in the cumulus parameterization problem. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society, pp. 1–15.CrossRef
Arakawa, A. and Schubert, W. H. (1974). Interaction of a cumulus cloud ensemble with the large-scale environment. Part I. J. Atmos. Sci., 31, 674–701.2.0.CO;2>CrossRefGoogle Scholar
Arpe, K., Dümenil, L., and Giorgetta, M. A. (1998). Variability of the Indian monsoon in the CHAM3 model: sensitivity to sea surface temperature, soil moisture, and the stratospheric quasi-biennial oscillation. J. Climate, 8, 1837–1858.CrossRefGoogle Scholar
Artaz, M.-A. and Andre, J.-C. (1980). Similarity studies of entrainment in convective mixed layers. Bound.-Layer Meteor., 19, 51–66.CrossRefGoogle Scholar
Asai, T. (1965). A numerical study of the air-mass transformation over the Japan Sea in winter. J. Meteor. Soc. Japan, 43, 1–15.CrossRefGoogle Scholar
Atger, F. (1999). The skill of ensemble prediction systems. Mon. Wea. Rev., 127, 1941–1953.2.0.CO;2>CrossRefGoogle Scholar
Atkinson, B. W. (1981). Meso-scale Atmospheric Circulations. Academic Press.Google Scholar
Atwater, M. A. and Ball, J. T. (1981). A surface solar radiation model for cloudy atmospheres. Mon. Wea. Rev., 109, 878–888.2.0.CO;2>CrossRefGoogle Scholar
Avissar, R. and Pielke, R. A. (1989). A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Mon. Wea. Rev., 117, 2113–2136.2.0.CO;2>CrossRefGoogle Scholar
Avissar, R., Avissar, P., Mahrer, Y., and Bravdo, B. (1985). A model to simulate response of plant stomata to environmental conditions. Agric. For. Meteor., 34, 21–29.CrossRefGoogle Scholar
Ayotte, K. W., Sullivan, P. P., Andren, A., et al. (1996). An evaluation of neutral and convective planetary boundary-layer parameterizations relative to large eddy simulations. Bound.-Layer Meteor., 79, 131–175.CrossRefGoogle Scholar
Bailey, M. and Hallett, J. (2004). Growth rates and habits of ice crystals between − 20° and − 70 °C. J. Atmos. Sci., 61, 514–544.2.0.CO;2>CrossRefGoogle Scholar
Baldocchi, D. and Rao, K. S. (1995). Intra-field variability of scalar flux densities across a transition between a desert and an irrigated potato field. Bound.-Layer Meteor., 76, 109–121.CrossRefGoogle Scholar
Baldwin, M. E., Kain, J. S., and Kay, M. P. (2002). Properties of the convection scheme in NCEP's Eta Model that affect forecast sounding interpretation. Wea. Forecasting, 17, 1063–1079.2.0.CO;2>CrossRefGoogle Scholar
Ball, J., Woodrow, I., and Berry, J. (1987). A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In Progress in Photosynthesis Research, Vol. IV. Martinus Nijhoff, pp. 221–224.CrossRefGoogle Scholar
Ballard, S. P., Golding, B., and Smith, R. N. B. (1991). Mesoscale model experimental forecasts of the haar of northeast Scotland. Mon. Wea. Rev., 119, 2107–2123.2.0.CO;2>CrossRefGoogle Scholar
Bane, J. M. and Osgood, K. E. (1989). Wintertime air–sea interaction processes across the Gulf Stream. J. Geophys. Res., 94, 10 755–10 772.CrossRefGoogle Scholar
Bao, J.-W., Wilczak, J. M., Choi, J.-K., and Kantha, L. H. (2000). Numerical simulations of air–sea interaction under high wind conditions using a coupled model: a study of hurricane development. Mon. Wea. Rev., 128, 2109–2210.2.0.CO;2>CrossRefGoogle Scholar
Bao, J.-W., Michelson, S. A., Neiman, P. J., Ralph, F. M., and Wilczak, J. M. (2006). Interpretation of enhanced integrated water vapor bands associated with extratropical cyclones: their formation and connection to tropical moisture. Mon. Wea. Rev., 134, 1063–1080.CrossRefGoogle Scholar
Barker, D. M., Huang, W., Guo, Y.-R., Bourgeois, A. J. and Xiao, Q. N. (2004). A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon. Wea. Rev., 132, 897–914.2.0.CO;2>CrossRefGoogle Scholar
Barker, H. W. and Davies, J. A. (1992). Solar radiative fluxes for stochastic, scaling invariant broken cloud field. J. Atmos. Sci., 49, 1115–1126.2.0.CO;2>CrossRefGoogle Scholar
Barker, H. W., Stephens, G. L., Partain, P. T., et al. (2003). Assessing 1D atmospheric solar radiative transfer models: interpretation and handling of unresolved clouds. J. Climate, 16, 2676–2699.2.0.CO;2>CrossRefGoogle Scholar
Bartels, D. L. and Maddox, R. A. (1991). Midlevel cyclonic vortices generated by mesoscale convective systems. Mon. Wea. Rev., 119, 104–118.2.0.CO;2>CrossRefGoogle Scholar
Barton, I. J. (1979). A parameterization of the evaporation from nonsaturated surface. J. Appl. Meteor., 18, 43–47.2.0.CO;2>CrossRefGoogle Scholar
Basara, J. B. (2000). The value of point-scale measurements of soil moisture in planetary boundary layer simulations. Ph.D. Dissertation, University of Oklahoma.
Basara, J. B. and Crawford, K. C. (2002). Linear relationships between root-zone soil moisture and atmospheric processes in the planetary boundary layer. J. Geophys. Res., 107, 10.1029/2001JD000633.CrossRefGoogle Scholar
Bazzaz, F. A. and Fajer, E. D. (1992). Plant life in a CO2-rich world. Sci. Amer., 266(1), 68–74.CrossRefGoogle Scholar
Beard, K. V. and Pruppacher, H. R. (1971). A wind tunnel investigation of the rate of evaporation of small water drops falling at terminal velocity in the air. J. Atmos. Sci., 28, 1455–1464.2.0.CO;2>CrossRefGoogle Scholar
Bechtold, P., Pinty, J. P., and Mascart, P. (1993). The use of partial cloudiness in a warm-rain parameterization: a subgrid-scale precipitation scheme. Mon. Wea. Rev., 121, 3301–3311.2.0.CO;2>CrossRefGoogle Scholar
Beljaars, A. C. M. and Holtslag, A. A. M. (1991). Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor., 30, 327–341.2.0.CO;2>CrossRefGoogle Scholar
Beljaars, A. C. M., Viterbo, P., Miller, M. J., and Betts, A. K. (1996). Anomalous rainfall over the United States during July 1993: sensitivity to land surface parameterization and soil moisture anomalies. Mon. Wea. Rev., 124, 362–383.2.0.CO;2>CrossRefGoogle Scholar
Benjamin, S. G. (1983). Some effects of surface heating and topography on the regional severe storm environment. Ph.D. Thesis, Department of Meteorology, The Pennsylvania State University, University Park, PA.
Benjamin, S. G. and Carlson, T. N. (1986). Some effects of surface heating and topography on the regional severe storm environment. Part I: Three-dimensional simulation. Mon. Wea. Rev., 114, 307–329.2.0.CO;2>CrossRefGoogle Scholar
Berg, L. K. and Stull, R. B. (2005). A simple parameteriztion coupling the convective daytime boundary layer and fair-weather cumuli. J. Atmos. Sci., 62, 1976–1988.CrossRefGoogle Scholar
Berry, E. X. (1965). Cloud droplet growth by condensation. Ph.D. Thesis, University of Nevada.
Berry, E. X.(1967). Cloud droplet growth by collection. J. Atmos. Sci., 24, 688–701.2.0.CO;2>CrossRefGoogle Scholar
Berry, E. X.(1968). Modification of the warm rain process. In Proc. Ist National Conf. on Weather Modification, Albany, NY. American Meteorological Society, pp. 81–88.Google Scholar
Berry, E. X. and Reinhardt, R. L. (1974a). An analysis of cloud drop growth by collection. Part I: Double distributions. J. Atmos. Sci., 31, 1814–1824.2.0.CO;2>CrossRefGoogle Scholar
Berry, E. X. and Reinhardt, R. L.(1974b). An analysis of cloud drop growth by collection. Part II: Single initial distributions. J. Atmos. Sci., 31, 2127–2135.2.0.CO;2>CrossRefGoogle Scholar
Betts, A. K. (1973). Non-precipitating cumulus convection and its parameterization. Quart. J. Roy. Meteor. Soc., 99, 178–196.CrossRefGoogle Scholar
Betts, A. K.(1983). Thermodynamics of mixed stratocumulus layers: saturation point budgets. J. Atmos. Sci., 40, 2655–2670.2.0.CO;2>CrossRefGoogle Scholar
Betts, A. K.(1985). Mixing line analysis of clouds and cloudy boundary layers. J. Atmos Sci., 42, 2751–2763.2.0.CO;2>CrossRefGoogle Scholar
Betts, A. K.(1986). A new convective adjustment scheme. Part I: Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112, 677–691.Google Scholar
Betts, A. K.(1992). FIFE atmospheric boundary layer budget methods. J. Geophys. Res., 97(D17), 18 523–18 531.CrossRefGoogle Scholar
Betts, A. K. and Miller, M. J. (1986). A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693–709.Google Scholar
Betts, A. K. and M. J. Miller(1993). The Betts–Miller scheme. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society, pp. 107–121.CrossRef
Betts, A. K. and Ball, J. H. (1997). Albedo over the boreal forest. J. Geophys. Res., 102, 28 901–28 909.CrossRefGoogle Scholar
Betts, A. K., Desjardins, R. L., MacPherson, J. I., and Kelly, R. D. (1990). Boundary layer heat and moisture budgets from FIFE. Bound.-Layer Meteor., 50, 109–137.CrossRefGoogle Scholar
Betts, A. K., Desjardins, R. L., and MacPherson, J. I. (1992). Budget analysis of the boundary layer grid flights during FIFE 1987. J. Geophys. Res., 97(D17), 18 533–18 546.CrossRefGoogle Scholar
Betts, A. K., Chen, F., Mitchell, K., and Janjic, Z. I. (1997). Assessment of the land surface and boundary layer models in two operational versions of the NCEP Eta Model using FIFE data. Mon. Wea. Rev., 125, 2896–2916.2.0.CO;2>CrossRefGoogle Scholar
Bhumralkar, C. M. (1975). Numerical experiments on the computation of ground surface temperature in an atmospheric general circulation model. J. Appl. Meteor., 14, 1246–1258.2.0.CO;2>CrossRefGoogle Scholar
Bitz, C. M. and Lipscomb, W. H. (1999). An energy-conserving thermodynamic model of sea ice. J. Geophys. Res., 104, 15 669–15 677.CrossRefGoogle Scholar
Black, T. L. (1994). The new NMC mesoscale Eta Model: description and forecast examples. Wea. Forecasting, 9, 265–284.2.0.CO;2>CrossRefGoogle Scholar
Blackadar, A. K. (1957). Boundary layer wind maxima and their significance for the growth of nocturnal inversions. Bull. Amer. Meteor. Soc., 38, 283–290.Google Scholar
Blackadar, A. K.(1976). Modeling the nocturnal boundary layer. In Proceedings 3rd Symposium on Atmospheric Turbulence, Diffusion and Air Quality. American Meteorological Society, pp. 46–49.
Blackadar, A. K.(1978). Modeling pollutant transfer during daytime convection. Preprints. In Fourth Symp. on Atmos. Turbulence, Diffusion and Air Quality, Reno, NV. American Meteorological Society, pp. 443–447.
Blackadar, A. K.(1979). High-resolution models of the planetary boundary layer. In Advances in Environmental Science and Engineering, vol. 1, ed. Pfafflin, J. R. and Ziegler, E. N.. Gordon and Breach Science Publishers, pp. 50–85.Google Scholar
Blyth, E. M. and Dolman, A. J. (1995). The roughness length for heat of sparse vegetation. J. Appl. Meteor., 54, 583–585.CrossRefGoogle Scholar
Boer, G. J., McFarlane, N. A., Laprise, R., Henderson, J. D., and Blanchet, J.-P. (1984). The Canadian Climate Centre spectral atmospheric general circulation model. Atmos.-Ocean, 22, 397–429.CrossRefGoogle Scholar
Bonan, G. B., Levis, S., Sitch, S., Vertenstein, M., and Oleson, K. W. (2003). A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation. Global Change Biol., 9, 1543–1566.CrossRefGoogle Scholar
Bony, S. and Emanuel, K. A. (2001). A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J. Atmos. Sci., 58, 3158–3183.2.0.CO;2>CrossRefGoogle Scholar
Bosart, L. F. (1999). Observed cyclone life cycles. In The Life Cycles of Extratropical Cyclones, ed. Shapiro, M. A. and Grønås, S.. American Meteorological Society, pp. 187–213.CrossRefGoogle Scholar
Bosart, L. F.(2003). Whither the weather analysis and forecasting process? Wea. Forecasting, 18, 520–529.2.0.CO;2>CrossRefGoogle Scholar
Bosseut, C., Déqué, M., and Cariolle, D. (1998). Impact of a simple parameterization of convective gravity-wave drag in a stratosphere–troposphere general circulation model and its sensitivity to vertical resolution. Ann. Geophysicae, 16, 238–239.CrossRefGoogle Scholar
Boucher, O., Schwartz, S. E., Ackerman, T. P., et al. (1998). Intercomparison of models representing direct shortwave radiative forcing by sulfate aerosols. J. Geophys. Res., 103, 16 979–16 998.CrossRefGoogle Scholar
Bourassa, M. A., Vincent, D. G., and Wood, W. L. (1999). A flux parameterization including the effects of capillary waves and sea state. J. Atmos. Sci., 56, 1123–1139.2.0.CO;2>CrossRefGoogle Scholar
Bourassa, M. A., Vincent, D. G., and Wood, W. L.(2001). A sea state parameterization with nonarbitrary wave age applicable to low and moderate wind speeds. J. Phys. Oceanogr., 31, 2840–2851.2.0.CO;2>CrossRefGoogle Scholar
Bowdle, D. A., Hobbs, P. V., and Radke, L. F. (1985). Particles in the lower troposphere over the High Plains of the United States. Part III: Ice nuclei. J. Clim. Appl. Meteor., 24, 1370–1376.2.0.CO;2>CrossRefGoogle Scholar
Bowen, I. S. (1926). The ratio of heat losses by conduction and by evaporation from any water surface. Phys. Rev., 27, 779–787.CrossRefGoogle Scholar
Bresch, J. F., Reed, R. J., and Albright, M. D. (1997). A polar-low development over the Bering Sea: analysis, numerical simulation, and sensitivity experiments. Mon. Wea. Rev., 125, 3109–3130.2.0.CO;2>CrossRefGoogle Scholar
Bretherton, C. S., McCaa, J. R., and Grenier, H. (2004). A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864–882.2.0.CO;2>CrossRefGoogle Scholar
Bretherton, F. P. (1969). Momentum transport by gravity waves. Quart. J. Roy. Meteor. Soc., 95, 213–243.CrossRefGoogle Scholar
Briegleb, B. P. (1992). Delta-Eddington approximation for solar radiation in the NCAR Community Climate Model. J. Geophys. Res., 97, 7603–7612.CrossRefGoogle Scholar
Bright, D. R. and Mullen, S. L. (2002). The sensitivity of the numerical simulation of the southwest monsoon boundary layer to the choice of PBL turbulence parameterization in MM5. Wea. Forecasting, 17, 99–114.2.0.CO;2>CrossRefGoogle Scholar
Brooks, D. A. (1983). The wake of Hurricane Allen in the western Gulf of Mexico. J. Phys. Oceanogr., 13, 117–129.2.0.CO;2>CrossRefGoogle Scholar
Brooks, H. E., Doswell, C. A. III, and Maddox, R. A. (1992). On the use of mesoscale and cloud-scale models in operational forecasting. Wea. Forecasting, 7, 120–132.2.0.CO;2>CrossRefGoogle Scholar
Brooks, H. E., Doswell, C. A. III, and Cooper, J. (1994b). On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606–618.2.0.CO;2>CrossRefGoogle Scholar
Brooks, H. E., Doswell, C. A. III, and Wilhelmson, R. B. (1994a). On the role of midtropospheric winds in the evolution and maintenance of low-level mesocyclones. Mon. Wea. Rev., 122, 126–136.2.0.CO;2>CrossRefGoogle Scholar
Brown, R. A. (1980). Longitudinal instabilities and secondary flows in the planetary boundary layer: a review. Rev. Geophys. Space Phys., 18, 683–697.CrossRefGoogle Scholar
Brutsaert, W. (1982). Evaporation into the Atmosphere. Theory, History, and Applications. Reidel.CrossRefGoogle Scholar
Bryan, G. H., Wyngaard, J. C., and Fritsch, J. M. (2003). Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 2394–2416.2.0.CO;2>CrossRefGoogle Scholar
Buermann, W., Dong, J., Zeng, X., Myneni, R. B., and Dickinson, R. E. (2001). Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations. J. Climate, 14, 3536–3550.2.0.CO;2>CrossRefGoogle Scholar
Buirez, J.-C., Bonnel, B., Fouquart, Y., Geleyn, J.-F., and Morcrette, J.-J. (1988). Comparison of model-generated and satellite-derived cloud cover and radiation budget. J. Geophys. Res., 93, 3705–3719.CrossRefGoogle Scholar
Buizza, R. (1997). Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon. Wea. Rev., 125, 99–119.2.0.CO;2>CrossRefGoogle Scholar
Buizza, R. and Palmer, T. N. (1995). The singular-vector structure of the atmospheric general circulation. J. Atmos. Sci., 52, 1434–1456.2.0.CO;2>CrossRefGoogle Scholar
Buizza, R., Miller, M., and Palmer, T. N. (1999). Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 2887–2908.CrossRefGoogle Scholar
Burnash, R. J., R. L. Ferral, and R. A. McGuire (1973). A generalized streamflow simulation system: conceptual modeling for digital computers. Technical Report, Joint Fed. and State River Forecast Center, Sacramento, CA.
Bush, B. C., Valero, F. P. J., Simpson, A. S., and Bignone, L. (2000). Characterization of thermal effects in pyranometers: a data correction algorithm for improved measurement of surface insolation. J. Atmos. Oceanic Technol., 17, 165–175.2.0.CO;2>CrossRefGoogle Scholar
Businger, S., Bauman, W. H. III, and Watson, G. F. (1991). The development of the Piedment front and associated outbreak of severe weather on 13 March 1986. Mon. Wea. Rev., 119, 2224–2251.2.0.CO;2>CrossRefGoogle Scholar
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F. (1971). Flux profile relationships in the atmospheric surface layer. J. Atmos. Sci., 28, 181–189.2.0.CO;2>CrossRefGoogle Scholar
Byers, H. R. (1965). Elements of Cloud Physics. The University of Chicago Press.Google Scholar
Cahalan, R. F. and Joseph, J. H. (1989). Fractal statistics of cloud fields. Mon. Wea. Rev., 117, 261–272.2.0.CO;2>CrossRefGoogle Scholar
Cahalan, R. F., Oreopoulos, L., Marshak, A., et al. (2005). The I3RC: bringing together the most advanced radiative transfer tools for cloudy atmospheres. Bull. Amer. Meteor. Soc., 86, 1275–1293.CrossRefGoogle Scholar
Camacho-B, S. E., Hall, A. E., and Kaufmann, M. R. (1974). Efficiency and regulation of water transport in some woody and herbaceous species. Plant Physiol., 54, 169–172.CrossRefGoogle ScholarPubMed
Campbell, G. S. (1974). A simple method for determining unsaturated conductivity from moisture retention data. Water Resources Res., 12, 1118–1124.Google Scholar
Canny, M. J. (1998). Transporting water in plants. Amer. Scientist, 86, 152–160.CrossRefGoogle Scholar
Cantelaube, P. and Terres, J.-M. (2005). Seasonal weather forecasts for crop yield modelling in Europe. Tellus, 57A, 476–487.CrossRefGoogle Scholar
Capehart, W. J. and Carlson, T. N. (1994). Estimating near-surface moisture availability using a meteorologically driven soil–water profile model. J. Hydrol., 160, 1–20.CrossRefGoogle Scholar
Capehart, W. J. and Carlson, T. N.(1997). Decoupling of surface and near-surface soil water content: a remote sensing perspective. Water Resources Res., 33, 1383–1395.CrossRefGoogle Scholar
Carlson, T. N. and Boland, F. E. (1978). Analysis of urban–rural canopy using a surface heat flux/temperature model. J. Appl. Meteor., 17, 998–1013.2.0.CO;2>CrossRefGoogle Scholar
Carlson, T. N. and Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62, 241–252.CrossRefGoogle Scholar
Carlson, T. N., Benjamin, S. G., and Forbes, G. S. (1983). Elevated mixed layers in the regional severe storm environment: conceptual model and case studies. Mon. Wea. Rev., 111, 1453–1474.2.0.CO;2>CrossRefGoogle Scholar
Carruthers, D. J. and J. C. R. Hunt (1990). Fluid mechanics of airflow over hills: turbulence, fluxes, and waves in the boundary layer. In Atmospheric Processes over Complex Terrain, ed. W. Blumen. Meteorology Monographs, No. 45. American Meteorological Society, pp. 83–107.CrossRef
Carson, D. J. (1973). The development of a dry inversion-capped convectively unstable boundary layer. Quart. J. Roy. Meteor. Soc., 99, 450–467.CrossRefGoogle Scholar
Carson, R. B. (1950). The Gulf Stream front: a cause of stratus on the lower Atlantic coast. Mon. Wea. Rev., 78, 91–101.2.0.CO;2>CrossRefGoogle Scholar
Cary, J. W. and Maryland, H. F. (1972). Salt and water movement in unsaturated frozen soil. Soil Sci. Soc. Amer. Proc., 36, 549–555.CrossRefGoogle Scholar
Cavalieri, D. J., et al. (1991). Aircraft active and passive microwave validation of sea-ice concentration from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager. J. G. R. Oceans, 96(C12), 21 989–22 008.CrossRefGoogle Scholar
Cess, R. D., Potter, G. L., Blanchet, J. P., et al. (1990). Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16 601–16 615.CrossRefGoogle Scholar
Chaboureau, J.-P. and Bechtold, P. (2002). A simple cloud parameterization derived from cloud resolving model data: diagnostic and prognostic applications. J. Atmos. Sci., 59, 2362–2372.2.0.CO;2>CrossRefGoogle Scholar
Challinor, A. J., Slingo, J. M., Wheeler, T. R., and Doblas-Reyes, F. J. (2005). Probabilistic simulations of crop yield over western Indian using the DEMETER seasonal hindcast ensembles. Tellus, 57A, 498–512.CrossRefGoogle Scholar
Chang, F.-L. and Coakley, J. A. (1993). Estimating errors in fractional cloud cover obtained with infrared threshold methods. J. Geophys. Res., 98, 8825–8839.CrossRefGoogle Scholar
Chang, J.-T. and Wetzel, P. J. (1991). Effects of spatial variations of soil moisture and vegetation to the evolution of a prestorm environment: a numerical case study. Mon. Wea. Rev., 119, 1368–1390.2.0.CO;2>CrossRefGoogle Scholar
Charlock, T. and Herman, B. (1976). Discussion of the Elsasser formulation for infrared fluxes. J. Appl. Meteor., 15, 657–661.2.0.CO;2>CrossRefGoogle Scholar
Charney, J. G., Fjørtoft, R., and Neuman, J. (1950). Numerical integration of the barotropic vorticity equation. Tellus, 2, 237–254.CrossRefGoogle Scholar
Charnock, H. (1955). Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639–640.CrossRefGoogle Scholar
Chase, T. N., Pielke, R. A. Sr., Kittel, T. G. F., et al. (2001). Relative climatic effects of landcover change and elevated carbon dioxide combined with aerosols: a comparison of model results and observations. J. Geophys. Res., 106, 31 685–31 691.CrossRefGoogle Scholar
Chelton, D. B. and Wentz, F. J. (2005). Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research. Bull. Amer. Meteor. Soc., 86, 1097–1115.CrossRefGoogle Scholar
Chen, F. and Dudhia, J. (2001). Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.2.0.CO;2>CrossRefGoogle Scholar
Chen, F. and Mitchell, K. (1999). Using GEWEX/ISLSCP forcing data to simulate global soil moisture fields and hydrological cycle for 1987–1988. J. Meteor. Soc. Japan, 77, 1–16.CrossRefGoogle Scholar
Chen, F., Mitchell, K., Schaake, J., et al. (1996). Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251–7268.CrossRefGoogle Scholar
Chen, S. and Cotton, W. R. (1988). The sensitivity of a simulated extratropical mesoscale convective system to longwave radiation and ice phase microphysics. J. Atmos. Sci., 45, 3897–3910.2.0.CO;2>CrossRefGoogle Scholar
Chen, T. H., Henderson-Sellers, A., Milly, P. C. D., et al. (1997). Cabauw experimental results from the Project for Intercomparison of Land-Surface Parameterization Schemes. J. Climate, 10, 1194–1215.2.0.CO;2>CrossRefGoogle Scholar
Cheng, C.-P. and Houze, R. A. Jr. (1979). The distribution of convective and mesoscale precipitation in GATE radar echo patterns. Mon. Wea. Rev., 107, 1370–1381.2.0.CO;2>CrossRefGoogle Scholar
Chevallier, F. and Morcrette, J.-J. (2000). Comparison of model fluxes with surface and top-of-the-atmosphere observations. Mon. Wea. Rev., 128, 3839–3852.2.0.CO;2>CrossRefGoogle Scholar
Chouinard, C., Beland, M., and McFarlane, N. (1986). A simple gravity-wave drag parameterization for use in medium-range weather forecast models. Atmos.-Ocean, 24, 91–110.CrossRefGoogle Scholar
Chun, H.-Y. and Baik, J.-J. (1998). Momentum flux by thermally induced internal gravity waves and its approximation for large-scale models. J. Atmos. Sci., 55, 3299–3310.2.0.CO;2>CrossRefGoogle Scholar
Chun, H.-Y. and Baik, J.-J.(2002). An updated parameterization of convectively forced gravity wave drag for use in large-scale models. J. Atmos. Sci., 59, 1006–1017.2.0.CO;2>CrossRefGoogle Scholar
Chylek, P. and Dobbie, J. S. (1995). Radiative properties of finite inhomogeneous cirrus clouds: Monte Carlo simulations. J. Atmos. Sci., 52, 3512–3522.2.0.CO;2>CrossRefGoogle Scholar
Clapp, R. B. and Hornberger, G. M. (1978). Empirical equations for some soil hydraulic properties. Water Resources Res., 14, 601–604.CrossRefGoogle Scholar
Clark, T. (1973). Numerical modeling of the dynamics and microphysics of warm cumulus convection. J. Atmos. Sci., 30, 857–878.2.0.CO;2>CrossRefGoogle Scholar
Clark, T.(1974). A study in cloud phase parameterization using the gamma distribution. J. Atmos. Sci., 31, 142–155.2.0.CO;2>CrossRefGoogle Scholar
Clark, T.(1976). Use of log-normal distributions for numerical calculations of condensation and collection. J. Atmos. Sci., 33, 810–821.2.0.CO;2>CrossRefGoogle Scholar
Clark, T. and Miller, M. J. (1991). Pressure drag and momentum fluxes due to the Alps. II: Representation in large-scale atmospheric models. Quart. J. Roy. Meteor. Soc., 117, 527–552.CrossRefGoogle Scholar
Coakley, J. A. and Chylek, P. (1975). The two stream approximation in radiative transfer: including the angle of incident radiation. J. Atmos. Sci., 32, 409–418.2.0.CO;2>CrossRefGoogle Scholar
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A. (1991). Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agric. For. Meteor., 54, 107–136.CrossRefGoogle Scholar
Colle, B. A., Mass, C. F., and Westrick, K. J. (2000). MM5 precipitation verification over the Pacific Northwest during the 1997–99 cool seasons. Wea. Forecasting, 15, 730–744.2.0.CO;2>CrossRefGoogle Scholar
Colle, B. A., Olson, J. B., and Tongue, J. S. (2003). Multiseason verification of the MM5. Part I: Comparison with the Eta model over the central and eastern United States and impact of MM5 resolution. Wea. Forecasting, 18, 431–457.2.0.CO;2>CrossRefGoogle Scholar
Colle, B. A., Garvert, M. F., Wolfe, J. B., et al. (2005b). The 13–14 December 2001 IMPROVE-2 event. Part III: Simulated microphysical budgets and sensitivity studies. J. Atmos. Sci., 62, 3535–3558.CrossRefGoogle Scholar
Colle, B. A., Wolfe, J. B., Steenburgh, W. J., et al. (2005a). High-resolution simulations and microphysical validation of an orographic precipitation event over the Wasatch Mountains during IPEX IOP3. Mon. Wea. Rev., 133, 2947–2971.CrossRefGoogle Scholar
Coniglio, M. C. and Stensrud, D. J. (2001). Simulation of a progressive derecho using composite initial conditions. Mon. Wea. Rev., 129, 1593–1616.2.0.CO;2>CrossRefGoogle Scholar
Cooper, W. A. (1986). Ice initiation in natural clouds. In Precipitation Enhancement – A Scientific Challenge. Meteorology Monographs, No. 43. American Meteorological Society, pp. 29–32.CrossRef
Cortinas, J. V. Jr. and Stensrud, D. J. (1995). The importance of understanding mesoscale model parameterization schemes for weather forecasting. Wea. Forecasting, 10, 716–740.2.0.CO;2>CrossRefGoogle Scholar
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R. (1984). A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Res., 20, 682–690.CrossRefGoogle Scholar
Cotton, W. R. (1972). Numerical simulation of precipitation development in supercooled cumuli – Part I. Mon. Wea. Rev., 100, 757–763.2.3.CO;2>CrossRefGoogle Scholar
Cotton, W. R. and Anthes, R. A. (1989). Storm and Cloud Dynamics. Academic Press.Google Scholar
Cotton, W. R., Thompson, G., and Mielke, P. W. (1994). Realtime mesoscale prediction on workstations. Bull. Amer. Meteor. Soc., 75, 349–362.2.0.CO;2>CrossRefGoogle Scholar
Cox, P. M., Huntingford, C., and Harding, R. J. (1998). A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol., 212–213, 79–94.CrossRefGoogle Scholar
Cox, S. K. and Griffith, K. T. (1979). Estimates of radiative divergence during Phase III of the GARP Atlantic Tropical Experiment. Part II: Analysis of Phase III results. J. Atmos. Sci., 36, 586–601.2.0.CO;2>CrossRefGoogle Scholar
Crawford, T. M., Stensrud, D. J., Carlson, T. N., and Capehart, W. J. (2000). Using a soil hydrology model to obtain regionally averaged soil moisture values. J. Hydrometeor., 1, 353–363.2.0.CO;2>CrossRefGoogle Scholar
Csanady, G. T. (2001). Air–Sea Interaction. Cambridge University Press.CrossRefGoogle Scholar
Curtis, A. R. (1952). Discussion of “A statistical model for water vapour absorption” by R. M. Goody. Quart. J. Roy. Meteor. Soc., 78, 638.Google Scholar
Cusack, S., Edwards, J. M., and Kershaw, R. (1999). Estimating the subgrid variance of saturation, and its parameterization for use in a GCM cloud scheme. Quart. J. Roy. Meteor. Soc., 125, 3057–3076.CrossRefGoogle Scholar
Dai, Y., Dickinson, R. E., and Wang, Y.-P. (2004). A two-big-leaf model for canopy temperature, photosynthesis, and stomatal conductance. J. Climate, 17, 2281–2299.2.0.CO;2>CrossRefGoogle Scholar
Dai, Y., Zeng, X., Dickinson, R. E., et al. (2003). The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1023.CrossRefGoogle Scholar
Daley, R. (1991). Atmospheric Data Analyses. Cambridge University Press.Google Scholar
Daly, E., Porporato, A., and Rodriguez-Iturbe, I. (2004). Coupled dynamics of photosynthesis, transpiration, and soil water balance. Part I: Upscaling from hourly to daily level. J. Hydrometeor., 5, 546–558.2.0.CO;2>CrossRefGoogle Scholar
Davies, J. A. and Allen, C. D. (1973). Equilibrium, potential and actual evaporation from cropped surfaces in southern Ontario. J. Appl. Meteor., 12, 649–657.2.0.CO;2>CrossRefGoogle Scholar
Davis, C. A., Manning, K. W., Carbone, R. E., Trier, S. B., and Tuttle, J. D. (2003). Coherence of warm-season continental rainfall in numerical weather prediction models. Mon. Wea. Rev., 131, 2667–2679.2.0.CO;2>CrossRefGoogle Scholar
Davis, M. H. and Sartor, J. D. (1967). Theoretical collision efficiencies for small cloud droplets in Stokes flow. Nature, 215, 1371–1372.CrossRefGoogle Scholar
Deardorff, J. W. (1966). The counter-gradient heat flux in the lower atmosphere and in the laboratory. J. Atmos. Sci., 23, 503–506.2.0.CO;2>CrossRefGoogle Scholar
Deardorff, J. W.(1972). Theoretical expression for the countergradient vertical heat flux. J. Geophys. Res., 77, 5900–5904.CrossRefGoogle Scholar
Deardorff, J. W.(1978). Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res., 83, 1889–1903.CrossRefGoogle Scholar
Deardorff, J. W.(1979). Prediction of convective mixed-layer entrainment for realistic capping inversion structure. J. Atmos. Sci., 36, 424–436.2.0.CO;2>CrossRefGoogle Scholar
Deardorff, J. W., Willis, G. E., and Lilly, D. K. (1969). Laboratory investigation of non-steady penetrative convection. J. Fluid. Mech., 35, 7–31.CrossRefGoogle Scholar
DeFries, R. S. and Townshend, J. R. G. (1994). NDVI derived land cover classifications at a global scale. Int. J. Remote Sens., 5, 3567–3586.CrossRefGoogle Scholar
Del Genio, A. D., Yao, M. S., Kovari, W., and Lo, K. W. W. (1996). A prognostic cloud water parameterization for global climate models. J. Climate, 9, 270–304.2.0.CO;2>CrossRefGoogle Scholar
DeMott, P. J., Meyers, M. P., and Cotton, W. R. (1994). Parameterization and impact of ice initiation processes relevant to numerical model simulations of cirrus clouds. J. Atmos. Sci., 51, 77–90.2.0.CO;2>CrossRefGoogle Scholar
Deng, A. and Stauffer, D. R. (2006). On improving 4-km mesoscale model simulations. J. Appl. Meteor. Climatology, 45, 361–381.CrossRefGoogle Scholar
Deng, A., Seaman, N. L., and Kain, J. S. (2003). A shallow-convection parameterization for mesoscale models. Part I: Submodel description and preliminary applications. J. Atmos. Sci., 60, 34–56.2.0.CO;2>CrossRefGoogle Scholar
Dennis, A. S., Smith, P. L. Jr., Peterson, G. A. P., and McNeil, R. D. (1971). Hailstone size distributions and equivalent radar reflectivity factors computed from hailstone momentum records. J. Appl. Meteor., 10, 79–85.2.0.CO;2>CrossRefGoogle Scholar
Derber, J. C. (1989). A variational continuous assimilation technique. Mon. Wea. Rev., 117, 2437–2446.2.0.CO;2>CrossRefGoogle Scholar
Desborough, C. E. (1997). The impact of root weighting on the response of transpiration to moisture stress in land surface schemes. Mon. Wea. Rev., 125, 1920–1930.2.0.CO;2>CrossRefGoogle Scholar
Dickinson, R. E. (1983). Land surface processes and climate-surface albedos and energy balance. In Advances in Geophysics, vol. 25. Academic Press, pp. 305–353.CrossRefGoogle Scholar
Dickinson, R. E.(1984). Modeling evapotranspiration for three-dimensional global climate models. In Climate Processes and Climate Sensitivity, ed. Hansen, J. E. and Takahashi, T.. American Geophysical Union, pp. 58–72.CrossRefGoogle Scholar
Ding, P. and Randall, D. A. (1998). A cumulus parameterization with multiple cloud base levels. J. Geophys. Res., 103, 11 341–11 354.CrossRefGoogle Scholar
Donner, L. J. (1993). A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects. J. Atmos. Sci., 50, 889–906.2.0.CO;2>CrossRefGoogle Scholar
Dopplick, T. G. (1972). Radiative heating of the global atmosphere. J. Atmos. Sci., 29, 1278–1294.2.0.CO;2>CrossRefGoogle Scholar
Doswell, C. A. III and Brooks, H. E. (1998). Budget cutting and the value of weather services. Wea. Forecasting, 13, 206–212.2.0.CO;2>CrossRefGoogle Scholar
Douglas, M. W. (1993). Current research into the monsoon. Sonorensis, Arizona Sonora Desert Museum Newsletter, 13(3), 10–11.Google Scholar
Douville, H. (2003). Assessing the influence of soil moisture on seasonal climate variability with AGCMs. J. Hydrometeor., 4, 1044–1066.2.0.CO;2>CrossRefGoogle Scholar
Dowell, D. C., Zhang, F., Wicker, L. J., Snyder, C., and Crook, N. A. (2004). Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma supercell: ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982–2005.2.0.CO;2>CrossRefGoogle Scholar
Doyle, J. D. (1995). Coupled ocean wave/atmosphere mesoscale model simulations of cyclogenesis. Tellus, 47A, 766–788.CrossRefGoogle Scholar
Doyle, J. D.(2002). Coupled atmosphere–ocean wave simulations under high wind conditions. Mon. Wea. Rev., 130, 3087–3099.2.0.CO;2>CrossRefGoogle Scholar
Doyle, J. D. and Warner, T. T. (1990). Mesoscale processes during GALE IOP 2. Mon. Wea. Rev., 118, 283–308.2.0.CO;2>CrossRefGoogle Scholar
Doyle, J. D., Durran, D. R., Chen, C., et al. (2000). An intercomparison of model-predicted wave breaking for the 11 January 1972 Boulder windstorm. Mon. Wea. Rev., 128, 901–914.2.0.CO;2>CrossRefGoogle Scholar
Drake, V. A. (1985). Radar observations of moths migrating in a nocturnal low-level jet. Ecol. Entomol., 10, 259–265.CrossRefGoogle Scholar
Dubosclard, G. (1980). A comparison between observed and predicted values for the entrainment coefficient in the planetary boundary layer. Bound.-Layer Meteor., 18, 473–483.CrossRefGoogle Scholar
Dubovik, O. and King, M. D. (2000). A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. J. Geophys. Res., 105, 20 673–20 696.CrossRefGoogle Scholar
Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107.2.0.CO;2>CrossRefGoogle Scholar
Durran, D. R. (1986). Mountain waves. In Mesoscale Meteorology and Forecasting, ed. Ray, P.. American Meteorological Society, pp. 472–492.CrossRefGoogle Scholar
Durran, D. R.(1990). Mountain waves and downslope winds. In Atmospheric Processes over Complex Terrain, ed. W. Blumen. Meteorology Monographs, No. 45. American Meteorological Society, pp. 59–81.CrossRef
Durran, D. R.(1999). Numerical Methods for Wave Equations in Geophysical Fluid Dynamics. Springer-Verlag.CrossRefGoogle Scholar
Durran, D. R. and Klemp, J. B. (1982). The effects of moisture on trapped mountain lee waves. J. Atmos. Sci., 39, 2490–2506.2.0.CO;2>CrossRefGoogle Scholar
Dusek, U., Frank, G. P., Hildebrandt, L., et al. (2006). Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science, 312, 1375–1378.CrossRefGoogle ScholarPubMed
Dutton, E. G., Michalsky, J. J., Stoffel, T., et al. (2001). Measurements of broadband diffuse solar irradiance using current commercial instrumentation with a correction for thermal offset errors. J. Atmos. Oceanic Technol., 18, 297–314.2.0.CO;2>CrossRefGoogle Scholar
Dutton, J. A. (1976). The Ceaseless Wind: an Introduction to the Theory of Atmospheric Motion. McGraw-Hill.Google Scholar
Dutton, J. A. and Fichtl, G. H. (1969). Approximate equations of motion for gases and liquids. J. Atmos. Sci., 26, 241–254.2.0.CO;2>CrossRefGoogle Scholar
Dyer, A. J. (1963). The adjustment of profiles and eddy fluxes. Quart. J. Roy. Meteor. Soc., 89, 276–280.CrossRefGoogle Scholar
Dyer, A. J.(1974). A review of flux-profile relations. Bound.-Layer Meteor., 1, 363–372.CrossRefGoogle Scholar
Ebert, E. E. and Curry, J. A. (1992). A parameterization of ice cloud optical properties for climate models, J. Geophys. Res., 97, 3831–3836.CrossRefGoogle Scholar
Ebert, E. E. and Curry, J. A.(1993). An intermediate one-dimensional thermodynamic sea ice model for investigating ice–atmosphere interactions. J. Geophys. Res., 98, 10 085–10 109.CrossRefGoogle Scholar
Ebert, E. E., Schumann, U., and Stull, R. B. (1989). Nonlocal turbulent mixing in the convective boundary layer evaluated from large-eddy simulation. J. Atmos. Sci., 46, 2178–2207.2.0.CO;2>CrossRefGoogle Scholar
Edwards, J. M. and Slingo, A. (1996). Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Quart. J. Roy. Meteor. Soc., 122, 689–719.CrossRefGoogle Scholar
Ek, M. B., Mitchell, K. E., Lin, Y., et al. (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi: 10.1029/2002JD003296.CrossRefGoogle Scholar
Eliassen, A. and Palm, E. (1961). On the transfer of energy in stationary mountain waves. Geofys. Publ., 22, 1–23.Google Scholar
Ellingston, R. G., Ellis, J., and Fels, S. (1991). Intercomparison of radiation codes used in climate models: long wave results. J. Geophys. Res., 96, 8929–8953.CrossRefGoogle Scholar
Elsasser, W. M. and M. F. Culbertson (1960). Atmospheric Radiation Tables. Meteorology Monographs, No. 4. American Meteorological Society.
Emanuel, K. A. (1986). An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585–605.2.0.CO;2>CrossRefGoogle Scholar
Emanuel, K. A.(1991) A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 2313–2335.2.0.CO;2>CrossRefGoogle Scholar
Emanuel, K. A.(1994) Atmospheric Convection. Oxford University Press.Google Scholar
Emanuel, K. A. and D. J. Raymond (1993). The Representation of Cumulus Convection in Numerical Models, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society.CrossRef
Emanuel, K. A. and Zivkovic-Rothman, M. (1999). Development and evaluation of a convection scheme for use in climate models. J. Atmos. Sci., 56, 1766–1782.2.0.CO;2>CrossRefGoogle Scholar
Epstein, E. S. (1969). Stochastic dynamic prediction. Tellus, 21, 739–759.CrossRefGoogle Scholar
Errico, R. and Baumhefner, D. P. (1987). Predictability experiments using a high-resolution limited-area model. Mon. Wea. Rev., 115, 488–504.2.0.CO;2>CrossRefGoogle Scholar
Estoque, M. A. (1968). Vertical mixing due to penetrative convection. J. Atmos. Sci., 25, 1046–1051.2.0.CO;2>CrossRefGoogle Scholar
Eumas, D. and Jarvis, P. G. (1989). The direct effects of increase in the global atmospheric CO2 concentration on natural and commercial temperate trees and forests. Adv. Ecol. Res., 19, 1–55.Google Scholar
Evans, K. F. (1998). The spherical harmonics discrete ordinate method for three-dimensional atmospheric radiative transfer. J. Atmos. Sci., 55, 429–446.2.0.CO;2>CrossRefGoogle Scholar
Evans, R. E., Harrison, M. S. J., Graham, R. J., and Mylne, K. R. (2000). Joint medium-range ensembles from the Met. Office and ECMWF systems. Mon. Wea. Rev., 128, 3104–3127.2.0.CO;2>CrossRefGoogle Scholar
Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162.CrossRefGoogle Scholar
Evensen, G.(1997). Advanced data assimilation for strongly nonlinear dynamics. Mon. Wea. Rev., 125, 1342–1354.2.0.CO;2>CrossRefGoogle Scholar
Fairall, C. W., Kepert, J. D., and Holland, G. J. (1994). The effect of sea spray on surface energy transports over the ocean. Global Atmos. Ocean Syst., 2, 121–142.Google Scholar
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J. B. (2003). Bulk parameterization of air–sea fluxes: updates and verification for the COARE algorithm. J. Climate, 16, 571–591.2.0.CO;2>CrossRefGoogle Scholar
Fawbush, E. J. and Miller, R. C. (1954). The types of air masses in which North American tornadoes form. Bull. Amer. Meteor. Soc., 4, 154–165.Google Scholar
Feddema, J. J., Oleson, K. W., Bonan, G. B., et al. (2005a). The importance of land-cover change in simulating future climates. Science, 310, 1674–1678.CrossRefGoogle Scholar
Feddema, J. J., Oleson, K. W., Bonan, G. B., et al. (2005b). A comparison of a GCM response to historical anthropogenic land cover change and model sensitivity to uncertainty in present-day land cover representations. Clim. Dyn., 25, 581–609.CrossRefGoogle Scholar
Federer, B. and Waldvogel, A. (1975). Hail and raindrop size distributions from a Swiss multicell storm. J. Appl. Meteor., 14, 91–97.2.0.CO;2>CrossRefGoogle Scholar
Ferrier, B. S. (1994). A double-moment multiple-phase four-class bulk ice scheme. Part I: Description. J. Atmos. Sci., 51, 249–280.2.0.CO;2>CrossRefGoogle Scholar
Ferrier, B. S., Tao, W.-K., and Simpson, J. (1995). A double-moment multiple-phase four-class bulk ice scheme. Part II: Simulations of convective storms in different large-scale environments and comparisons with other bulk parameterizations. J. Atmos. Sci., 52, 1001–1033.2.0.CO;2>CrossRefGoogle Scholar
Fels, S. and Schwarzkopf, M. D. (1975). The simplified exchange approximation. A new method for radiative transfer calculations. J. Atmos. Sci., 32, 1475–1488.2.0.CO;2>CrossRefGoogle Scholar
Fisher, E. L. (1958). Hurricane and the sea surface temperature field. J. Meteor., 15, 328–333.2.0.CO;2>CrossRefGoogle Scholar
Flerchinger, G. N. and Saxton, K. E. (1989). Simultaneous heat and water model of a freezing snow–residue-soil system. I: Theory and development. Trans. Amer. Soc. Agric. Eng., 32, 565–571.CrossRefGoogle Scholar
Fletcher, N. H. (1962). The Physics of Rain Clouds. Cambridge University Press.Google Scholar
Foukal, P. (1994). Study of solar irradiance variations holds key to climate questions. Eos, 75(33), 377–383.CrossRefGoogle Scholar
Fouquart, Y., Bonnel, B., and Ramaswamy, V. (1991). Intercomparing shortwave radiation codes for climate studies. J. Geophys. Res., 96, 8955–8968.CrossRefGoogle Scholar
Fowler, L. D., Randall, D. A., and Rutledge, S. A. (1996). Liquid and ice cloud microphysics in the CSU general circulation model. Part I: Model description and simulated microphysical processes. J. Climate, 9, 489–529.2.0.CO;2>CrossRefGoogle Scholar
Frank, W. M. and Cohen, C. (1987). Simulation of tropical convective systems. Part I: A cumulus parameterization. J. Atmos. Sci., 44, 3787–3799.2.0.CO;2>CrossRefGoogle Scholar
Frank, W. M. and J. Molinari (1993). Convective adjustment. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society, pp. 101–106.CrossRef
Frey, H., Latif, M., and Stockdale, T. (1997). The coupled GCM ECHO-2. Part I: The tropical Pacific. Mon. Wea. Rev., 125, 703–720.2.0.CO;2>CrossRefGoogle Scholar
Friedl, M. A., McIver, D. K., Hodges, J. C. F., et al. (2002). Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ., 83, 287–302.CrossRefGoogle Scholar
Fritsch, J. M. and Chappell, C. F. (1980). Numerical prediction of convectively driven mesoscale pressure systems. Part I: Convective parameterization. J. Atmos. Sci., 37, 1722–1733.2.0.CO;2>CrossRefGoogle Scholar
Fritsch, J. M. and Maddox, R. A. (1981). Convectively driven mesoscale pressure systems aloft. Part I: Observations. J. Appl. Meteor., 20, 9–19.2.0.CO;2>CrossRefGoogle Scholar
Fritsch, J. M. and Carbone, R. E. (2004). Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. Bull. Amer. Meteor. Soc., 85, 955–965.CrossRefGoogle Scholar
Fritsch, J. M., Chappell, C. F., and Hoxit, L. R. (1976). The use of large-scale budgets for convective parameterization. Mon. Wea. Rev., 104, 1408–1418.2.0.CO;2>CrossRefGoogle Scholar
Fritsch, J. M., Hilliker, J., Ross, J., and Vislocky, R. L. (2000). Model consensus. Wea. Forecasting, 15, 571–582.2.0.CO;2>CrossRefGoogle Scholar
Fu, Q. (1996). An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 2058–2082.2.0.CO;2>CrossRefGoogle Scholar
Fu, Q. and Liou, K.-N. (1992). On the correlated-von Karman's constant (∼0.4) distribution method for radiative transfer in non-homogeneous atmospheres. J. Atmos. Sci., 49, 2139–2156.2.0.CO;2>CrossRefGoogle Scholar
Fu, Q., Yang, P., and Sun, W. B. (1998). An accurate parameterization of the infrared properties of cirrus clouds for climate models. J. Climate, 11, 2223–2237.2.0.CO;2>CrossRefGoogle Scholar
Gallus, W. A. Jr. (1999). Eta simulations of three extreme precipitation events: sensitivity to resolution and convective parameterization. Wea. Forecasting, 14, 405–426.2.0.CO;2>CrossRefGoogle Scholar
Gallus, W. A. Jr. and Johnson, R. H. (1991). Heat and moisture budgets of an intense midlatitude squall line. J. Atmos. Sci., 48, 122–146.2.0.CO;2>CrossRefGoogle Scholar
Gardiner, M. J. (1982). Use of retrieval and global soils data for global modeling. In Land Surface Processes in Atmospheric General Circulation Models, ed. Eagleson, P. S.. Cambridge University Press.Google Scholar
Garratt, J. R. (1992). The Atmospheric Boundary Layer. Cambridge University Press.Google Scholar
Garratt, J. R. and Hicks, B. B. (1973). Momentum, heat and water vapour transfer to and from natural and artificial surfaces. Quart. J. Roy. Meteor. Soc., 99, 680–687.CrossRefGoogle Scholar
Garratt, J. R. and Hicks, B. B.(1990). Micrometeorological and PBL experiments in Australia. Bound.-Layer Meteor., 50, 11–29.CrossRefGoogle Scholar
Garvert, M. F., Woods, C. P., Colle, B. A., et al. (2005). The 13–14 December 2001 IMPROVE-2 event. Part II: Comparisons of MM5 model simulations of clouds and precipitation with observations. J. Atmos. Sci., 62, 3520–3534.CrossRefGoogle Scholar
Gates, L. W., Boyle, J. S., Covey, C., et al. (1999). An overview of the results of the atmospheric model intercomparison project (AMIP I). Bull. Amer. Meteor. Soc., 80, 229–55.2.0.CO;2>CrossRefGoogle Scholar
Gauthier, P., Charette, C., Fillion, L., Koclas, P., and Laroche, S. (1999). Implementation of a 3D variational data assimilation system at the Canadian Meteorological Centre. Part I: The global analysis. Atmos.-Ocean, 37, 103–156.CrossRefGoogle Scholar
Gedney, N. and Cox, P. M. (2003). The sensitivity of global climate model simulations to the representation of soil moisture heterogeneity. J. Hydrometeor., 4, 1265–1275.2.0.CO;2>CrossRefGoogle Scholar
Geleyn, J. P. and Hollingsworth, A. (1979). An economical analytical method for the computation of the interaction between scattering and line absorption of radiation. Contrib. Atmos. Phys., 52, 1–16.Google Scholar
Georgelin, M. and Lott, F. (2001). On the transfer of momentum by trapped lee waves: case of the IOP3 of PYREX. J. Atmos. Sci., 58, 3563–3580.2.0.CO;2>CrossRefGoogle Scholar
GEWEX Cloud System Science Team (1993). The GEWEX cloud system study (GCSS). Bull. Amer. Meteor. Soc., 74, 387–399.2.0.CO;2>CrossRef
Gill, A. E. (1982). Atmosphere–Ocean Dynamics. Academic Press.Google Scholar
Gilmore, M. S., Straka, J. M., and Rasmussen, E. N. (2004a). Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610–2627.CrossRefGoogle Scholar
Gilmore, M. S., Straka, J. M., and Rasmussen, E. N.(2004b). Precipitation and evolution sensitivity in simulated deep convective storms: comparisons between liquid-only and simple ice and liquid phase microphysics. Mon. Wea. Rev., 132, 1897–1916.2.0.CO;2>CrossRefGoogle Scholar
Giorgi, F., Marinucci, M. R., Bates, G. T., and Canio, G. (1993). Development of a second-generation regional climate model (RegCM2). Part II: Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 2814–2832.2.0.CO;2>CrossRefGoogle Scholar
Glahn, H. R. and Lowry, D. A. (1972). The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 1203–1211.2.0.CO;2>CrossRefGoogle Scholar
Godfrey, C. M., D. J. Stensrud, and L. M. Leslie (2005). The influence of improved land surface and soil data to mesoscale model predictions. Preprints. In 19th Conf. on Hydrology, San Diego, CA, Paper 4.7. American Meteorological Society.
Godson, W. L. (1954). Spectral models and the properties of transmission functions. In Proc. Toronto Meteor. Conf., 1953. Royal Meteorological Society, pp. 35–42.Google Scholar
Goldman, A. and Kyle, T. G. (1968). A comparison between statistical model and line calculation with application to the 9.6 μm ozone and the 2.7 μm water vapor bands. Appl. Opt., 7, 1167–1177.CrossRefGoogle Scholar
Goody, R. M. (1964). Atmospheric Radiation I: Theoretical Basis. Clarendon Press.Google Scholar
Goody, R. M. and Yung, Y. L. (1995). Atmospheric Radiation: Theoretical Basis. Oxford University Press.Google Scholar
Grabowski, W. W. (2001). Coupling cloud processes with the large-scale dynamics using cloud-resolving convection parameterization (CRCP). J. Atmos. Sci., 58, 978–997.2.0.CO;2>CrossRefGoogle Scholar
Grabowski, W. W. and Smolarkiewicz, P. K. (1999). CRCP: a cloud resolving convection parameterization for modeling the tropical convective atmosphere. Physica D, 133, 171–178.CrossRefGoogle Scholar
Grabowski, W. W., Bechtold, P., Cheng, A., et al. (2006). Daytime convective development over land: a model intercomparison based on LBA observations. Quart. J. Roy. Meteor. Soc., 132, 317–344.CrossRefGoogle Scholar
Gregory, D. and Rowntree, P. R. (1990). A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 1483–1506.2.0.CO;2>CrossRefGoogle Scholar
Gregory, D., Kershaw, R., and Inness, P. M. (1997). Parametrization of momentum transport by convection. II: Tests in single-column and general circulation models. Quart. J. Roy. Meteor. Soc., 123, 1153–1183.CrossRefGoogle Scholar
Gregory, D., Morcrette, J.-J., Jakob, C., Beljaars, A. C. M., and Stockdale, T. (2000). Revision of convection, radiation, and cloud schemes in the ECMWF Integrated Forecasting System. Quart. J. Roy. Meteor. Soc., 126, 1685–1710.CrossRefGoogle Scholar
Grell, G. and Devenyi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, doi: 10.1029/2002GL015311.CrossRefGoogle Scholar
Grell, G., Kuo, Y.-H., and Pasch, R. J. (1991). Semiprognostic tests of three cumulus parameterization schemes in middle latitudes. Mon. Wea. Rev., 119, 5–31.2.0.CO;2>CrossRefGoogle Scholar
Grubisic, V. and Moncrieff, M. W. (2000). Parameterization of convective momentum transport in highly baroclinic conditions. J. Atmos. Sci., 57, 3035–3049.2.0.CO;2>CrossRefGoogle Scholar
Gu, Y. and Liou, K. N. (2000). Interactions of radiation, microphysics, and turbulence in the evolution of cirrus clouds. J. Atmos. Sci., 57, 2463–2479.2.0.CO;2>CrossRefGoogle Scholar
Guichard, F., Parsons, D. B., Dudhia, J., and Bresch, J. (2003). Evaluating mesoscale model predictions of clouds and radiation with SGP ARM data over a seasonal timescale. Mon. Wea. Rev., 131, 926–944.2.0.CO;2>CrossRefGoogle Scholar
Gunn, R. and Kinzer, G. D. (1949). The terminal velocity of fall for water drops in stagnant air. J. Meteor., 6, 243–248.2.0.CO;2>CrossRefGoogle Scholar
Gutman, G. and Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens., 19, 1533–1543.CrossRefGoogle Scholar
Hagedorn, R., Doblas-Reyes, F. J., and Palmer, T. N. (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept. Tellus, 57A, 219–233.Google Scholar
Hallett, J. and Mossop, S. C. (1974). Production of secondary ice particles during the riming process. Nature, 249, 26–28.CrossRefGoogle Scholar
Halthore, R. N., Nemesure, S., Schwartz, S. E., et al. (1998). Models overestimate diffuse clear-sky irradiance: a case for excess atmospheric absorption. Geophys. Res. Lett., 25, 3591–3594.CrossRefGoogle Scholar
Haltiner, G. J. and R. T.Williams, (1980). Numerical Prediction and Dynamic Meteorology. John Wiley and Sons.Google Scholar
Hamill, T. M., Whitaker, J. S., and Wei, X. (2004). Ensemble reforecasting: improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 1434–1447.2.0.CO;2>CrossRefGoogle Scholar
Hamill, T. M., Whitaker, J. S., and Mullen, S. L. (2006). Reforecasts: an important dataset for improving weather predictions. Bull. Amer. Meteor. Soc., 86, 33–46.CrossRefGoogle Scholar
Han, J. and Pan, H.-L. (2006). Sensitivity of hurricane intensity to convective momentum transport parameterization. Mon. Wea. Rev., 134, 664–674.CrossRefGoogle Scholar
Hanks, R. J. and Ashcroft, G. L. (1986). Applied Soil Physics. Springer-Verlag.Google Scholar
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., and Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21, 1331–1364.CrossRefGoogle Scholar
Harrison, M. S. J., Palmer, T. N., Richardson, D. S., and Buizza, R. (1999). Analysis and model dependencies in medium-range ensembles: two transplant case studies. Quart. J. Roy. Meteor. Soc., 125, 2487–2515.CrossRefGoogle Scholar
Harshvardhan, and Weinman, J. (1982). Infrared radiative transfer through a regular array of cuboidal clouds. J. Atmos. Sci., 39, 431–439.2.0.CO;2>CrossRefGoogle Scholar
Harshvardhan, , Davies, R., Randall, D. A., and Corsetti, T. G. (1987). A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92, 1009–1015.CrossRefGoogle Scholar
Hart, K. A., Steenburgh, W. J., Onton, D. J., and Siffert, A. J. (2004). An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 Olympic and Paralympic winter games. Wea. Forecasting, 19, 200–218.2.0.CO;2>CrossRefGoogle Scholar
Hastenrath, S. L. (1966). The flux of atmospheric water vapor over the Caribbean Sea and the Gulf of Mexico. J. Appl. Meteor., 5, 778–788.2.0.CO;2>CrossRefGoogle Scholar
Heath, D. F., Krueger, A. J., Roder, A. J., and Henderson, B. D. (1975). The solar scatter ultraviolet and total ozone mapping spectrometer (SBUV/TOMS) for Nimbus G. Opt. Eng., 14, 323–331.CrossRefGoogle Scholar
Heidt, F. D. (1977). The growth of the mixed layer in a stratified fluid due to penetrative convection. Bound.-Layer Meteor., 12, 439–461.CrossRefGoogle Scholar
Henderson-Sellers, A., Yang, Z.-L., and Dickinson, R. E. (1993). The project for intercomparison of land–surface parameterization schemes. Bull. Amer. Meteor. Soc., 74, 1335–1350.2.0.CO;2>CrossRefGoogle Scholar
Henderson-Sellers, A., McGuffie, K., and Gross, C. (1995). Sensitivity of global climate model simulations to increased stomatal resistance and CO2 increases. J. Climate, 8, 1738–1756.2.0.CO;2>CrossRefGoogle Scholar
Henry, W. K. and Thompson, A. H. (1976). An example of polar air modification over the Gulf of Mexico. Mon. Wea. Rev., 104, 1324–1327.2.0.CO;2>CrossRefGoogle Scholar
Higgins, P. A. T. and Vellinga, M. (2004). Ecosystem responses to abrupt climate change: teleconnections, scale and the hydrological cycle. Climatic Change, 64, 127–142.CrossRefGoogle Scholar
Hinkelman, L. M., Ackerman, T. P., and Marchand, R. T. (1999). An evaluation of NCEP Eta model predictions of surface energy budget and cloud properties by comparison with measured ARM data. J. Geophys. Res., 104, 19 535–19 549.CrossRefGoogle Scholar
Hobbs, P. V. (1987). The Gulf Stream rainband. Geophys. Res. Lett., 14, 1142–1145.CrossRefGoogle Scholar
Hobbs, P. V. and Rangno, A. L. (1985). Ice particle concentrations in clouds. J. Atmos. Sci., 42, 2523–2549.2.0.CO;2>CrossRefGoogle Scholar
Hocking, L. M. (1959). The collision efficiency of small drops. Quart. J. Roy. Meteor. Soc., 85, 44–50.CrossRefGoogle Scholar
Hocking, L. M. and Jonas, P. R. (1970). The collision efficiency of small drops. Quart. J. Roy. Meteor. Soc., 96, 722–729.CrossRefGoogle Scholar
Hodur, R. M. (1997). The Naval Research Laboratory's coupled ocean/atmosphere mesoscale prediction system (COAMPS). Mon. Wea. Rev., 125, 1414–1430.2.0.CO;2>CrossRefGoogle Scholar
Holt, T. R. and Raman, S. (1992). Three-dimensional mean and turbulence structure of a coastal front influenced by the Gulf Stream. Mon. Wea. Rev., 120, 17–39.2.0.CO;2>CrossRefGoogle Scholar
Holtslag, A. A. and A. C. M. Beljaars (1989). Surface flux parameterization schemes, developments and experiences at KNMI. In Parameterizations of Fluxes over Land Surface, ECMWF Workshop Proceedings, October 1988, Reading, UK, pp. 121–147.
Holtslag, A. A. and Moeng, C.-H. (1991). Eddy diffusivity and countergradient transport in the convective atmospheric boundary layer. J. Atmos. Sci., 48, 1690–1698.2.0.CO;2>CrossRefGoogle Scholar
Holtslag, A. A. and Boville, B. A. (1993). Local versus nonlocal boundary layer diffusion in a global climate model. J. Climate, 6, 1825–1842.2.0.CO;2>CrossRefGoogle Scholar
Holtslag, A. A., Bruijn, I. F., and Pan, H.-L. (1990). A high resolution air mass transformation model for short-range weather forecasting. Mon. Wea. Rev., 118, 1561–1575.2.0.CO;2>CrossRefGoogle Scholar
Homar, V., Romero, R., Stensrud, D. J., Ramis, C., and Alonso, S. (2003). Numerical diagnosis of a small, quasi-tropical cyclone over the western Mediterranean: dynamical vs. boundary factors. Quart. J. Royal Meteor. Soc., 129, 1469–1490.CrossRefGoogle Scholar
Homar, V., Stensrud, D. J., Levit, J. J., and Bright, D. R. (2006). Value of human-generated pertubations in short-range ensemble forecasts of severe weather. Wea. Forecasting, 21, 347–363.CrossRefGoogle Scholar
Homleid, M. (1995). Diurnal corrections of short-term temperature forecasts using the Kalman filter. Wea. Forecasting, 10, 689–707.2.0.CO;2>CrossRefGoogle Scholar
Hong, S.-Y. and Pan, H.-L. (1996). Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339.2.0.CO;2>CrossRefGoogle Scholar
Hong, X., Chang, S. W., Raman, S., Shay, L. K., and Hodur, R. (2000). The interaction between Hurricane Opal (1995) and a warm core ring in the Gulf of Mexico. Mon. Wea. Rev., 128, 1347–1365.2.0.CO;2>CrossRefGoogle Scholar
Hopwood, W. P. (1995). Surface transfer of heat and momentum over an inhomogeneous vegetated land. Quart. J. Roy. Meteor. Soc., 121, 1549–1574.CrossRefGoogle Scholar
Hou, D., Kalnay, E., and Drogemeier, K. (2001). Objective verification of the SAMEX98 ensemble forecasts. Mon. Wea. Rev., 129, 73–91.2.0.CO;2>CrossRefGoogle Scholar
Houser, P. R. (2003). Infiltration and soil moisture processes, ch. 27. In Handbook of Weather, Climate, and Water: Atmospheric Chemistry, Hydrology, and Societal Impacts, ed. Potter, T. D. and Colman, B. R.. John Wiley and Sons, Inc., pp. 493–506.CrossRefGoogle Scholar
Houtekamer, P. L. (1995). The construction of optimal perturbations. Mon. Wea. Rev., 123, 2888–2898.2.0.CO;2>CrossRefGoogle Scholar
Houtekamer, P. L. and Lefaivre, L. (1997). Using ensemble forecasts for model verification. Mon. Wea. Rev., 125, 2416–2426.2.0.CO;2>CrossRefGoogle Scholar
Houtekamer, P. L. and Mitchell, H. L. (2001). A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123–137.2.0.CO;2>CrossRefGoogle Scholar
Houtekamer, P. L., Lefaivre, L., Derome, J., Ritchie, H., and Mitchell, H. L. (1996). A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225–1242.2.0.CO;2>CrossRefGoogle Scholar
Houze, R. A. Jr. (1982). Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396–410.CrossRefGoogle Scholar
Houze, R. A. Jr.(1993). Cloud Dynamics. Academic Press.Google Scholar
Houze, R. A. Jr.(1997). Stratiform precipitation in regions of convection: a meteorological paradox?Bull. Amer. Meteor. Soc., 78, 2179–2196.2.0.CO;2>CrossRefGoogle Scholar
Hoxit, L. R. (1975). Diurnal variations in planetary boundary-layer winds over land. Bound.-Layer Meteor., 8, 21–38.CrossRefGoogle Scholar
Hunke, E. C. and Dukowicz, J. K. (1997). An elastic-viscous-plastic model for sea ice dynamics. J. Phys. Oceanogr., 27, 1849–1867.2.0.CO;2>CrossRefGoogle Scholar
Huete, A. R., Liu, H. Q., Batchily, K., and Leeuwen, W. (1996). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ., 59, 440–451.CrossRefGoogle Scholar
Iacobellis, S. F. and Somerville, R. C. J. (2000). Implications for microphysics for cloud–radiation parameterizations: lessons from TOGA COARE. J. Atmos. Sci., 57, 161–183.2.0.CO;2>CrossRefGoogle Scholar
Ineson, S. and Davey, M. K. (1997). Interannual climate simulation and predictability in a coupled TOGA GCM. Mon. Wea. Rev., 125, 721–741.2.0.CO;2>CrossRefGoogle Scholar
Iwasaki, T., Yamada, S., and Tada, K. (1989). A parameterization scheme of orographic gravity-wave drag with two different vertical partitionings. Part I: Impacts on medium-range forecasts. J. Meteor. Soc. Japan, 67, 11–27.CrossRefGoogle Scholar
Jacks, E., Bower, J. B., Dagostaro, V. J., et al. (1990). New NGM-based MOS guidance for maximum/minimum temperature, probability of precipitation, cloud amount, and surface wind. Wea. Forecasting, 5, 128–138.2.0.CO;2>CrossRefGoogle Scholar
Jacquemin, B. and Noilhan, J. (1990). Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound.-Layer Meteor., 52, 93–134.CrossRefGoogle Scholar
Jang, K.-I., Zou, X., Pondeca, M. S. F. V., et al. (2003). Incorporating TOMS ozone measurements into the prediction of the Washington, D.C., winter storm during 24–25 January 2000. J. Appl. Meteor., 42, 797–812.2.0.CO;2>CrossRefGoogle Scholar
Janjic, Z. I. (1994). The step-mountain Eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.2.0.CO;2>CrossRefGoogle Scholar
Jarvis, P. G. (1976). The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. Roy. Soc. LondonB, 273, 593–610.CrossRefGoogle Scholar
Johansen, O. (1975). Thermal conductivity of soils. Ph.D. Thesis, University of Trondheim. (Available from Universitestsbiblioteket I Trondheim, Hogskoleringen 1, 7034 Trondheim, Norway.)
Johns, R. H. and Doswell, C. A. III (1992). Severe local storms forecasting. Wea. Forecasting, 7, 588–612.2.0.CO;2>CrossRefGoogle Scholar
Johnson, D. E., Wang, P. K., and Straka, J. M. (1993). Numerical simulations of the 2 August 1981 CCOPE supercell storm with and without ice microphysics. J. Appl. Meteor., 32, 745–759.2.0.CO;2>CrossRefGoogle Scholar
Johnson, R. H. (1984). Partitioning tropical heat and moisture budgets into cumulus and mesoscale components: implications for cumulus parameterization. Mon. Wea. Rev., 112, 1590–1601.2.0.CO;2>CrossRefGoogle Scholar
Johnson, R. H. and Young, G. S. (1983). Heat and moisture budgets of tropical mesoscale anvil clouds. J. Atmos. Sci., 40, 2138–2147.2.0.CO;2>CrossRefGoogle Scholar
Johnson, R. H., Rickenbach, T. M., Rutledge, S. A., Ciesielski, P. E., and Schubert, W. H. (1999). Trimodal characteristics of tropical convection. J. Climate, 12, 2397–2418.2.0.CO;2>CrossRefGoogle Scholar
Jordon, C. L. (1964). On the influence of tropical cyclones on the sea surface temperature field. In Proc. Symp. Tropical Meteorology, Rotorua, New Zealand, November 1963. New Zealand Meteorological Service, pp. 614–622.
Joseph, J. H., Wiscombe, W. J., and Weinman, J. A. (1976). The delta-Eddington approximation for radiative flux transfer. J. Atmos. Sci., 33, 2452–2459.2.0.CO;2>CrossRefGoogle Scholar
Juang, H.-M. H., Lee, C.-T., Zhang, Y., et al. (2005). Applying horizontal diffusion on pressure surface to mesoscale models on terrain-following coordinates. Mon. Wea. Rev., 133, 1384–1402.CrossRefGoogle Scholar
Kaimal, J. C. and Wyngaard, J. C. (1990). The Kansas and Minnesota experiments. Bound.-Layer Meteor., 50, 31–47.CrossRefGoogle Scholar
Kaimal, J. C. and Finnigan, J. J. (1994). Atmospheric Boundary Layer Flows. Oxford University Press.Google Scholar
Kain, J. S. (2004). The Kain–Fritsch convective parameterization: an update. J. Appl. Meteor., 43, 170–181.2.0.CO;2>CrossRefGoogle Scholar
Kain, J. S. and Fritsch, J. M. (1990). A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802.2.0.CO;2>CrossRefGoogle Scholar
Kain, J. S. and Fritsch, J. M.(1992). The role of the convective “trigger function” in numerical forecasts of mesoscale convective systems. Meteor. Atmos. Phys., 49, 93–106.CrossRefGoogle Scholar
Kain, J. S. and J. M. Fritsch(1993). Convective parameterization for mesoscale models: the Kain–Fritsch scheme. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society, pp. 165–170.CrossRef
Kain, J. S., Baldwin, M. E., and Weiss, S. J. (2003). Parameterized updraft mass flux as a predictor of convective intensity. Wea. Forecasting, 18, 106–116.2.0.CO;2>CrossRefGoogle Scholar
Kalnay, E. (2003). Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press.Google Scholar
Kanamitsu, M., Kumar, A., Juang, H. -M. H., et al. (2002). NCEP dynamical seasonal forecast system 2000. Bull. Amer. Meteor. Soc., 83, 1019–1037.2.3.CO;2>CrossRefGoogle Scholar
Katz, R. W. and Murphy, A. H. (1997). Economic Value of Weather and Climate Forecasts. Cambridge University Press.CrossRefGoogle Scholar
Kaufman, Y. J. and Nakajima, T. (1993). Effect of Amazon smoke on cloud microphysics and albedo – analysis from satellite imagery. J. Appl. Meteor., 32, 729–744.2.0.CO;2>CrossRefGoogle Scholar
Kershaw, R. (1995). Parametrization of momentum transport by convectively generated gravity waves. Quart. J. Roy. Meteor. Soc., 121, 1023–1040.CrossRefGoogle Scholar
Kershaw, R. and Gregory, D. (1997). Parametrization of momentum transport by convection. I: Theory and cloud modeling results. Quart. J. Roy. Meteor. Soc., 123, 1133–1151.CrossRefGoogle Scholar
Kessler, E. (1969). On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Meteorology Monographs, No. 32. American Meteorological Society.
Keyser, D. (1986). Atmospheric fronts: an observational perspective. In Mesoscale Meteorology and Forecasting, ed. Ray, P. S.. American Meteorological Society, pp. 216–258.CrossRef
Keyser, D. and Johnson, D. R. (1984). Effects of diabatic heating on the ageostrophic circulation of an upper tropospheric jet streak. Mon. Wea. Rev., 112, 1709–1724.2.0.CO;2>CrossRefGoogle Scholar
Kiehl, J. T. and Gent, P. R. (2004). The Community Climate System Model, Version Two. J. Clim., 17, 3666–3682.2.0.CO;2>CrossRefGoogle Scholar
Kiehl, J. T., J. J. Hack, G. B. Bonan, et al. (1996). Description of the NCAR community climate model. NCAR Tech. Note NCAR/TN-420 + STR.
Kim, J. and Verma, S. (1991). Modeling canopy photosynthesis: scaling up from a leaf to canopy in a temperate grassland ecosystem. Agric. For. Meteor., 57, 187–208.CrossRefGoogle Scholar
Kim, Y.-J. and Arakawa, A. (1995). Improvement of orographic gravity wave parameterization using a mesoscale gravity wave model. J. Atmos. Sci., 52, 1875–1902.2.0.CO;2>CrossRefGoogle Scholar
Kim, Y. -J. and Doyle, J. D. (2005). Extension of an orographic-drag parametrization scheme to incorporate orographic anisotropy and flow blocking. Quart. J. Roy. Meteor. Soc., 131, 1893–1921.CrossRefGoogle Scholar
Kim, Y. -J., Eckermann, S. D., and Chun, H.-Y. (2003). An overview of the past, present and future of gravity-wave drag parametrization for numerical climate and weather prediction models. Atmos.-Ocean, 41, 65–98.Google Scholar
King, M. D., Kaufman, Y. J., Tanre, D., and Nakajima, T. (1999). Remote sensing of tropospheric aerosols from space: past, present, and future. Bull. Amer. Meteor. Soc., 80, 2229–2259.2.0.CO;2>CrossRefGoogle Scholar
Kinzer, G. D. and Gunn, R. (1951). The evaporation, temperature and thermal relaxation-time of freely falling waterdrops. J. Atmos. Sci., 8, 71–83.Google Scholar
Klazura, G. E. (1971). Measurements of precipitation particles in warm cumuli over southeast Texas. J. Appl. Meteor., 10, 739–750.2.0.CO;2>CrossRefGoogle Scholar
Knight, C. A. and Knight, N. C. (2005). Very large hailstones from Aurora, Nebraska. Bull. Amer. Meteor. Soc., 86, 1773–1781.CrossRefGoogle Scholar
Knight, C. A., Cooper, W. A., Breed, D. W., et al. (1982). Microphysics. In Hailstorms of the Central High Plains, vol. 1, ed. Knight, C. and Squires, P.. Colorado Associated University Press, pp. 151–193.
Koch, S. E. (1984). The role of an apparent mesoscale frontogenetic circulation in squall line initiation. Mon. Wea. Rev., 112, 2090–2111.2.0.CO;2>CrossRefGoogle Scholar
Koenig, L. R. (1971). Numerical modeling of ice deposition. J. Atmos. Sci., 28, 226–237.2.0.CO;2>CrossRefGoogle Scholar
Kogan, Y. L. (1991). The simulation of a convective cloud in a 3D model with explicit microphysics. Part I: Model description and sensitivity experiments. J. Atmos. Sci., 48, 1160–1189.2.0.CO;2>CrossRefGoogle Scholar
Koren, V., Schaake, J., Mitchell, K., et al. (1999). A parameterization for snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res., 104, 19 569–19 585.CrossRefGoogle Scholar
Koster, R. D. and M. J. Suarez (1996). Energy and water balance calculations in the Mosaic LSM. NASA Tech. Memo., 104606, 9.
Koster, R. D. and Suarez, M. J.(2001). Soil moisture memory in climate models. J. Hydrometeor., 2, 558–570.2.0.CO;2>CrossRefGoogle Scholar
Kraus, E. B. and Businger, J. A. (1994). Atmosphere–Ocean Interaction. Oxford University Press.Google Scholar
Krinner, G., Viovy, N., Noblet-Ducoudré, N., et al. (2005). A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system. Global Biogeochem. Cycles, 19, GB1015, doi: 10.1029/2003GB002199.CrossRefGoogle Scholar
Krishnamurti, T. N., Kanamitsu, M., Godbole, R., et al. (1976). Study of a monsoon depression (ii), dynamic structure. J. Meteor. Soc., Japan, 54, 208–225.CrossRefGoogle Scholar
Krishnamurti, T. N., Kishtawal, C. M., Shin, D. W., and Williford, C. Eric (2000). Improving tropical precipitation forecasts from a multianalysis superensemble. J. Climate, 13, 4217–4227.2.0.CO;2>CrossRefGoogle Scholar
Kubota, A. and Sugita, M. (1994). Radiometrically determined skin temperature and scalar roughness to estimate surface heat flux. Part I: Parameterization of radiometric scalar roughness. Bound.-Layer Meteor., 69, 397–416.CrossRefGoogle Scholar
Kuettner, J. P. (1959). Cloud bands in the Earth's atmosphere: observations and theory. Tellus, 11, 267–294.CrossRefGoogle Scholar
Kuo, H.-L. (1965). On the formation and intensification of tropical cyclones through latent heat release by cumulus convection. J. Atmos. Sci., 22, 40–63.2.0.CO;2>CrossRefGoogle Scholar
Kurkowski, N. P., Stensrud, D. J., and Baldwin, M. E. (2003). Assessment of implementing satellite-derived land cover data in the Eta model. Wea. Forecasting, 18, 404–416.2.0.CO;2>CrossRefGoogle Scholar
Kutchment, L. S., Demidov, V. N., and Motovilov, Y. G. (1983). River Runoff Generation. Russian Academy of Science, Moscow (in Russian).Google Scholar
Lacis, A. A. and Hansen, J. E. (1974). A parameterization for the absorption of solar radiation in the Earth's atmosphere. J. Atmos. Sci., 31, 118–133.2.0.CO;2>CrossRefGoogle Scholar
Lacis, A. A. and Oinas, V. (1991). A description of the correlated von Karman's constant (∼0.4) distribution method for modeling nongray gaseous absorption, thermal emission, and multiple scattering in vertically inhomogeneous atmospheres. J. Geophys. Res., 96, 9027–9063.CrossRefGoogle Scholar
Laing, A. G. and Fritsch, J. M. (1997). The global population of mesoscale convective complexes. Quart. J. Roy. Meteor. Soc., 123, 389–406.CrossRefGoogle Scholar
Lakhtakia, M. N. and Warner, T. T. (1987). A real-data numerical study of the development of precipitation along the edge of an elevated mixed layer. Mon. Wea. Rev., 115, 156–168.2.0.CO;2>CrossRefGoogle Scholar
Lamb, D. (2001). Rain production in convective storms. In Severe Convective Storms, ed. C. A. Doswell III. Meteorology Monographs, No. 50. American Meteorological Society, pp. 299–321.CrossRef
Lamb, P. J. (1978a). Case studies of tropical Atlantic surface circulation patterns during recent sub-Saharan weather anomalies: 1967 and 1968. Mon. Wea. Rev., 106, 481–491.2.0.CO;2>CrossRefGoogle Scholar
Lamb, P. J.(1978b). Large-scale tropical Atlantic surface circulation patterns associated with Subsaharan weather anomalies. Tellus, 30A, 198–212.Google Scholar
Lamb, P. J. and Peppler, R. A. (1992). Further case studies of tropical Atlantic surface atmospheric and oceanic patterns associated with sub-Saharan drought. J. Climate, 5, 476–488.2.0.CO;2>CrossRefGoogle Scholar
Langmuir, I. (1948). The production of rain by chain-reaction in cumulus clouds at temperatures above freezing. J. Meteor., 5, 175–192.2.0.CO;2>CrossRefGoogle Scholar
Lanicci, J. M. and Warner, T. T. (1991). A synoptic climatology of elevated mixed-layer inversion over the southern Great Plains in spring. Part I: Structure, dynamics, and seasonal evolution. Wea. Forecasting, 6, 181–197.2.0.CO;2>CrossRefGoogle Scholar
Lanicci, J. M., Carlson, T. N., and Warner, T. T. (1987). Sensitivity of the Great Plains severe-storm environment to soil-moisture distribution. Mon. Wea. Rev., 115, 2660–2673.2.0.CO;2>CrossRefGoogle Scholar
Launder, B. E., Reece, G. J., and Rodi, W. (1975). Progress in the development of a Reynolds-stress turbulence closure. J. Fluid Mech., 68, 537–566.CrossRefGoogle Scholar
Lee, B. D. and Wilhelmson, R. B. (1997). The numerical simulation of nonsupercell tornadogenesis. Part II: Evolution of a family of tornadoes along a weak outflow boundary. J. Atmos. Sci., 54, 2387–2415.2.0.CO;2>CrossRefGoogle Scholar
Lee, T. J. and Pielke, R. A. (1992). Estimating the soil surface specific humidity. J. Appl. Meteor., 31, 480–484.2.0.CO;2>CrossRefGoogle Scholar
Leftwich Jr, P. W., J. T. Schaefer, S. J. Weiss, and M. Kay (1998). Severe convective storm probabilities for local areas in watches issued by the storm prediction center. Preprints. In 19th Conf. Severe Local Storms, Minneapolis, MN. American Meteorological Society, pp. 548–551.
Leipper, D. (1967). Observed ocean conditions and Hurricane Hilda, 1964. J. Atmos. Sci., 24, 182–196.2.0.CO;2>CrossRefGoogle Scholar
Leith, C. E. (1974). Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409–418.2.0.CO;2>CrossRefGoogle Scholar
Leslie, L. M. and Speer, M. S. (2000). Comments on “Using ensembles for short-range forecasting.” Mon. Wea. Rev., 128, 3018–3020.2.0.CO;2>CrossRefGoogle Scholar
Treut, H. and Li, Z.-X. (1991). Sensitivity of an atmospheric general circulation model to prescribed SST changes: feedback effects associated with the simulation of cloud optical properties. Climate Dyn., 5, 175–187.CrossRefGoogle Scholar
Leuning, R., Kelliher, F. M., Pury, D. G. G., and Schulze, E.-D. (1995). Leaf nitrogen, photosynthesis, conductance and transpiration: scaling from leaves to canopy. Plant Cell Environ., 18, 1183–1200.CrossRefGoogle Scholar
Levitt, J. (1974). Introduction to Plant Physiology. C. V. Mosby Co.Google Scholar
Lewis, J. M. (2005). Roots of ensemble forecasting. Mon. Wea. Rev., 133, 1865–1885.CrossRefGoogle Scholar
Liang, X., Wood, E. F., and Lettenmaier, D. P. (1996). Surface soil parameterization of the VIC-2L model: evaluation and modification. Global Planet. Change, 13, 195–206.CrossRefGoogle Scholar
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J. (1994). A simple hydrologically based model of land surface water and energy fluxes for GCM. J. Geophys. Res., 99, 14 415–14 428.CrossRefGoogle Scholar
Lilly, D. K. (1968). Models of cloud-topped mixed layers under a strong inversion. Quart. J. Roy. Meteor. Soc., 94, 292–309.CrossRefGoogle Scholar
Lilly, D. K.(1972). Wave momentum flux – a GARP problem. Bull. Amer. Meteor. Soc., 53, 17–23.CrossRefGoogle Scholar
Lilly, D. K.(1988). Cirrus outflow dynamics. J. Atmos. Sci., 45, 1594–1605.2.0.CO;2>CrossRefGoogle Scholar
Lilly, D. K. and Kennedy, P. J. (1973). Observations of a stationary mountain wave and its associated momentum flux and energy dissipation. J. Atmos. Sci., 30, 1135–1152.2.0.CO;2>CrossRefGoogle Scholar
Lin, Y.-L., Farley, R. D., and Orville, H. D. (1983). Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.2.0.CO;2>CrossRefGoogle Scholar
Lindstrom, S. S. and Nordeng, T. E. (1992). Parameterized slantwise convection in a numerical model. Mon. Wea. Rev., 120, 742–756.2.0.CO;2>CrossRefGoogle Scholar
Lindzen, R. S. (1981). Turbulence and stress due to gravity wave and tidal breakdown. J. Geophys. Res., 86, 9707–9714.CrossRefGoogle Scholar
Lindzen, R. S.(1984). Gravity waves in the middle atmosphere. In Dynamics of the Middle Atmosphere, ed. Holton, J. R. and Matsuno, T.. Terra, pp. 3–18.CrossRefGoogle Scholar
Liou, K.-N. (1980). An Introduction to Atmospheric Radiation. Academic Press.Google Scholar
Liou, K.-N.(1986). Influence of cirrus clouds on weather and climate processes: a global perspective. Mon. Wea. Rev., 114, 1167–1199.2.0.CO;2>CrossRefGoogle Scholar
Liou, K.-N. and Wittman, G. D. (1979). Parameterization of radiative properties of clouds. J. Atmos. Sci., 7, 1261–1273.2.0.CO;2>CrossRefGoogle Scholar
Liu, H.-C., Wang, P. K., and Schlesinger, R. E. (2003). A numerical study of cirrus clouds. Part II: Effects of ambient temperature, stability, radiation, ice microphysics, and microdynamics on cirrus evolution. J. Atmos. Sci., 60, 1097–1119.2.0.CO;2>CrossRefGoogle Scholar
Liu, J. Y. and Orville, H. D. (1969). Numerical modeling of precipitation and cloud shadow effects on mountain-induced cumuli. J. Atmos. Sci., 26, 1283–1298.2.0.CO;2>CrossRefGoogle Scholar
Liu, Q., Lewis, J. M., and Schneider, J. M. (1992). A study of cold-air modification over the Gulf of Mexico using in situ data and mixed-layer modeling. J. Appl. Meteor., 31, 909–924.2.0.CO;2>CrossRefGoogle Scholar
Liu, Q. and Schmetz, J. (1988). On the problem of an analytical solution to the diffusivity factor. Beitr. Phys. Atmos., 61, 23–29.Google Scholar
Liu, W. T., Katsaros, K. B., and Businger, J. A. (1979). Bulk parameterization of the air–sea exchange of heat and water vapor including the molecular constraints at the surface. J. Atmos. Sci., 36, 1722–1735.2.0.CO;2>CrossRefGoogle Scholar
Lobocki, L. (1993). A procedure for the derviation of surface-layer bulk relationships from simplified second order closure models. J. Appl. Meteor., 32, 126–138.2.0.CO;2>CrossRefGoogle Scholar
Lohmann, U., McFarlane, N., Levkov, L., Abdella, K., and Albers, F. (1999). Comparing different cloud schemes of a single column model by using mesoscale forcing and nudging technique. J. Climate, 12, 438–461.2.0.CO;2>CrossRefGoogle Scholar
London, J., R. D. Bojkov, S. Oltmans, and J. I. Kelley (1976). Atlas of the global distribution of total ozone, July 1957–June 1967. NCAR Tech. Note 113 + STR.
Lord, S. J., Chao, W. C., and Arakawa, A. (1982). Interactions of a cumulus cloud ensemble with the large-scale environment. Part IV: The discrete model. J. Atmos. Sci., 39, 104–113.2.0.CO;2>CrossRefGoogle Scholar
Lorenc, A. C., Ballard, S. P., Bell, R. S., et al. (2000). The Met. Office global three-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126, 2991–3012.CrossRefGoogle Scholar
Lorenz, E. N. (1963). Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130–141.2.0.CO;2>CrossRefGoogle Scholar
Lorenz, E. N.(1969). The predictability of a flow which possesses many scales of motion. Tellus, 21, 289–307.CrossRefGoogle Scholar
Lott, F. and Miller, M. J. (1997). A new subgrid-scale orographic drag parameterization: its formulation and testing. Quart. J. Roy. Meteor. Soc., 123, 101–127.CrossRefGoogle Scholar
Louis, J. F. (1979). A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor., 17, 187–202.CrossRefGoogle Scholar
Louis, J. F., M. Tiedtke, and J. F. Geleyn (1982). A short history of the operational PBL – parameterization of ECMWF. In Workshop on Planetary Boundary Layer Parameteriztion, European Centre for Medium Range Weather Forecasting, Shinfield Park, Reading.
Loveland, T. R., Merchant, J. W., Brown, J. F., et al. (1995). Seasonal land-cover regions of the United States. Ann. Assoc. Amer. Geographers, 85, 339–355.CrossRefGoogle Scholar
Loveland, T. R., Reed, B. C., Brown, J. F., et al. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens., 21, 1303–1365.CrossRefGoogle Scholar
Lumley, J. L. and Khajeh-Nouri, B. (1974). Computational modeling of turbulent transport. Adv. Geophys., 18A, 169–192.Google Scholar
Luo, H. and Yanai, M. (1984). The large-scale circulation and heat sources over the Tibetan plateau and surrounding areas during the early summer of 1979. Part II: Heat and moisture budgets. Mon. Wea. Rev., 112, 966–989.2.0.CO;2>CrossRefGoogle Scholar
Luo, L., Robock, A., Vinnikov, K. Y., et al. (2003). Effects of frozen soil on soil temperature, spring infiltration, and runoff: results from the PILPS 2(d) experiment at Valdai, Russia. J. Hydrometeor., 4, 334–351.2.0.CO;2>CrossRefGoogle Scholar
Lynn, B. H., Khain, A. P., Dudhia, J., et al. (2005). Spectral (bin) microphysics coupled with a mesoscale model (MM5). Part I: Model description and first results. Mon. Wea. Rev., 133, 44–58.CrossRefGoogle Scholar
Maddox, R. A. (1980). Mesoscale convective complexes. Bull. Amer. Meteor. Soc., 61, 1374–1387.2.0.CO;2>CrossRefGoogle Scholar
Mahfouf, J.-F. (1991). Analysis of soil moisture from near-surface parameters: a feasibility study. J. Appl. Meteor., 30, 1534–1547.2.0.CO;2>CrossRefGoogle Scholar
Mahfouf, J.-F., Richard, E., and Mascart, P. (1987). The influence of soil and vegetation on the development of mesoscale circulations. J. Clim. Appl. Meteor., 26, 1483–1495.2.0.CO;2>CrossRefGoogle Scholar
Mahfouf, J.-F., Manzi, A. O., Noilhan, J., Giordani, H., and DéQué, M. (1995). The land surface scheme ISBA within Météo-France climate model ARPEGE. Part I: Implementation and preliminary results. J. Climate, 8, 2039–2057.2.0.CO;2>CrossRefGoogle Scholar
Mahrt, L. (1976). Mixed layer moisture structure. Mon. Wea. Rev., 104, 1403–1407.2.0.CO;2>CrossRefGoogle Scholar
Mahrt, L.(1987). Grid-averaged surface fluxes. Mon. Wea. Rev., 115, 1550–1560.2.0.CO;2>CrossRefGoogle Scholar
Mahrt, L.(1991). Boundary-layer moisture regimes. Quart J. Roy. Meteor. Soc., 117, 151–176.CrossRefGoogle Scholar
Mahrt, L. and Ek, M. (1984). The influence of atmospheric stability on potential evaporation. J. Climate Appl. Meteor., 23, 222–234.2.0.CO;2>CrossRefGoogle Scholar
Mahrt, L. and Pan, H. L. (1984). A two-layer model of soil hydrology. Bound.-Layer Meteor., 29, 1–20.CrossRefGoogle Scholar
Makin, V. K. and Mastenbroek, C. (1996). Impact of waves on air–sea exchange of sensible heat and momentum. Bound.-Layer Meteor., 79, 279–300.CrossRefGoogle Scholar
Manabe, S. (1969). The atmospheric circulation and hydrology of the Earth's surface. Mon. Wea. Rev., 97, 739–774.2.3.CO;2>CrossRefGoogle Scholar
Manabe, S. and Strickler, R. (1964). Thermal equilibrium of the atmosphere with a convective adjustment. J. Atmos. Sci., 21, 361–385.2.0.CO;2>CrossRefGoogle Scholar
Manabe, S., Smagorinsky, J., and Strickler, R. F. (1965). Simulated climatology of a general circulation model with a hydrologic cycle. Mon. Wea. Rev., 93, 769–798.2.3.CO;2>CrossRefGoogle Scholar
Mao, Q., McNider, R. T., Mueller, S. F., and Juang, H.-M. H. (1999). An optimal model output calibration algorithm suitable for objective temperature forecasting. Wea. Forecasting, 14, 190–202.2.0.CO;2>CrossRefGoogle Scholar
Mapes, B. E. (1997). Equilibrium vs. activation control of large-scale variations of tropical deep convection. In The Physics and Parameterization of Moist Atmospheric Convection, ed. Smith, R. K.. Kluwer Academic Publishers, pp. 321–358.CrossRefGoogle Scholar
Mapes, B. E. and Lin, J. (2005). Doppler radar observations of mesoscale wind divergence in regions of tropical convection. Mon. Wea. Rev., 133, 1808–1824.CrossRefGoogle Scholar
Mapes, B. E., Ciesielski, P. E., and Johnson, R. H. (2003). Sampling errors in rawinsonde-array budgets. J. Atmos. Sci., 60, 2697–2714.2.0.CO;2>CrossRefGoogle Scholar
Marchuk, G., Mikhailov, G., Nazaraliev, M., et al. (1980). The Monte Carlo Methods in Atmospheric Optics. Springer-Verlag.CrossRefGoogle Scholar
Margulis, S. A. and Entekhabi, D. (2004). Boundary-layer entrainment estimation through assimilation of radiosonde and micrometeorological data into a mixed-layer model. Bound.-Layer Meteor., 110, 405–433.CrossRefGoogle Scholar
Markowski, P. M. and Harrington, J. Y. (2005). A simulation of a supercell thunderstorm with emulated radiative cooling beneath the anvil. J. Atmos. Sci., 62, 2607–2617.CrossRefGoogle Scholar
Marletto, V., Zinoni, F., Criscuolo, L., et al. (2005). Evaluation of downscaled DEMETER multi-model ensemble seasonal hindcasts in a northern Italy location by means of a model of wheat growth and soil water balance. Tellus, 57A, 488–497.CrossRefGoogle Scholar
Marshak, A. and Davis, A. B. (2005). Radiative Transfer in Cloudy Atmospheres. Springer-Verlag.CrossRefGoogle Scholar
Marshall, C. H., Crawford, K. C., Mitchell, K. E., and Stensrud, D. J. (2003). The impact of the land surface physics in the operational NCEP Eta model on simulating the diurnal cycle: evaluation and testing using Oklahoma Mesonet data. Wea. Forecasting, 18, 748–768.2.0.CO;2>CrossRefGoogle Scholar
Marshall, J. S. and Palmer, W. M. (1948). The distribution of raindrops with size. J. Meteor., 5, 165–166.2.0.CO;2>CrossRefGoogle Scholar
Marshall, T. J., Holmes, J. W., and Rose, C. W. (1996). Soil Physics. Cambridge University Press.CrossRefGoogle Scholar
Mason, B. J. (1956). On the melting of hailstones. Quart. J. Roy. Meteor. Soc., 82, 209–216.CrossRefGoogle Scholar
Mason, B. J.(1971). The Physics of Clouds, 2nd edn. Oxford University Press.Google Scholar
Mass, C. F. and Kuo, Y.-H. (1998). Regional real-time numerical weather prediction: current status and future potential. Bull. Amer. Meteor. Soc., 79, 253–263.2.0.CO;2>CrossRefGoogle Scholar
Mass, C. F., Albright, M., Overs, D., et al. (2003). Regional environmental prediction over the Pacific Northwest. Bull. Amer. Meteor. Soc., 84, 1353–1366.CrossRefGoogle Scholar
Maykut, G. A. and Untersteiner, N. (1971). Some results from a time dependent thermodynamic model of sea ice. J. Geophys. Res., 76, 1550–1575.CrossRefGoogle Scholar
McCarthy, D. J., J. T. Schaefer, and M. Kay (1998). Watch verification at the storm prediction center 1970–1997. Preprints. In 19th Conf. Severe Local Storms. American Meteorological Society, pp. 603–606.
McClain, E. P., Pichel, W. G., and Walton, C. C. (1985). Comparative performance of AVHRR-based multichallen sea surface temperatures. J. Geophys. Res., 90, 11 587–11 601.CrossRefGoogle Scholar
McCorcle, M. D. (1988). Simulation of surface-moisture effects on the Great Plains low-level jet. Mon. Wea. Rev., 116, 1705–1720.2.0.CO;2>CrossRefGoogle Scholar
McCumber, M. C. and Pielke, R. A. (1981). Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model soil layer. J. Geophys. Res., 86, 9929–9938.CrossRefGoogle Scholar
McCumber, M. C., Tao, W.-K., Simpson, J., Penc, R., and Soong, S.-T. (1991). Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30, 985–1004.CrossRefGoogle Scholar
McDonald, J. E. (1958). The physics of cloud modification. Adv. Geophys., 5, 223–303.CrossRefGoogle Scholar
McFarlane, N. A. (1987). The effect of orographically excited gravity wave drag on the general circulation of the lower stratosphere and troposphere. J. Atmos. Sci., 44, 1775–1800.2.0.CO;2>CrossRefGoogle Scholar
McPherson, R. A. and Stensrud, D. J. (2005). Influences of a winter wheat belt on the evolution of the boundary layer. Mon. Wea. Rev., 133, 2178–2199.CrossRefGoogle Scholar
McPherson, R. A., Stensrud, D. J., and Crawford, K. C. (2004). The impact of Oklahoma's winter wheat belt on the mesoscale environment. Mon. Wea. Rev., 132, 405–421.2.0.CO;2>CrossRefGoogle Scholar
Meador, W. E. and Weaver, W. R. (1980). Two stream approximations to radiative transfer in planetary atmospheres: a unified description of existing methods and a near improvement. J. Atmos. Sci., 37, 630–643.2.0.CO;2>CrossRefGoogle Scholar
Meleshko, V. P. and Wetherald, R. T. (1981). The effect of a geographical cloud distribution on climate: a numerical experiment with an atmospheric general circulation model. J. Geophys. Res., 86, 11 995–12 014.CrossRefGoogle Scholar
Mellor, G. L. and Yamada, T. (1974). A hierarchy of turbulence closure models for planetary boundary layers. J. Atmos. Sci., 31, 1791–1806.2.0.CO;2>CrossRefGoogle Scholar
Mellor, G. L. and Yamada, T.(1982). Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851–875.CrossRefGoogle Scholar
Mesinger, F., Janjic, Z. I., Nickovic, S., Gavrilov, D., and Deaven, D. G. (1988). The step-mountain coordinate: model description and performance for cases of Alpine lee cyclogenesis and for a case of an Appalachian redevelopment. Mon. Wea. Rev., 116, 1493–1520.2.0.CO;2>CrossRefGoogle Scholar
Meyers, M. P., DeMott, P. J., and Cotton, W. R. (1992). New primary ice nucleation parameterizations in an explicit cloud model. J. Appl. Meteor., 31, 708–721.2.0.CO;2>CrossRefGoogle Scholar
Meyers, M. P., Walko, R. L., Harrington, J. Y., and Cotton, W. R. (1997). New RAMS cloud microphysics parameterization. Part II: The two-moment scheme. Atmos. Res., 45, 3–39.CrossRefGoogle Scholar
Miller, D. A. and White, R. A. (1998). A conterminous United States multilayer soil characteristics data set for regional climate and hydrology modeling. Earth Interactions, 2. (Available online at http://EarthInteractions.org.)2.3.CO;2>CrossRefGoogle Scholar
Milly, P. C. D. (1997). Sensitivity of greenhouse summer dryness to changes in plant rooting characteristics. Geophys. Res. Lett., 24, 269–271.CrossRefGoogle Scholar
Mitchell, H. L. and Houtekamer, P. L. (2000). An adaptive ensemble Kalman filter. Mon. Wea. Rev., 128, 416–433.2.0.CO;2>CrossRefGoogle Scholar
Mitchell, K. E., Lohman, D., Houser, P. R., et al. (2004). The multi-institution North American Land Data Assimilation System (NLDAS): utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, doi: 10.1029/2003JD0003823.CrossRefGoogle Scholar
Mlawer, E. J., Brown, P. D., Clough, S. A., et al. (2000). Comparison of spectral direct and diffuse solar irradiance measurements and calculations for cloud-free conditions. Geophys. Res. Lett., 27, 2653–2656.CrossRefGoogle Scholar
Mlawer, E. J., Taubman, S. J., Iacono, P. D., and Clough, S. A. (1997). Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-von Karman's constant (∼0.4) model for the longwave. J. Geophys. Res., 102(D14), 16 663–16 682.CrossRefGoogle Scholar
Molinari, J. (1982). A method for calculating the effects of deep cumulus convection in numerical models. Mon. Wea. Rev., 110, 1527–1534.2.0.CO;2>CrossRefGoogle Scholar
Molinari, J.(1985). A general form of Kuo's cumulus parameterization. Mon. Wea. Rev., 113, 1411–1416.2.0.CO;2>CrossRefGoogle Scholar
Molinari, J.(1993). An overview of cumulus parameterization in mesoscale models. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. Emanuel, K. A. and Raymond, D. J.. Meteorology Monographs, No. 46. American Meteorological Society, pp. 155–158.CrossRefGoogle Scholar
Molinari, J. and Dudek, M. (1992). Parameterization of convective precipitation in mesoscale models: a critical review. Mon. Wea. Rev., 120, 326–344.2.0.CO;2>CrossRefGoogle Scholar
Monteith, J. L. (1961). An empirical method for estimating long-wave radiation exchange in the British Isles. Quart. J. Roy. Meteor. Soc., 87, 171–179.CrossRefGoogle Scholar
Monteith, J. L.(1965). Evaporation and environment. Symp. Soc. Exp. Biol., 19, 205–234.Google Scholar
Monteith, J. L. and M. H. Unsworth (1990). Principles of Environmental Physics, 2nd edn. Edward Arnold.
Molteni, F., Buizza, R., Palmer, T. N., and Petroliagis, T. (1996). The ECMWF ensemble prediction system: methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73–119.CrossRefGoogle Scholar
Moore, K. E., Fitzjarrald, D. R., Sakai, R. K., et al. (1996). Seasonal variations in radiative and turbulent exchange at a deciduous forecast in Central Massachusetts. J. Appl. Meteor., 35, 122–134.2.0.CO;2>CrossRefGoogle Scholar
Morcrette, J.-J. (2002). The surface downward longwave radiation in the ECMWF forecast system. J. Climate, 15, 1875–1892.2.0.CO;2>CrossRefGoogle Scholar
Morcrette, J.-J. and Fouquart, Y. (1985). On systematic errors in the parametrized calculations of longwave radiation transfer. J. Atmos. Sci., 43, 321–328.2.0.CO;2>CrossRefGoogle Scholar
Morse, A. P., Doblas-Reyes, F. J., Hoshen, M. B., Hagedorn, R., and Palmer, T. N. (2005). A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model. Tellus, 57A, 464–475.CrossRefGoogle Scholar
Morss, R. E., Miller, K. A., and Vasil, M. S. (2005). A systematic economic approach to evaluating public investment in observations for weather forecasting. Mon. Wea. Rev., 133, 374–388.CrossRefGoogle Scholar
Mullen, S. L. and Baumhefner, D. P. (1994). Monte Carlo simulations of explosive cyclogenesis. Mon. Wea. Rev., 122, 1548–1567.2.0.CO;2>CrossRefGoogle Scholar
Müller, M. D. and Scherer, D. (2005). A grid- and subgrid-scale radiation parameterization of topographic effects for mesoscale weather forecast models. Mon. Wea. Rev., 133, 1431–1442.CrossRefGoogle Scholar
Munn, R. E. (1966). Descriptive Micrometeorology. Academic Press.Google Scholar
Murikami, M. (1990). Numerical modeling of dynamical and microphysical evolution of an isolated convective cloud – the 19 July 1982 CCOPE cloud. J. Meteor. Soc. Japan, 68, 107–128.CrossRefGoogle Scholar
Murphy, A. H. and Winkler, R. L. (1979). Probabilistic temperature forecasts: the case for an operational program. Bull. Amer. Meteor. Soc., 60, 12–19.2.0.CO;2>CrossRefGoogle Scholar
Musil, D. J. (1970). Computer modeling of hailstone growth in feeder clouds. J. Atmos. Sci., 27, 474–482.2.0.CO;2>CrossRefGoogle Scholar
Nakaya, U. and T. Terada (1935). Simultaneous observations of the mass, falling velocity, and form of individual snow crystals. Hokkaido University, Ser. II, 1, 191–201.
Ninomiya, K. (1971a). Dynamical analysis of outflow from tornado-producing thunderstorms as revealed by ATS III pictures. J. Appl. Meteor., 10, 275–294.2.0.CO;2>CrossRefGoogle Scholar
Ninomiya, K.(1971b). Mesoscale modification of synoptic situations from thunderstorm development as revealed by ATS III and aerological data. J. Appl. Meteor., 10, 1103–1121.2.0.CO;2>CrossRefGoogle Scholar
Niyogi, D. S. and Raman, S. (1997). Comparison of stomatal resistance simulated by four different schemes using FIFE observations. J. Appl. Meteor., 36, 903–917.2.0.CO;2>CrossRefGoogle Scholar
Niyogi, D. S., Raman, S., and Alapaty, K. (1998). Comparison of four different stomatal resistance schemes using FIFE data. Part II: Analysis of terrestrial biospheric–atmospheric interactions. J. Appl. Meteor., 37, 1301–1320.2.0.CO;2>CrossRefGoogle Scholar
Niyogi, D. S., Chang, H., Saxena, V. K.et al. (2004). Direct observations of the effects of aerosol loading on net ecosystem CO2 exchanges over different landscapes. Geophys. Res. Lett., 31, L20506, doi: 10.1029/2004GL020915.CrossRefGoogle Scholar
Noilhan, J. and Planton, S. (1989). A simple parameterization of land-surface processes for meteorological models. Mon. Wea. Rev., 117, 536–549.2.0.CO;2>CrossRefGoogle Scholar
Nordeng, T. E. (1987). The effect of vertical and slantwise convection on the simulation of polar lows. Tellus, 39A, 354–375.CrossRefGoogle Scholar
Nordeng, T. E.(1993). Parameterization of slantwise convection in numerical weather prediction models. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society, pp. 195–202.CrossRef
Nowlin, W. D. Jr. and Parker, C. A. (1974). Effects of a cold-air outbreak on shelf waters of the Gulf of Mexico. J. Phys. Oceanogr., 4, 467–486.2.0.CO;2>CrossRefGoogle Scholar
Oglesby, R. J. and Erickson, D. J. III (1989). Soil moisture and the persistence of North American drought. J. Climate, 2, 1362–1380.2.0.CO;2>CrossRefGoogle Scholar
Olson, D. A., Junker, N. W., and Korty, B. (1995). Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Wea. Forecasting, 10, 498–511.2.0.CO;2>CrossRefGoogle Scholar
Ookouchi, Y., Segal, M., Kessler, M. C., and Pielke, R. A. (1984). Evaluation of soil moisture effects on generation and modification of mesoscale circulations. Mon. Wea. Rev., 112, 2281–2292.2.0.CO;2>CrossRefGoogle Scholar
Oost, W. A., Komen, G. J., Jacobs, C. M. J., and Oort, C. (2002). New evidence for a relation between wind stress and wave age from measurements during ASGAMAGE. Bound.-Layer Meteor., 103, 409–438.CrossRefGoogle Scholar
Otte, T. L., Pouliot, G., Pleim, J. E., et al. (2005). Linking the Eta model with the community multiscale air quality (CMAQ) modeling system to build a national air quality forecasting system. Wea. Forecasting, 20, 367–384.CrossRefGoogle Scholar
Ovtchinnikov, M. and Kogan, Y. L. (2000). An investigation of ice production mechanisms in small cumuliform clouds using a 3D model with explicit microphysics. Part I: Model description. J. Atmos. Sci., 57, 2989–3003.2.0.CO;2>CrossRefGoogle Scholar
Palmer, T. N., Shutts, G. J., and Swinbank, R. (1986). Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parametrization. Quart. J. Roy. Meteor. Soc., 112, 1001–1039.CrossRefGoogle Scholar
Palmer, T. N., Alessandri, A., Andersen, U., et al. (2004). Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull. Amer. Meteor. Soc., 85, 853–872.CrossRefGoogle Scholar
Pan, D.-M. and Randall, D. A. (1998). A cumulus parametrization with a prognostic closure. Quart. J. Roy. Meteor. Soc., 124, 949–981.Google Scholar
Pan, H. L. and Mahrt, L. (1987). Interaction between soil hydrology and boundary-layer development. Bound.-Layer Meteor., 38, 185–202.CrossRefGoogle Scholar
Pan, Y., McGuire, A. D., Melillo, J. M., et al. (2002). A biogeochemistry-based dynamic vegetation model and its application along a moisture gradient in the continental United States. J. Vegetation Sci., 13, 369–382.CrossRefGoogle Scholar
Panofsky, H. A. and Dutton, J. A. (1984). Atmospheric Turbulence: Models and Methods for Engineering Applications. John Wiley and Sons.Google Scholar
Paulson, C. A. (1970). The mathematical representation of wind speed and temperature profiles in an unstable atmospheric surface layer. J. Appl. Meteor., 9, 857–861.2.0.CO;2>CrossRefGoogle Scholar
Peixoto, J. P. and Kettani, M. A. (1973). The control of the water cycle. Sci. Amer., 228, 46–61.CrossRefGoogle Scholar
Peixoto, J. P. and A. H. Oort (1992). Physics of Climate. American Institute of Physics.CrossRef
Peláez, D. V. and Bóo, R. M. (1987). Plant water potential for shrubs in Argentina. J. Range Manag., 40, 6–9.CrossRefGoogle Scholar
Peltier, W. R. and Clark, T. L. (1979). The evolution and stability of finite-amplitude mountain waves. Part II: Surface-wave drag and severe downslope windstorms. J. Atmos. Sci., 36, 1498–1529.2.0.CO;2>CrossRefGoogle Scholar
Penman, H. L. (1948). Natural evaporation from open water, bare soil, and grass. Proc. Roy. Soc. London, A193, 120–195.CrossRefGoogle Scholar
Persson, A. (2005a). Early operational numerical weather prediction outside the USA: an historical introduction. Part I: Internationalism and engineering, NWP in Sweden, 1952–69. Meteorol. Appl., 12, 135–159.CrossRefGoogle Scholar
Persson, A.(2005b). Early operational numerical weather prediction outside the USA: an historical introduction. Part II: Twenty countries around the world. Meteorol. Appl., 12, 269–289.CrossRefGoogle Scholar
Persson, A.(2005c). Early operational numerical weather prediction outside the USA: an historical introduction. Part III: Endurance and mathematics – British NWP, 1948–1965. Meteorol. Appl., 12, 381–413.CrossRefGoogle Scholar
Peters-Lidard, C. D., Blackburn, E., Liang, X., and Wood, E. F. (1998). The effect of soil thermal conductivity parameterization on surface energy fluxes and temperatures. J. Atmos. Sci., 55, 1209–1224.2.0.CO;2>CrossRefGoogle Scholar
Philander, S. G. H. (1989). El Niño, La Niña, and the Southern Oscillation. Academic Press.Google Scholar
Philipona, R. (2002). Underestimation of solar global and diffuse radiation measured at the Earth's surface. J. Geophys. Res., 107(D22), doi: 10.1019/2002JD002396.CrossRefGoogle Scholar
Phillips, S. P. (1984). Analytical surface pressure and drag for linear hydrostatic flow over three-dimensional elliptical mountains. J. Atmos. Sci., 41, 1073–1084.2.0.CO;2>CrossRefGoogle Scholar
Pielke, R. A., Dalu, G. A., Snook, J. S., Lee, T. J., and Kittel, T. G. F. (1991). Nonlinear influence of mesoscale land use on weather and climate. J. Climate, 4, 1053–1069.2.0.CO;2>CrossRefGoogle Scholar
Pierrehumbert, R. T. (1986). An essay on the parameterization of orographic gravity-wave drag. In Proc. Seminar/Workshop on Observation, Theory and Modeling of Orographic Effects, vol. 1, September, Shinfield Park, Reading, ECMWF, pp. 251–282.Google Scholar
Pincus, R. and S. A. Ackermann (2003). Radiation in the atmosphere: foundations. In Handbook of Weather, Climate, and Water. Dynamics, Climate, Physical Meteorology, Weather Systems, and Measurements, ed. Potter, T. D. and Colman, B. R.. Wiley-Interscience, pp. 301–342.CrossRefGoogle Scholar
Pleim, J. E. and Chang, J. S. (1992). A non-local closure model for vertical mixing in the convective boundary layer. Atmos. Environ., 26A, 965–981.CrossRefGoogle Scholar
Powers, J. G. and Stoelinga, M. T. (2000). A coupled air–sea mesoscale model: experiments in atmospheric sensitivity to marine roughness. Mon. Wea. Rev., 128, 208–228.2.0.CO;2>CrossRefGoogle Scholar
Pressman, D. Y. (1994). Numerical model of hydrothermal processes in soil as part of the scheme of mesoscale forecasting. Meteorol. Gidrol, 11, 62–75 (in Russian).Google Scholar
Priestley, C. H. B. (1954). Convection from a large horizontal surface. Australian J. Phys., 6, 279–290.CrossRefGoogle Scholar
Priestley, C. H. B. and Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 81–92.2.3.CO;2>CrossRefGoogle Scholar
Pruppacher, H. R. and Klett, J. D. (2000). Microphysics of Clouds and Precipitation, 2nd edn. Kluwer Academic Publishers.Google Scholar
Pudykiewicz, R., Benoit, R., and Mailhot, J. (1992). Inclusion and verification of a predictive cloud-water scheme in a regional numerical weather prediction model. Mon. Wea. Rev., 120, 612–626.2.0.CO;2>CrossRefGoogle Scholar
Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J. F., and Simmons, A. (2000). The ECMWF operational implementation of four-dimensional variational assimilation. Part I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143–1170.CrossRefGoogle Scholar
Rabin, R. M., Stadler, S., Wetzel, P. J., Stensrud, D. J., and Gregory, M. (1990). Observed effects of landscape variability on convective clouds. Bull. Amer. Meteor. Soc., 71, 272–280.2.0.CO;2>CrossRefGoogle Scholar
Ramanathan, V., Pitcher, E. J., Malone, R. C., and Blackman, M. L. (1983). The response of a spectral general circulation model to refinements in radiative processes. J. Atmos. Sci., 40, 605–630.2.0.CO;2>CrossRefGoogle Scholar
Randall, D. A., Abeles, J. A., and Corsetti, T. G. (1985). Seasonal simulations of the planetary boundary layer and boundary-layer stratocumulus clouds with a general circulation model. J. Atmos. Sci., 42, 641–675.2.0.CO;2>CrossRefGoogle Scholar
Randall, D. A., Khairoutdinov, M., Arakawa, A., and Grabowski, W. (2003). Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 1547–1564.CrossRefGoogle Scholar
Rasmussen, E. (1985). A case study of a polar low development over the Barents Sea. Tellus, 37A, 407–418.CrossRefGoogle Scholar
Rasmussen, E. N. (1967). Atmospheric water vapor transport and the water balance of North America. Part I: Characteristics of the water vapor flux field. Mon. Wea. Rev., 95, 403–426.2.3.CO;2>CrossRefGoogle Scholar
Rasmussen, R., Politovich, M., Marwitz, J., et al. (1992). Winter Icing and Storms Project (WISP). Bull. Amer. Meteor. Soc., 73, 951–974.2.0.CO;2>CrossRefGoogle Scholar
Rauber, R. M. (2003). Microphysical processes in the atmosphere, ch. 18. In Handbook of Weather, Climate, and Water. Dynamics, Climate, Physical Meteorology, Weather Systems, and Measurements, ed. Potter, T. D. and Colman, B. R.. Wiley, pp. 255–299.CrossRefGoogle Scholar
Rawls, W. J., Brakensiek, D. L., and Saxton, K. E. (1982). Estimation of soil water properties. Trans. Amer. Soc. Agric. Eng., 25, 1316–1320.CrossRefGoogle Scholar
Raymond, D. J. (1995). Regulation of moist convection over the west Pacific warm pool. J. Atmos. Sci., 52, 3945–3959.2.0.CO;2>CrossRefGoogle Scholar
Raymond, D. J. and Blyth, A. M. (1986). A stochastic model for nonprecipitating cumulus clouds. J. Atmos. Sci., 43, 2708–2718.2.0.CO;2>CrossRefGoogle Scholar
Raymond, D. J. and K. A. Emanuel (1993). The Kuo cumulus parameterization. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. Emanuel, K. A. and Raymond, D. J.. Meteorology Monographs, No. 46. American Meteorological Society, pp. 145–147.CrossRefGoogle Scholar
Raymond, T. M. and Pandis, S. N. (2002). Cloud activation of single-component organic aerosol particles. J. Geophys. Res., 107, doi: 10.1029/2002JD002159.CrossRefGoogle Scholar
Raymond, W. H. and Stull, R. B. (1990). Application of transilient turbulence theory to mesoscale numerical weather forecasting. Mon. Wea. Rev., 118, 2471–2499.2.0.CO;2>CrossRefGoogle Scholar
Reed, R. J. and Recker, E. E. (1971). Structure and properties of synoptic-scale wave disturbances in the equatorial western Pacific. J. Atmos. Sci., 28 1117–1133.2.0.CO;2>CrossRefGoogle Scholar
Reisner, J., Rasmussen, R. M., and Bruintjes, R. T. (1998). Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Royal Meteor. Soc., 124, 1071–1107.CrossRefGoogle Scholar
Reynolds, C. A., Jackson, T. J., and Rawls, W. J. (2000). Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions. Water Resources Res., 36, 3653–3662.CrossRefGoogle Scholar
Reynolds, D. (2003). Value-added quantitative precipitation forecasts: how valuable is the forecaster?Bull. Amer. Meteor. Soc., 84, 876–878.CrossRefGoogle Scholar
Reynolds, R. W. and Smith, T. M. (1994). Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929–948.2.0.CO;2>CrossRefGoogle Scholar
Richards, L. A. (1931). Capillary conduction of liquids through porous mediums. Physics, 1, 318–333.CrossRefGoogle Scholar
Robock, A., Luo, L., Wood, E. F., et al. (2003). Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season. J. Geophys. Res., 108, doi: 10.1029/2002JD003245.CrossRefGoogle Scholar
Rodell, M., Houser, P. R., Jambor, U., et al. (2004). The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381–394.CrossRefGoogle Scholar
Rodgers, C. D. (1967). The use of emissivity in atmospheric radiation calculations. Quart. J. Roy. Meteor. Soc., 93, 43–54.CrossRefGoogle Scholar
Rodgers, C. D. and Walshaw, C. D. (1966). The computation of infrared cooling rate in planetary atmospheres. Quart. J. Roy. Meteor. Soc., 92, 67–92.CrossRefGoogle Scholar
Roebber, P. J., Schultz, D. M., Colle, B. A., and Stensrud, D. J. (2004). The risks and rewards of high-resolution and ensemble numerical weather prediction. Wea. Forecasting, 19, 936–949.2.0.CO;2>CrossRefGoogle Scholar
Roeger, C., Stull, R., McClung, D., et al. (2003). Verification of mesoscale numerical weather forecasts in mountainous terrain for application to avalanche prediction. Wea. Forecasting, 18, 1140–1160.2.0.CO;2>CrossRefGoogle Scholar
Rogers, R. R. (1976). A Short Course in Cloud Physics, 2nd edn. Pergamon.Google Scholar
Rogers, R. R. and Yau, M. K. (1989). A Short Course in Cloud Physics, 3rd edn. Butterworth-Heinemann.Google Scholar
Ronda, R. J., Hurt, B. J. J. M., and Holtslag, A. A. M. (2002). Spatial heterogeneity of the soil moisture content and its impact on surface flux densities and near-surface meteorology. J. Hydrometeor., 3, 556–570.2.0.CO;2>CrossRefGoogle Scholar
Ross, G. H. (1989). Model output statistics – an updateable scheme. Preprints. In 11th Conf. on Probability and Statistics in Atmospheric Sciences, Monterey, CA. American Meteorological Society, pp. 93–97.
Rotta, J. C. (1951). Statistische theorie nichthomogener turbulenz. Zeitschrift Phys., 129, 547–572.CrossRefGoogle Scholar
Rutledge, S. A. and Hobbs, P. V. (1983). The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40, 1185–1206.2.0.CO;2>CrossRefGoogle Scholar
Rutledge, S. A. and Hobbs, P. V.(1984). The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41, 2949–2972.2.0.CO;2>CrossRefGoogle Scholar
Rutter, A. J., Kershaw, K. A., Robins, P. C., and Morton, A. J. (1971). A predictive model of rainfall interception in forests. I. Derivation of the model from observations in a plantation of Corsican pine. Agric. Meteor., 9, 367–384.CrossRefGoogle Scholar
Rutter, A. J., Morton, A. J., and Robins, P. C. (1975). A predictive model of rainfall interception in forests. II. Generalization of the model and comparison with observations in some coniferous and hardwood stands. J. Appl. Ecol., 12, 367–380.CrossRefGoogle Scholar
Sanders, F. and Gyakum, J. R. (1980). Synoptic-dynamic climatology of the “bomb.”Mon. Wea. Rev., 108, 1589–1606.2.0.CO;2>CrossRefGoogle Scholar
Santanello, J. A. Jr. and Carlson, T. N. (2001). Mesoscale simulation of rapid soil drying and its implication for predicting daytime temperature. J. Hydrometeor., 2, 71–88.2.0.CO;2>CrossRefGoogle Scholar
Sarachik, E. S. (2003). The ocean in climate, ch. 10. In Handbook of Weather, Climate, and Water. Dynamics, Climate, Physical Meteorology, Weather Systems, and Measurements, ed. Potter, T. D. and Colman, B. R.. Wiley-Interscience, pp. 129–133.CrossRefGoogle Scholar
Sasamori, T. (1968). The radiative cooling calculation for application to general circulation experiments. J. Appl. Meteor., 7, 721–729.2.0.CO;2>CrossRefGoogle Scholar
Satheesh, S. K., Ramanathan, V., Li-Jones, X., et al. (1999). A model for the natural and anthropogenic aerosols over the tropical Indian Ocean derived from Indian Ocean Experiment data. J. Geophys. Res., 104, 27 421–27 440.CrossRefGoogle Scholar
Sato, N., Sellers, P. J., Randall, D. A., et al. (1989). Effects of implementing the simple biosphere model in a general circulation model. J. Atmos. Sci., 46, 2757–2782.2.0.CO;2>CrossRefGoogle Scholar
Savijärvi, H. (1990). Fast radiation parameterization schemes for mesoscale and short-range forecast models. J. Appl. Meteor., 29, 437–447.2.0.CO;2>CrossRefGoogle Scholar
Savijärvi, H. and Räisänen, P. (1998). Long-wave optical properties of water clouds and rain. Tellus, 50A, 1–11.Google Scholar
Schaake, J. C., Koren, V. I., Duan, Q.-Y., Mitchell, K., and Chen, F. (1996). Simple water balance model for estimating runoff at different spatial and temporal scales. J. Geophys. Res., 101, 7461–7475.CrossRefGoogle Scholar
Schaake, J. C.et al. (2004). An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res., 109, doi: 10.1019/2002JD003309.CrossRefGoogle Scholar
Schlesinger, W. H. (1997). Biogeochemistry: an Analysis of Global Change. Academic Press.Google Scholar
Schneider, E. K., Zhu, Z., Giese, B. S., et al. (1997). Annual cycle and ENSO in a coupled ocean–atmosphere general circulation model. Mon. Wea. Rev., 125, 680–702.2.0.CO;2>CrossRefGoogle Scholar
Schneider, J. M. and Lilly, D. K. (1999). An observational and numerical study of a sheared, convective boundary layer. Part I: Phoenix II observations, statistical description, and visualization. J. Atmos. Sci., 56, 3059–3078.2.0.CO;2>CrossRefGoogle Scholar
Schneider, S. H. (1972). Cloudiness as a global climate feedback mechanism: the effects on the radiation balance and surface temperature variations in cloudiness. J. Atmos. Sci., 29, 1413–1422.2.0.CO;2>CrossRefGoogle Scholar
Schreiner, A. J., Unger, D. A., Menzel, W. P., et al. (1993). A comparison of ground and satellite observations of cloud cover. Bull. Amer. Meteor. Soc., 74, 1851–1861.2.0.CO;2>CrossRefGoogle Scholar
Schultz, D. M., Bracken, W. E., Bosart, L. F., et al. (1997). The 1993 Superstorm cold surge: frontal structure, gap flow, and tropical impact. Mon. Wea. Rev., 125, 5–39; Corrigendum, 125, 662.2.0.CO;2>CrossRefGoogle Scholar
Schultz, D. M., Arndt, D. S., Stensrud, D. J., and Hanna, J. W. (2004). Snowbands during the cold-air outbreak of 23 January 2003. Mon. Wea. Rev., 132, 827–842.2.0.CO;2>CrossRefGoogle Scholar
Schultz, P. (1995). An explicit cloud physics parameterization for operational numerical weather prediction. Mon. Wea. Rev., 123, 3331–3343.2.0.CO;2>CrossRefGoogle Scholar
Schwarzkopf, M. D. and Fels, S. B. (1991). The simplified exchange model revisited: an accurate, rapid method for computation of infrared cooling rates and fluxes. J. Geophys. Res., 96, 9075–9096.CrossRefGoogle Scholar
Scinocca, J. F. and McFarlane, N. A. (2000). The parametrization of drag induced by stratified flow over anisotropic orography. Quart. J. Roy. Meteor. Soc., 126, 2353–2393.CrossRefGoogle Scholar
Scorer, R. S. (1949). Theory of waves in the lee of mountains. Quart. J. Roy. Meteor. Soc., 75, 41–56.CrossRefGoogle Scholar
Segal, M. and Arritt, R. W. (1992). Nonclassical mesoscale circulations caused by surface sensible heat-flux gradients. Bull. Amer. Meteor. Soc., 73, 1593–1604.2.0.CO;2>CrossRefGoogle Scholar
Segal, M., Avissar, R., McCumber, M. C., and Pielke, R. A. (1988). Evaluation of vegetation effects on the generation and modification of mesoscale circulations. J. Atmos. Sci., 45, 2268–2292.2.0.CO;2>CrossRefGoogle Scholar
Segal, M., Purdom, J. F. W., Song, J. L., Pielke, R. A., and Mahrer, Y. (1986). Evaluation of cloud shading effects on the generation and modification of mesoscale circulations. Mon. Wea. Rev., 114, 1201–1212.2.0.CO;2>CrossRefGoogle Scholar
Segal, M., Schreiber, W. E., Kallos, G., et al. (1989). The impact of crop areas in northeast Colorado on midsummer mesoscale thermal circulations. Mon. Wea. Rev., 117, 809–825.2.0.CO;2>CrossRefGoogle Scholar
Segal, M., Physick, W. L., Heim, J. E., and Arritt, R. W. (1993). The enhancement of cold-front temperature contrast by differential cloud cover. Mon. Wea. Rev., 121, 867–873.2.0.CO;2>CrossRefGoogle Scholar
Segele, Z. T., Stensrud, D. J., Ratcliffe, I. C., and Henebry, G. M. (2005). Influence of a hailstreak on boundary layer evolution. Mon. Wea. Rev., 133, 942–960.CrossRefGoogle Scholar
Seguin, B. and Gignoux, N. (1974). Etude experimentale de l'influence d'un reseau de brise-vent sur le profil vertical de vitesse du vent (Experimental study of the effects of a network of windbreaks on the vertical profile of windspeed). Agric. For. Meteor., 13, 15–23.CrossRefGoogle Scholar
Sela, J. G. (1980). Spectral modeling at the National Meteorological Center. Mon. Wea. Rev., 108, 1279–1292.2.0.CO;2>CrossRefGoogle Scholar
Sellers, P. J. (1985). Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens., 6, 1335–1372.CrossRefGoogle Scholar
Sellers, P. J.(1987). Canopy reflectance, photosynthesis, and transpiration. II. The role of biophysics in the linearity of their interdepencence. Rem. Sens. Env., 21, 143–183.CrossRefGoogle Scholar
Sellers, P. J., Mintz, Y., Sud, Y. C., and Dalcher, A. (1986). A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43, 505–531.2.0.CO;2>CrossRefGoogle Scholar
Shettle, E. P. and Weinman, J. A. (1970). The transfer of solar irradiance through inhomogeneous turbid atmospheres evaluated by Eddington's approximation. J. Atmos. Sci., 27, 1048–1055.2.0.CO;2>CrossRefGoogle Scholar
Shukla, J. and Sud, Y. (1981). Effect of cloud–radiation feedback on the climate of a general circulation model. J. Atmos. Sci., 38, 2337–2353.2.0.CO;2>CrossRefGoogle Scholar
Shulman, M. L., Jacobson, M. C., Charlson, R. J., Synovec, R. E., and Young, T. E. (1997). Dissolution behavior and surface tension effects of organic compounds in nucleating cloud droplets. Geophys. Res. Lett., 23, 277–280.CrossRefGoogle Scholar
Shuman, F. G. (1989). History of numerical weather prediction at the National Meteorological Center. Wea. Forecasting, 4, 286–296.2.0.CO;2>CrossRefGoogle Scholar
Shuman, F. G. and Hovermale, J. B. (1968). An operational six-layer primitive equation model. J. Appl. Meteor., 7, 525–547.2.0.CO;2>CrossRefGoogle Scholar
Siebesma, A. P. and Cuijpers, J. W. M. (1995). Evaluation of parametric assumptions for shallow cumulus convection. J. Atmos. Sci., 52, 650–666.2.0.CO;2>CrossRefGoogle Scholar
Simpson, J. and Wiggert, V. (1969). Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471–489.2.3.CO;2>CrossRefGoogle Scholar
Sitch, S., Smith, B., Prentice, I. C., et al. (2003). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biol., 9, 161–185.CrossRefGoogle Scholar
Slater, A. G., Schlosser, C. A., Desborough, C. E., et al. (2001). The representation of snow in land surface schemes: results from PILPS 2(d). J. Hydrometeor., 2, 7–25.2.0.CO;2>CrossRefGoogle Scholar
Slingo, J. M. (1980). A cloud parameterization scheme derived from GATE data for use with a numerical model. Quart. J. Roy. Meteor. Soc., 106, 747–770.CrossRefGoogle Scholar
Slingo, J. M.(1987). The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899–927.CrossRefGoogle Scholar
Smagorinsky, J. (1960). On the dynamical prediction of large-scale condensation by numerical methods. Geophys. Monogr., No. 5. American Geophysical Union, pp. 71–78.Google Scholar
Smagorinsky, J., Manabe, S., and Holloway, J. L. Jr. (1965). Numerical results from a nine-level general circulation model of the atmosphere. Mon. Wea. Rev., 93, 727–768.2.3.CO;2>CrossRefGoogle Scholar
Smirnova, T. G., Brown, J. M., and Benjamin, S. G. (1997). Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 1870–1884.2.0.CO;2>CrossRefGoogle Scholar
Smirnova, T. G., Brown, J. M., Benjamin, S. G., and Kim, D. (2000). Parameterization of cold-season processes in the MAPS land-surface scheme. J. Geophys. Res., 105, 4077–4086.CrossRefGoogle Scholar
Smith, E. A., Wai, M. M.-K., Cooper, H. J., Rubes, M. T., and Hsu, A. (1994). Linking boundary-layer circulations and surface processes during FIFE 89. Part I: Observational analysis. J. Atmos. Sci., 51, 1497–1529.2.0.CO;2>CrossRefGoogle Scholar
Smith, P. L. (2003). Raindrop size distributions: exponential or gamma – does the difference matter?J. Appl. Meteor., 42, 1031–1034.2.0.CO;2>CrossRefGoogle Scholar
Smith, R. B. (1979). The influence of mountains on the atmosphere. Adv. Geophys., 33, 87–230.CrossRefGoogle Scholar
Smith, R. B.(1990). A scheme for predicting layer clouds and their water-content in a general-circulation model. Quart. J. Roy. Meteor. Soc., 116, 435–460.CrossRefGoogle Scholar
Smith, S. D. (1988). Coefficients for seas surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J. Geophys. Res., 93, 15 467–15 472.CrossRefGoogle Scholar
Snyder, C. and Zhang, F. (2003). Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663–1677.CrossRefGoogle Scholar
Spahn, J. F. (1976). The airborne hail disdrometer: an analysis of its 1975 performance. Rep. 76–13, Inst. Atmos. Sci., South Dakota School of Mines and Technology, Rapid City, SD.
Stainforth, D. A., Aina, T., Christensen, C., et al. (2005). Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433, 403–406.CrossRefGoogle ScholarPubMed
Staley, D. O. and Jurica, G. M. (1970). Flux emissivity tables for water vapor, carbon dioxide and ozone. J. Appl. Meteor., 9, 365–372.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J. (1993). Elevated residual layers and their influence on surface boundary-layer evolution. J. Atmos. Sci., 50, 2284–2293.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J.(1996). Importance of low-level jets to climate: a review. J. Climate, 9, 1698–1711.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J.(1996). Effects of a persistent, midlatitude mesoscale region of convection on the large-scale environment during the warm season. J. Atmos. Sci., 53, 3503–3527.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J. and Anderson, J. L. (2001). Is midlatitude convection an active or a passive player in producing global circulation patterns?J. Climate, 14, 2222–2237.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J. and Fritsch, J. M. (1994). Mesoscale convective systems in weakly forced large-scale environments. Part III: Numerical simulations and implications for operational forecasting. Mon. Wea. Rev., 112, 2084–2104.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J. and Weiss, S. J. (2002). Mesoscale model ensemble forecasts of the 3 May 1999 tornado outbreak. Wea. Forecasting, 17, 526–543.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J. and Yussouf, N. (2003). Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131, 2510–2524.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J. and Yussouf, N.(2005). Bias-corrected short-range ensemble forecasts of near surface variables. Meteor. Appl., 12, 217–230.CrossRefGoogle Scholar
Stensrud, D. J., Brooks, H. E., Du, J., Tracton, M. S., and Rogers, E. (1999). Using ensembles for short-range forecasting. Mon. Wea. Rev., 127, 433–446.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J., Brooks, H. E., Du, J., Tracton, M. S., and Rogers, E.(2000a). Reply to comments on “Using ensembles for short-range forecasting.”Mon. Wea. Rev., 128, 3021–3023.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J., Bao, J.-W., and Warner, T. T. (2000b). Using initial condition and model physics perturbations in short-range ensembles of mesoscale convective systems. Mon. Wea. Rev., 128, 2077–2107.2.0.CO;2>CrossRefGoogle Scholar
Stensrud, D. J., Yussouf, N., Baldwin, M. E., et al. (2006). The New England High-Resolution Temperature Program (NEHRTP). Bull. Amer. Meteor. Soc., 87, 491–498.CrossRefGoogle Scholar
Stephens, G. L. (1978a). Radiative properties of extended water clouds. Part I: Theory. J. Atmos. Sci., 35, 2111–2122.2.0.CO;2>CrossRefGoogle Scholar
Stephens, G. L.(1978b). Radiative properties of extended water clouds. Part II: Parameterization schemes. J. Atmos. Sci., 35, 2123–2132.2.0.CO;2>CrossRefGoogle Scholar
Stephens, G. L.(1984). The parameterization of radiation for numerical weather prediction and climate models. Mon. Wea. Rev., 112, 826–867.2.0.CO;2>CrossRefGoogle Scholar
Stephens, G. L.(2005). Cloud feedbacks in the climate system: a critical review. J. Climate, 18, 237–273.CrossRefGoogle Scholar
Stephens, G. L. and Tsay, S.-C. (1990). On the cloud absorption anomaly. Quart. J. Roy. Meteor. Soc., 116, 671–704.CrossRefGoogle Scholar
Stephens, G. L., Tsay, S.-C., Stackhouse, P. W. Jr., and Flatau, P. J. (1990). The relevance of the microphysical and radiative properties of cirrus clouds to climatic feedback. J. Atmos. Sci., 47, 1742–1753.2.0.CO;2>CrossRefGoogle Scholar
Stephens, G. L., Wood, N. B., and Gabriel, P. M. (2004). Assessment of the parameterization of subgrid-scale cloud effects on radiative transfer. Part I: Vertical overlap. J. Atmos. Sci., 61, 715–732.2.0.CO;2>CrossRefGoogle Scholar
Stern, W. F., Pierrehumbert, R. T., Sirutis, J., Ploshay, J., and Miyakoda, K. (1987). Recent development in the GFDL extended-range forecasting system. J. Meteor. Soc. Japan (special volume WMO/IUGG NWP Smp.), 359–363.Google Scholar
Stevens, B., Moeng, C.-H., Ackerman, A. S., et al. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Mon. Wea. Rev., 133, 1443–1462.CrossRefGoogle Scholar
Stockdale, T. N., Anderson, D. L. T., Alves, J. O. S., and Balmaseda, M. A. (1998). Global seasonal rainfall forecasts using a coupled ocean–atmosphere model. Nature, 392, 370–373.CrossRefGoogle Scholar
Stoelinga, M. T., Hobbs, P. V., Mass, C. V., et al. (2003). Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE). Bull. Amer. Meteor. Soc., 84, 1807–1826.CrossRefGoogle Scholar
Stokes, G. M. and Schwartz, S. E. (1994). The Atmospheric Radiation Measurement (ARM) program: programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75, 1201–1221.2.0.CO;2>CrossRefGoogle Scholar
Straka, J. M. and Mansell, E. R. (2005). A bulk microphysics parameterization with multiple ice precipitation categories. J. Appl. Meteor., 44, 445–466.CrossRefGoogle Scholar
Straka, J. M. and Rasmussen, E. N. (1997). Toward improving microphysical parameterizations of conversion processes. J. Appl. Meteor., 36, 896–902.2.0.CO;2>CrossRefGoogle Scholar
Stull, R. B. (1976). The energetics of entrainment across a density interface. J. Atmos. Sci., 33, 1260–1278.Google Scholar
Stull, R. B.(1984). Transilient turbulence theory. Part I: The concept of eddy mixing across small distances. J. Atmos. Sci., 41, 3351–3367.2.0.CO;2>CrossRefGoogle Scholar
Stull, R. B.(1988). An Introduction to Boundary Layer Meteorology. Kluwer.CrossRefGoogle Scholar
Stull, R. B.(1991). Static stability – an update. Bull. Amer. Meteor. Soc., 72, 1521–1529.2.0.CO;2>CrossRefGoogle Scholar
Stull, R. B.(1993). Review of non-local mixing in turbulent atmospheres: transilient turbulence theory. Bound.-Layer Meteor., 62, 21–96.CrossRefGoogle Scholar
Stumpf, H. G. (1975). Satellite detection of upwelling in the Gulf of Tehuantepec, Mexico. J. Phys. Oceanogr., 5, 383–388.2.0.CO;2>CrossRefGoogle Scholar
Su, H. B., Paw U, K. T., and Shaw, R. (1996). Development of a coupled leaf and canopy model for the simulation of plant-atmosphere interaction. J. Appl. Meteor., 35, 734–748.2.0.CO;2>CrossRefGoogle Scholar
Sublette, M. S. and Young, G. S. (1996). Warm-season effects of the Gulf Stream on mesoscale characteristics of the atmospheric boundary layer. Mon. Wea. Rev., 124, 653–667.2.0.CO;2>CrossRefGoogle Scholar
Sundqvist, H. (1988). Parameterization of condensation and associated clouds for weather prediction and general circulation simulation. In Physically-Based Modelling and Simulation of Climate and Climate Change, ed. Schlesinger, M. E.. Reidel, pp. 433–461.Google Scholar
Sundqvist, H., Berge, E., and Kristjansson, J. E. (1989). Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117, 1641–1657.2.0.CO;2>CrossRefGoogle Scholar
Swann, H. (1998). Sensitivity to the representation of precipitating ice in CRM simulations of deep convection. Atmos. Res., 47–48, 415–435.CrossRefGoogle Scholar
Sweet, W., Felt, R., Kerling, J., and Violette, P. (1981). Air–sea interaction effects in the lower troposphere across the north wall of the Gulf Stream. Mon. Wea. Rev., 109, 1042–1052.2.0.CO;2>CrossRefGoogle Scholar
Swinbank, W. C. (1968). A comparison between predictions of dimensional analysis for the constant-flux layer and observations in unstable conditions. Quart. J. Roy. Meteor. Soc., 94, 460–467.CrossRefGoogle Scholar
Taiz, L. and Zeiger, E. (2002). Plant Physiology. Sinauer Associates, Inc.Google Scholar
Takara, E. E. and Ellingson, R. G. (2000). Broken cloud field longwave scattering effects. J. Atmos. Sci., 57, 1298–1310.2.0.CO;2>CrossRefGoogle Scholar
Tao, W.-K., Scala, J. R., Ferrier, B., and Simpson, J. (1995). The effect of melting processes on the development of a tropical and a midlatitude squall line. J. Atmos. Sci., 52, 1934–1948.2.0.CO;2>CrossRefGoogle Scholar
Taylor, P. K. and Yelland, M. A. (2001). The dependence of sea surface roughness on the height and steepness of the waves. J. Phys. Oceanogr., 31, 572–590.2.0.CO;2>CrossRefGoogle Scholar
Teixeira, J. and Hogan, T. F. (2002). Boundary layer clouds in a global atmospheric model: simple cloud cover parameterizations. J. Climate, 15, 1261–1275.2.0.CO;2>CrossRefGoogle Scholar
Teixeira, J., Ferreira, J. P., Miranda, P. M. A., et al. (2004). A new mixing-length formulation for the parameterization of dry convection: implementation and evaluation in a mesoscale model. Mon. Wea. Rev., 132, 2698–2707.CrossRefGoogle Scholar
Telford, J. W. (1955). A new aspect of coalescence theory. J. Meteor., 12, 436–444.2.0.CO;2>CrossRefGoogle Scholar
Telford, J. W.(1975). Turbulence, entrainment and mixing in cloud dynamics. Pure Appl. Geophys., 113, 1067–1084.CrossRefGoogle Scholar
Thiebaux, J., Rogers, E., Wang, W., and Katz, B. (2003). A new high-resolution blended real-time global sea surface temperature analysis. Bull. Amer. Meteor. Soc., 84, 645–656.CrossRefGoogle Scholar
Thompkins, A. M. (2002). A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 1917–1942.2.0.CO;2>CrossRefGoogle Scholar
Thompson, G., Rasmussen, R. M., and Manning, K. (2004). Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519–542.2.0.CO;2>CrossRefGoogle Scholar
Thompson, P. D. (1957). Uncertainty of the initial state as a factor in the predictability of large scale atmospheric flow patterns. Tellus, 9, 275–295.CrossRefGoogle Scholar
Tian, L. and Curry, J. A. (1989). Cloud overlap statistics. J. Geophys. Res., 94, 9925–9935.CrossRefGoogle Scholar
Tibaldi, S. (1986). Envelope orography and the maintenance of quasi-stationary waves in the ECMWF model. Adv. Geophys., 29, 339–374.CrossRefGoogle Scholar
Tiedtke, M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800.2.0.CO;2>CrossRefGoogle Scholar
Tiedtke, M.(1993). Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040–3061.2.0.CO;2>CrossRefGoogle Scholar
Tokay, A. and Short, D. A. (1996). Evidence from tropical raindrop spectra of the origin of rain from stratiform versus convective clouds. J. Appl. Meteor., 35, 355–371.2.0.CO;2>CrossRefGoogle Scholar
Tompkins, A. M. (2001). Organization of tropical convection in low vertical wind shears: the role of water vapor. J. Atmos. Sci., 58, 529–545.2.0.CO;2>CrossRefGoogle Scholar
Toth, Z. and Kalnay, E. (1993). Ensemble forecasting at NMC: the generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330.2.0.CO;2>CrossRefGoogle Scholar
Toth, Z. and Kalnay, E.(1997). Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297–3319.2.0.CO;2>CrossRefGoogle Scholar
Trenberth, K. E. and Guillemot, C. J. (1996). Physical processes involved in the 1988 drought and 1993 floods in North America. J. Climate, 9, 1288–1298.2.0.CO;2>CrossRefGoogle Scholar
Tribbia, J. J. (1991). The rudimentary theory of atmospheric teleconnections associated with ENSO. In Teleconnections Linking Worldwide Climate Anomalies, ed. Glantz, M. H., Katz, R. W., and Nicholls, N.. Cambridge University Press, pp. 285–308.Google Scholar
Tribbia, J. J. and Baumhefner, D. P. (2004). Scale interactions and atmospheric predictability: an updated perspective. Mon. Wea. Rev., 132, 703–713.2.0.CO;2>CrossRefGoogle Scholar
Trier, S. B., Davis, C. A., and Tuttle, J. D. (2000). Long-lived mesoconvective vortices and their environment. Part I: Observations from the central United States during the 1998 warm season. Mon. Wea. Rev., 128, 3376–3395.2.0.CO;2>CrossRefGoogle Scholar
Tripoli, G. J. and Cotton, W. R. (1980). A numerical investigation of several factors contributing to the observed variable intensity of deep convection over south Florida. J. Appl. Meteor., 19, 1037–1063.2.0.CO;2>CrossRefGoogle Scholar
Troen, I. and Mahrt, L. (1986). A simple model of the atmospheric boundary layer: sensitivity to surface evaporation. Bound.-Layer Meteor., 37, 129–148.CrossRefGoogle Scholar
Tucker, C. J., Gatlin, J. A., and Schneider, S. R. (1984). Monitoring vegetation in the Nile delta with NOAA-6 and NOAA-7 AVHRR imagery. Photo. Eng. Remote Sens., 50, 53–61.Google Scholar
Twomey, S. (1976). Computations of the absorption of solar radiation by clouds. J. Atmos. Sci., 33, 1087–1091.2.0.CO;2>CrossRefGoogle Scholar
Twomey, S.(1977). The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci., 34, 1149–1152.2.0.CO;2>CrossRefGoogle Scholar
Unsworth, M. H. and Monteith, J. L. (1975). Long-wave radiation at the ground. I. Angular distribution of incoming radiation. Quart. J. Roy. Meteor. Soc., 101, 13–24.CrossRefGoogle Scholar
Hulst, H. C. (1945). Theory of absorption lines in the atmosphere of the Earth. Ann. Astrophys., 8, 21–34.Google Scholar
Viterbo, P. and Beljaars, A. C. M. (1995). An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate, 8, 2716–2748.2.0.CO;2>CrossRefGoogle Scholar
Walcek, C. J. (1994). Cloud cover and its relationship to relative humidity during a springtime midlatitude cyclone. Mon. Wea. Rev., 122, 1021–1035.2.0.CO;2>CrossRefGoogle Scholar
Walko, R., Cotton, W. R., Meyers, M. P., and Harrington, J. Y. (1995). New RAMS cloud microphysics parameterization. Part I: The single-moment scheme. Atmos. Res., 38, 29–62.CrossRefGoogle Scholar
Wallace, J. M. and Hobbs, P. V. (1977). Atmospheric Science: an Introductory Survey. Academic Press.Google Scholar
Wallace, J. M., Tibaldi, S., and Simmons, A. J. (1983). Reduction of systematic forecast errors in the ECMWF model through the introduction of an envelope orography. Quart. J. Roy. Meteor. Soc., 109, 683–717.CrossRefGoogle Scholar
Walters, M. K. (2000). Comments on “The differentiation between grid spacing and resolution and their application to numerical modeling.”Bull. Amer. Meteor. Soc., 81, 2475–2477.2.3.CO;2>CrossRefGoogle Scholar
Walton, C. C. (1988). Nonlinear multichallen algorithms for estimating sea surface temperature with AVHRR satellite data. J. Appl. Meteor., 27, 115–124.2.0.CO;2>CrossRefGoogle Scholar
Wandishin, M. S., Mullen, S. L., Stensrud, D. J., and Brooks, H. E. (2001). Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129, 729–747.2.0.CO;2>CrossRefGoogle Scholar
Wang, W. and Seaman, N. L. (1997). A comparison study of convective parameterization schemes in a mesoscale model. Mon. Wea. Rev., 125, 252–278.2.0.CO;2>CrossRefGoogle Scholar
Wang, Y.-P. and Jarvis, P. J. (1990). Description and validation of an array model – MAESTRO. Agric. For. Meteor., 51, 257–280.CrossRefGoogle Scholar
Warner, T. T. and Seaman, N. L. (1990). A real-time, mesoscale numerical weather prediction system used for research, teaching, and public service at The Pennsylvania State University. Bull. Amer. Meteor. Soc., 71, 792–805.2.0.CO;2>CrossRefGoogle Scholar
Webb, R. S., Rosenzweig, C. E., and Levine, E. R. (1993). Specifying land surface characteristics in general circulation models: soil profile data and derived water-holding capacities. Global Biogeochemical Cycles, 7, 97–108.CrossRefGoogle Scholar
Weber, S. L., Storch, H., Viterbo, P., and Zambresky, L. (1993). Coupling an ocean wave model to an atmospheric general circulation model. Climate Dyn., 9, 53–61.CrossRefGoogle Scholar
Webster, P. J. and G. L. Stephens (1983). Cloud–radiation interaction and the climate problem. In The Global Climate, ed. Houghton, J. T.. Cambridge University Press, pp. 63–78.Google Scholar
Weidinger, T., Pinto, J., and Horváth, L. (2000). Effects of uncertainties in universal functions, roughness length, and displacement height on the calibration of surface layer fluxes. Meteor. Zeit., 9, 139–154.CrossRefGoogle Scholar
Weisman, M. L., Skamarock, W. C., and Klemp, J. B. (1997). The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527–548.2.0.CO;2>CrossRefGoogle Scholar
Welch, R. M., S. K. Cox, and J. M. Davis (1980). Solar Radiation and Clouds. Meteorology Monographs, No. 39. American Meteorological Society.CrossRef
Wetzel, P. and Chang, J.-T. (1987). Concerning the relationship between evapotranspiration and soil moisture. J. Clim. Appl. Meteor., 26, 18–27.2.0.CO;2>CrossRefGoogle Scholar
Wichmann, M. and Schaller, E. (1986). On the determination of the closure parameters in higher-order closure models. Bound.-Layer Meteor., 37, 323–341.CrossRefGoogle Scholar
Williams, R. J., Broersma, K., and Ryswyk, A. L. (1978). Equilibrium and actual evapotranspiration from a very dry vegetated surface. J. Appl. Meteor., 17, 1827–1832.2.0.CO;2>CrossRefGoogle Scholar
Wilson, D. R. and Ballard, S. P. (1999). A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quart. J. Roy. Meteor. Soc., 125, 1607–1636.CrossRefGoogle Scholar
Wilson, L. J. and Vallée, M. (2002). The Canadian updateable model output statistics (UMOS) system: design and development tests. Wea. Forecasting, 17, 206–222.2.0.CO;2>CrossRefGoogle Scholar
Wolf, B. J. and Johnson, D. R. (1995a). The mesoscale forcing of a midlatitude upper-tropospheric jet streak by a simulated convective system. Part I: Mass circulation and ageostrophic processes. Mon. Wea. Rev., 123, 1059–1087.2.0.CO;2>CrossRefGoogle Scholar
Wolf, B. J. and Johnson, D. R.(1995b). The mesoscale forcing of a midlatitude upper-tropospheric jet streak by a simulated convective system. Part II: Kinetic energy and resolution analysis. Mon. Wea. Rev., 123, 1088–1111.2.0.CO;2>CrossRefGoogle Scholar
Woodcock, F. and Engel, C. (2005). Operational consensus forecasts. Wea. Forecasting, 20, 101–111.CrossRefGoogle Scholar
Wu, M. L. (1980). The exchange of infrared radiative energy in the troposphere. J. Geophys. Res., 85, 4084–4090.CrossRefGoogle Scholar
Wyngaard, J. C. (1985). Structure of the planetary boundary layer and implications for its modeling. J. Clim. Appl. Meteor., 24, 1131–1142.2.0.CO;2>CrossRefGoogle Scholar
Xu, M., Bao, J.-W., Warner, T. T., and Stensrud, D. J. (2001a). Effect of time step size in MM5 simulations of a mesoscale convective system. Mon. Wea. Rev., 129, 502–516.2.0.CO;2>CrossRefGoogle Scholar
Xu, M., Stensrud, D. J., Bao, J.-W., and Warner, T. T. (2001b). Applications of the adjoint technique to short-range ensemble forecasting of mesoscale convective systems. Mon. Wea. Rev., 129, 1395–1418.2.0.CO;2>CrossRefGoogle Scholar
Xue, H. and Bane, J. M. Jr. (1997). A numerical investigation of the Gulf Stream and its meanders in response to cold air outbreaks. J. Phys. Oceanogr., 27, 2606–2629.2.0.CO;2>CrossRefGoogle Scholar
Xue, H., Bane, J. M. Jr., and Goodman, L. M. (1995). Modification of the Gulf Stream through strong air–sea interactions in winter: observations and numerical simulations. J. Phys. Oceanogr., 25, 533–557.2.0.CO;2>CrossRefGoogle Scholar
Xue, H., Pan, Z., and Bane, J. M. Jr. (2000). A 2D coupled atmosphere–ocean model study of air–sea interactions during a cold air outbreak over the Gulf Stream. Mon. Wea. Rev., 128, 973–996.2.0.CO;2>CrossRefGoogle Scholar
Xue, Y., Fennessy, M. J., and Sellers, P. J. (1996). Impact of vegetation properties on U.S. summer weather prediction. J. Geophys. Res., 101, 7419–7430.CrossRefGoogle Scholar
Yaglom, J. M. (1977). Comments on wind and temperature flux-profile relationships. Bound.-Layer Meteor., 11, 89–102.CrossRefGoogle Scholar
Yamada, T. and Mellor, G. (1975). A simulation of the Wangara boundary layer data. J. Atmos. Sci., 32, 2309–2329.2.0.CO;2>CrossRefGoogle Scholar
Yanai, M. and R. H. Johnson (1993). Impacts of cumulus convection on thermodynamic fields. In The Representation of Cumulus Convection in Numerical Models of the Atmosphere, ed. K. A. Emanuel and D. J. Raymond. Meteorology Monographs, No. 46. American Meteorological Society, pp. 39–62.
Yanai, M., Esbensen, S., and Chu, J. (1973). Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611–627.2.0.CO;2>CrossRefGoogle Scholar
Yanai, M., Chu, J.-H., Stark, T. E., and Nitta, T. (1976). Response of deep and shallow tropical maritime cumuli to large-scale processes. J. Atmos. Sci., 33, 976–991.2.0.CO;2>CrossRefGoogle Scholar
Yin, Z. and Williams, T. H. L. (1997). Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite images for a hydrological model. Photogr. Eng. Remote Sens., 63, 69–77.Google Scholar
Young, G. S. (1988). Turbulence structure of the convective boundary layer. Part II: Phoenix 78 aircraft observations of thermals and their environments. J. Atmos. Sci., 45, 727–735.2.0.CO;2>CrossRefGoogle Scholar
Yussouf, N., Stensrud, D. J., and Lakshmivarahan, S. (2004). Cluster analysis of multimodel ensemble data over New England. Mon. Wea. Rev., 132, 2452–2462.2.0.CO;2>CrossRefGoogle Scholar
Zamora, R. J., Solomon, S., Dutton, E. G., et al. (2003). Comparing MM5 radiative fluxes with observations gathered during the 1995 and 1999 Nashville southern oxidant studies. J. Geophys. Res., 108, doi: 10.1029/2002JD002122.CrossRefGoogle Scholar
Zamora, R. J., Dutton, E. G., Trainer, M., et al. (2005). The accuracy of solar irradiance calculations used in mesoscale numerical weather prediction. Mon. Wea. Rev., 133, 783–792.CrossRefGoogle Scholar
Zängl, G. (2002). An improved method for computing horizontal diffusion in a sigma-coordinate model and its application to simulations over mountainous topography. Mon. Wea. Rev., 130, 1423–1432.2.0.CO;2>CrossRefGoogle Scholar
Zeman, O. (1981). Progress in the modeling of planetary boundary layers. Ann. Rev. Fluid Mech., 13, 253–272.CrossRefGoogle Scholar
Zeng, X. (2001). Global vegetation root distribution for land modeling. J. Hydrometeor., 2, 525–530.2.0.CO;2>CrossRefGoogle Scholar
Zeng, X. and Dickinson, R. E. (1998). Effect of surface sublayer on surface skin temperature and fluxes. J. Climate, 11, 537–550.2.0.CO;2>CrossRef
Zeng, X., Zhao, M., and Dickinson, R. E. (1998). Comparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using the TOGA COARE data. J. Climate, 11, 2628–2644.2.0.CO;2>CrossRefGoogle Scholar
Zhang, D.-L. and Anthes, R. A. (1982). A high-resolution model of the planetary boundary layer – sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor., 21, 1594–1609.2.0.CO;2>CrossRefGoogle Scholar
Zhang, D.-L. and Harvey, R. (1995). Enhancement of extratropical cyclogenesis by a mesoscale convective system. J. Atmos. Sci., 52, 1107–1127.2.0.CO;2>CrossRefGoogle Scholar
Zhang, D.-L. and Zheng, W.-Z. (2004). Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. J. Appl. Meteor., 43, 157–169.2.0.CO;2>CrossRefGoogle Scholar
Zhang, D.-L., Hsie, E.-Y., and Moncrieff, M. W. (1988). A comparison of explicit and implicit predictions of convective and stratiform precipitating weather systems with a meso-β scale numerical model. Quart. J. Roy. Meteor. Soc., 114, 31–60.Google Scholar
Zhang, G. J. and Cho, H.-R. (1991). Parameterization of the vertical transport of momentum by cumulus clouds. Part II: Application. J. Atmos. Sci., 48, 2448–2457.2.0.CO;2>CrossRefGoogle Scholar
Zhang, H. and Frederiksen, C. S. (2003). Local and nonlocal impacts of soil moisture initialization on AGCM seasonal forecasts: a model sensitivity study. J. Climate, 16, 2117–2137.2.0.CO;2>CrossRefGoogle Scholar
Zhao, Q. and Carr, F. H. (1997). A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 1931–1953.2.0.CO;2>CrossRefGoogle Scholar
Zhou, Y. P. and Cess, R. D. (2000). Validation of longwave atmospheric radiation models using atmospheric radiation measurement data. J. Geophys. Res., 105, 29 703–29 716.CrossRefGoogle Scholar
Zhu, Y. and Newell, R. E. (1998). A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725–735.2.0.CO;2>CrossRefGoogle Scholar
Ziegler, C. L. (1985). Retrieval of thermal and microphysical variables in observed convective storms. Part I: Model development and preliminary testing. J. Atmos. Sci., 42, 1487–1509.2.0.CO;2>CrossRefGoogle Scholar
Ziegler, C. L.(1988). Retrieval of thermal and microphysical variables in observed convective storms. Part II: Sensitivity of cloud processes to variation of the microphysical parameterization. J. Atmos. Sci., 45, 1072–1090.2.0.CO;2>CrossRefGoogle Scholar
Ziegler, C. L., Ray, P. S., and Knight, N. C. (1983). Hail growth in an Oklahoma multicell storm. J. Atmos. Sci., 40, 1768–1791.2.0.CO;2>CrossRefGoogle Scholar
Ziegler, C. L., Lee, T. J., and Pielke, R. A. Sr. (1997). Convective initiation at the dryline: a modeling study. Mon. Wea. Rev., 125, 1001–1026.2.0.CO;2>CrossRefGoogle Scholar
Ziehmann, C. (2000). Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models. Tellus, 52A, 280–299.CrossRefGoogle Scholar
Zilitinkevich, S. (1995). Non-local turbulent transport: pollution dispersion aspects of coherent structure of convective flows. In Air Pollution Theory and Simulation, Air Pollution III, vol. I, ed. Power, H., Moussiopoulos, N., and Brebbia, C. A.. Computational Mechanics Publications, pp. 53–60.Google Scholar
Zilitinkevich, S. and Chalikov, D. V. (1968). On the determination of the universal wind and temperature profiles in the surface layer of the atmosphere. Izv. Acad. Sci. U.S.S.R., Atmos. Oceanic Phys., 4, 294–302.Google Scholar
Zilitinkevich, S., Grachev, A. A., and Fairall, C. W. (2001). Scaling reasoning and field data on the sea surface roughness lengths for scalars. J. Atmos. Sci., 58, 320–325.2.0.CO;2>CrossRefGoogle Scholar
Zobler, L. (1986). A world soil file for global climate modeling. NASA Tech. Memo. 87802.
Županski, D. and Mesinger, F. (1995). Four-dimensional variational assimilation of precipitation data. Mon. Wea. Rev., 123, 1112–1127.2.0.CO;2>CrossRefGoogle Scholar
Županski, M., Županski, D., Vukicevic, T., Eis, K., and Haar, T. Vonder (2005). CIRA/CSU four-dimensional variational data assimilation system. Mon. Wea. Rev., 133(4), 829–843.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • David J. Stensrud, National Oceanic and Atmospheric Administration, Norman, Oklahoma
  • Book: Parameterization Schemes
  • Online publication: 05 September 2013
  • Chapter DOI: https://doi.org/10.1017/CBO9780511812590.013
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • David J. Stensrud, National Oceanic and Atmospheric Administration, Norman, Oklahoma
  • Book: Parameterization Schemes
  • Online publication: 05 September 2013
  • Chapter DOI: https://doi.org/10.1017/CBO9780511812590.013
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • David J. Stensrud, National Oceanic and Atmospheric Administration, Norman, Oklahoma
  • Book: Parameterization Schemes
  • Online publication: 05 September 2013
  • Chapter DOI: https://doi.org/10.1017/CBO9780511812590.013
Available formats
×