Four different models for the prediction of frost events caused by air cooling due to thermal radiation are presented in this paper. Three of them are based on polynomial functions that simulate different types of temperature variations in comparison with an exponential one. The fourth, a post-processing method based on Kalman filters, is proposed for those cases where a systematic type of bias has emerged.

Most operational hail detection algorithms for single-polarisation radars are based on the analysis of the vertical profiles of radar reflectivity. At KNMI (Royal Netherlands Meteorological Institute) and RMI (Royal Meteorological Institute of Belgium) the probability of hail is derived from the height of the freezing level and the 45-dBZ radar echo top height (maximum height of the 45-dBZ echo). Echo tops are affected by errors in the measured reflectivity itself and by errors in the height assigned to these reflectivities. This study investigates the quality of radar echo top heights as a function of range and explores the implications for hail detection.

The method is based on the comparison between reflectivity measurements from two radars on the vertical cross-section extending between these radars. In a first step, sampling errors related to the radar Volume Coverage Patterns are analysed using idealised storm profiles. Subsequently, real reflectivity data for 25 thunderstorm episodes are compared. It is found that the quality of the maximum reflectivity measurements strongly deteriorates with range and that about half of this degradation can be attributed to overshooting effects. Height assignment differences between the two radars are limited to about 0.5 km. Errors on the reflectivity measurements strongly affect the frequency of 45-dBZ threshold exceedances. However, once the threshold is exceeded, errors in measuring the 45-dBZ echo top heights generally affect the derived probability of hail by less than 20%.

There are many challenges facing today's farmers and the use of weather and climate can help farmers in meeting and overcoming these challenges. Additionally, government decision-makers use information on actual and potential crop production impacts, a large percentage of which is based on weather and climate information such as drought, heavy rain events, and hurricanes. The Joint Agricultural Weather Facility (JAWF) is an example of one U.S. Government organization successfully using weather and climate information in many of its operational applications that are disseminated to users. These agricultural weather applications or products are grouped into tactical (short-term) and strategic (long-term) products. Examples of tactical products discussed include the Weekly Weather and Crop Bulletin (WWCB); routine and special crop-weather assessments, and a Regional Weather Network in Mississippi. Strategic products discussed include the U.S. Drought Monitor and the use of weather information in the monthly World Agricultural Supply and Demand Estimates (WASDE) process. The purpose of this paper is to provide some successful applications of climate and weather information and how this information is communicated between this agrometeorological service and farmers. Also, the goal is to provide lessons learned for other agrometeorological services around the world.

The Levanter is an easterly wind which frequently blows for several hours through the Strait of Gibraltar in summer. In the presence of a strong capping inversion, a synoptic or local scale pressure gradient forces the air through the gap between the orography on either side of the Strait. This flow cannot be resolved with either a global NWP model or a simplified linear model. Results from two Levanter cases, run using the Met Office Unified Model Version 5.3 at 1 km horizontal resolution over a 300 × 250 km limited area domain covering the Strait of Gibraltar, are presented, together with an unexpected source of verification.

This paper reports on work that demonstrates that weather radar can yield remarkably accurate rainfall measurements in Scotland. Analysis of data during 1999 and 2000 shows Nimrod radar data to have no consistent error bias and a 24% mean error in storm rainfall totals. For rivers such as the Ruchill Water in the Perthshire Highlands this highlights the fact that the observations and quality control corrections utilised by the Met Office Nimrod system may offer particular benefits in certain flood warning applications. Assessment of wind data indicates that smaller errors occur in Nimrod radar observations during lower wind speeds (less than 15 knots) and with an easterly airflow. However, errors may also be attributed to occasions when the Numerical Weather Prediction model fails to represent the wind conditions correctly at ground level.

The influence of a strong low-level temperature inversion on the occurrence of lee waves, rotors and hydraulic jumps has been investigated using high resolution numerical model simulations. The aim of the work is to develop tools for forecasting hazardous winds downstream of mountains. Two-dimensional simulations were conducted for a range of inversion heights and strengths and a fixed hill shape; lee waves, rotors and hydraulic jumps were found to occur. The flow type depends largely on the ratio of mountain height to inversion height and the upstream Froude number. A flow regime diagram based on these two parameters has been constructed and suggests that rotors could be forecast using upstream profiles, which are generally readily available from numerical weather prediction models.

The applicability of the regime diagram for two-dimensional flow to flows over real terrain has been tested using three-dimensional simulations of flows over East Falkland, South Atlantic, under a range of upstream conditions. The flow type is found to be determined largely by the upstream profiles of wind and temperature, and the maximum height of orography directly upstream, indicating that the flow regime diagram can be used to predict flow type downstream of such terrain. Various three-dimensional flow phenomena occur, such as flow channelling through gaps, and could be taken into account to improve the information available from the regime diagram.

The objective of the present study is the calculation of the spatial distribution of sunshine duration in the territory of Slovenia. Four maps at 1 km resolution were prepared to present the spatial distribution of sunshine duration for winter, spring, summer and autumn. The values on all four maps are 30-year mean seasonal sunshine duration, calculated from measurements from 43 meteorological stations in the period 1971–2000. The seasonal presentation was chosen due to the high inter-seasonal variability in spatial distribution of sunshine duration. The values on the maps were calculated on a mathematical horizon, to avoid the influence of geographical, urban and vegetation distortions. The interpolation model is a combination of a multivariate regression model, residual kriging and simple mathematical models. Geographical variables (altitude, latitude and longitude) are used in models to explain the spatial variability of sunshine duration. For each season, regionalisation is performed based on sunshine duration data, derived geographical data and radio-sounding data. The interpolation models are developed for each region separately and afterwards the calculated layers are merged using GIS techniques.

During an Inter-Regional Workshop on Improving Agrometeorological Bulletins in Barbados in October 2001, participants recommended that a dedicated web server be developed for distributing agrometeorological products from WMO members. Subsequently, an Expert Group Meeting on Internet Applications for Agrometeorological Products was held in Washington, DC, during May 2002, to discuss the practical steps needed to develop this web server. Discussions from this meeting led to the development of the World AgroMeteorological Information Service (WAMIS). WAMIS is a dedicated web server on which countries and organisations can place their agrometeorological bulletins and advisories. Provision of such a central location for agrometeorological information enables users to quickly and easily evaluate various bulletins and gain insight into improving their own bulletins. Also, these bulletins represent the expert knowledge of the individual countries and provide the possibility to assess extreme events and disasters in a historical perspective, especially when an archive of bulletins is present. Placement of agrometeorological bulletins on WAMIS also increases the visibility of the National Meteorological/Hydrological Services (NMHS). In March 2005, a tools and resources section was added to WAMIS to provide users with additional papers, links to software tools, Internet links, and other resources to help improving their agrometeorological bulletins and advisories and services.

Two models were used to assess the effects of coastal characteristics on radar propagation in ducting conditions in the Persian Gulf. The NCAR/Penn State MM5 model simulated atmospheric conditions at a 5-km horizontal spatial and hourly temporal resolution on a day on which observations of ducts existed. The output from this model was input to the AREPS propagation model to produce radar coverage over coastal areas. Four factors influenced radar propagation: the sea breeze; coastal configuration; orography; and ambient wind. The sea breeze alone allowed propagation to extend about 100 km inland in a layer 200 m deep. When the breeze was aided by a following ambient wind the propagation layer extended for 150 km and was 400 m deep. A coastal indentation caused differences in depth and intensity of propagation over a distance of about 30 km parallel to the coast in which the indentation occurred. Steep near-coastal orography blocked radar propagation.

The skill of seasonal mean precipitation and temperature predictions by multi-model ensemble (MME) schemes has been examined using hindcast data from the APEC Climate Network (APCN). The hindcast data of the member models have been collected from several institutions in the APEC region. Skill metrics, namely, anomaly correlation coefficient (ACC), Hanssen-Kuipers skill (HKS), and economic value were used to assess the APCN MME predictions. The member models have a range of skills in predicting the seasonal mean conditions: however, the MME schemes consistently produce improved predictions. Though the weighted multi-model ensemble schemes have better skill scores in terms of ACC, the unbiased multi-model ensemble mean provides better and more useful categorical predictions as seen from the HKS score and the economic values. Prediction skills show distinct seasonality in different regions. Precipitation prediction skill is the lowest in the Asian monsoon region in June-July-August. The APCN MME predictions have positive economic values in most regions and examination of monthly values suggests that the MME predictions are reasonably good and useful even with three-month lead times. An uncalibrated multi-model probabilistic prediction scheme gives an indication of threshold probability for which the seasonal predictions are more beneficial to users. The extreme climatic conditions are predicted well by the MME schemes. However, user sectors for which the extreme climate predictions are beneficial are different to those of regular three-category predictions. These results can be used in planning and preparedness for the extremes of climate in the future, and when developing economic decision support models.

The development of solar energy as a power source in the next few years requires reliable estimation of available solar energy resources. At local scales, topography is the most important factor in determining the distribution of solar radiation at the surface. Interpolation techniques are usually employed to estimate solar radiation where stations are not available, but their usefulness is limited where topography is an important source of variability. The use of satellite data and more recently of models based on techniques GIS, have contributed to solve this difficulty. In this work the usefulness of a digital elevation model (DEM) in providing topographic information for the estimation of solar radiation in areas of complex topography is analysed. Daily global radiation values were generated using the Solar Analyst software, which uses topographic information to generate radiation data. The generated data were compared with the experimental data obtained from 14 radiometric stations located within the Sierra Nevada Natural Park (southern Spain), an area of complex topography. Results show the usefulness of the topographic information derived from a DEM to estimate the solar radiation in areas of complex topography. Nevertheless, results depend on the DEM resolution and it is important that other factors, such as the albedo, should also be taken into account to obtain better estimates.

The basic condition for using precipitation data from raingauges and radars is data quality control. This aspect is important for comparing and using rainfall data, for example in models. In the scope of the EU-project VOLTAIRE (Validation of multisensors precipitation fields and numerical modelling in Mediterranean test sites) rain data from Cyprus have been analysed. Different quality control methods have been applied to the rainfall data of 158 raingauges and the data of 11 events (in 2002 and 2003) of the C-Band radar in Kykkos. The first results of the use of ground clutter algorithms for radar data in Cyprus are presented in the paper.

Food insecurity represents one of the main indicators of the poverty level of a country and can breed a strong dependence on foreign aid with a strong slowdown of the national economy. African arid and semi-arid areas are characterised by food precariousness and their agricultural activity is strongly dependent on the extreme climatic conditions that affect the available water supply. This situation has led to the need to develop new tools for the prediction and the management of crisis. The characterisation of climate and the identification of anomalies, the monitoring of weather conditions and their influence on crops, short and medium range weather forecasts and long-term climate predictions are among the most powerful tools to predict, in as short a time as possible, crises due to the absence or delay of rainfall season. Meteorological information can also be used as an input to agrometeorological models applied for crisis prevention or for its management during the growing season when the crops are already present. In this paper, a review of these tools is made and some operational products presented.

Spanish building legislation has recently changed and now requires an updated and restructured Technical Building Code which is in accordance with European directives. The norm contained in this Code is based on studies of extreme values for climatic elements such as temperature, precipitation and wind. Revised maps of extreme values for climatic elements with a 50-year recurrence interval are required. Here, extreme maximum and minimum temperature maps for Spain are evaluated and mapped by means of geographical information technology. The data are extracted from the historical database held by the Spanish Meteorological Institute. Daily extreme temperatures from 1,181 stations with records going back more than 30 years have been used. The maximum and minimum temperatures are determined as 50-year mean recurrence interval values. To obtain these values, a Gumbel distribution is fitted to the extreme annual values extracted from the database. Spatial interpolation in a regular 5 km × 5 km grid of the annual maximum temperature is made by ordinary kriging. Meanwhile, for the annual minimum temperature a residual kriging has been applied due to its strong dependence on altitude.

The moist convective instability over the Arabian Sea during the Arabian Sea Monsoon Experiment 2002 is studied using high resolution radiosonde data over the Arabian Sea off the west coast of India and inland at Goa (15°21′N, 73°51′E). The vertical structure of the lower troposphere is discussed in terms of the profiles of conserved variables and the time series of convective available potential energy (CAPE), mixed-layer depth, lifting condensation level and level of free convection. Analysis shows that the Arabian Sea within 200 km of the west coast of India is characteristically a low-CAPE region irrespective of whether it is a good or bad monsoon season. Little or no variability in the mean (0–500 hPa) lapse rate during July and August indicates the quasi-equilibrium state of the tropical atmosphere during the summer monsoon with an approximate balance between the convective and large-scale forces. Large-scale forcing for the ascent of surface air seems to be of prime importance for triggering deep convection and rainfall over the East Arabian Sea. Case studies of precipitating convection over inland at Goa during the monsoon indicate a moistening and warming of the lower troposphere following the convection.

A physically-based 1-D heat and mass transfer model was tested to estimate the heat fluxes of an asphalt surface. The model was run for two summer months for a road test site in southwest Sweden. Parameters for thermal properties, surface runoff, radiation and turbulent transfer were obtained from a description of the road stratification and from the literature. Coefficients of determination (r2) 0.94, 0.93 and 0.97 were obtained when simulated results were compared with observations of net radiation, heat flow below the surface and surface temperature respectively, all with slope coefficients close to unity. In addition, simulation results elucidated the role of water vapour transport through the asphalt-soil profile and its effect on the latent heat flow from the surface. Problems were identified with closure of the heat balance in measurements based on discrepancy between simulated and observed sensible heat flux.

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Paraná river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Agroclimatology represents the link between several agrometeorological fields and climatological applications. The German Weather Service (DWD) uses data from many meteorological stations for 40-year calculations by agrometeorological models. The topics extend from microclimatic elements over the wide field of soil conditions and water content, agricultural operations and product quality to forest fires and plant pests. Analyses of agroclimatic output allow for better planning, risk assessment, objective evaluation of the current situation and even for a test of the models used. Current applications concern crop microclimate, including soil, evapotranspiration and volatilisation, plant protection, phenology and product quality, with examples such as the frequency of extreme soil frost, optimum harvest conditions, plant water stress on different soils, and the frequency of high pest incidence. Not only singular places, but regions too can be evaluated. Results from combinations of elements, such as soil frozen with high water content, are considered valuable for recommendations to agricultural users. Agroclimatic model information concerning, for example, removal of plastic cover from spring crops, soil conditions for plant water use, tractability, herbicide effectiveness, germination, nutrient washout, and harvest, is directly usable by farmers. The information about forest fire index, extreme season assessment (for compensation payments), and trend analyses is of practical use for administration. The use of risk levels for plant pathogens is important both for farmers and extension services. The future importance of agroclimatic applications is considered high.

Springtime meteorological and satellite data from 1971 to 2000 have been used to assess surface desertification and grassland deterioration to obtain a better understanding of the synoptic characteristics of dust storms observed in Inner Mongolia, the relationship between dust storms and their impact on the Beijing-Tianjin Region, and the effect of climatic change on the surface environment of dust storm source areas in Inner Mongolia. Based on the statistics and the analysis of weather charts during each dust storm event, details and characteristics of sand and dust source regions, such as cold air paths, areas of strong winds and typical synoptic types were obtained. Temperature was found to be the most significant indicator of climate change in the dust storm source regions, having increased by 1.01 °C in the Alxa source region and by 0.96 °C in Hunshdak over the past three decades. Other factors include lack of precipitation, higher evaporation, lower relative humidity, soil desertification and decrease of grasslands in the source regions. As a consequence, nearly two-thirds of the very strong dust storm events observed in Inner Mongolia (characterised by horizontal visibility below 1000 m and wind speeds of 12 m s−1 or over) led to suspended dust or dust storms in the downwind area, the Beijing-Tianjin Region. This study also analyses the upper air and surface weather charts of 58 very strong dust storm events and established the direction of three cold air paths: northwesterly, westerly and northerly. The northwesterly cold air path is seen as the major one, responsible for 58.6% of the dust storm episodes. In addition, the observed areas where strong winds occur lie mostly across the central-west and central regions of Inner Mongolia. Finally, the synoptic situations associated with dust storm events can be classified into three types: trough, northwesterly current and Mongolian vortex. The predominant synoptic system is the trough, responsible for 63.8% of the 58 events analysed in this study.

Radar rainfall nowcasting at short lead times has important hydrometeorological applications in the fields of weather prediction and flood forecasting. The predictability of rainfall events can vary significantly with scale as smaller storm features are less predictable than the storm envelope motion. As a result, various techniques have been developed for filtering a radar image and deriving short-term forecasts from the more predictable, larger storm scales. In this study, the effects of image filtering on radar nowcasting performance using the Storm Tracker Nowcasting Model (STNM) are evaluated. Radar rainfall nowcasts are evaluated for three storms exhibiting varying degrees of organisation over the Arkansas-Red River basin. In each case, it is found that the nowcast skill decreases with the forecast lead time, increases with the verification area used around a forecast location, and decreases with higher rainfall thresholds. Furthermore, it is demonstrated that a set of properly tuned filtering nowcasts are superior to simple ‘persistence’ and slightly better than ‘uniform advection’. At the scale of a large hydrologic basin (∼ 6000 km2), filter-based nowcasting is shown to capture the temporal variation in rainfall amount and its spatial distribution based on a set of catchment-based metrics. Finally, a method for relating changes in nowcasting skill to errors associated with storm dynamics not captured by image filtering techniques is evaluated.