Road-surface cooling during three contrasting frontal passages was examined for various sites with widely varying exposure across Devon. Delays in road cooling were dependent on exposure, topography, thermal inertia, precipitation and surface wetness. Scattered cloud and cloud bands complicate the spatial variability, but for winter maintenance, worst-case scenarios of complete cloud clearance produce important guidelines for prioritising salting routes.

Over the last decade, research has greatly increased into the use of Geographical Information Systems (GIS) in a variety of applications that involve the processing of climatological and meteorological data. This upsurge of interest is related directly to the fall in price of ‘commercial off-the-shelf’ GIS products together with large advances in computer processing ability. Add to this the proliferation of the Internet and the result is a set of fast real-time bespoke solutions and visualisations for many national meterological services and end-users.

Hydrometric information is fundamental to the planning, operation and management of water resources and flood defence. The data network is greatly valued by those who use it, yet assignment of a monetary or economic value is problematic. Data managers are often frustrated by the need to continue to justify a network. The paper focuses on the uses of data within the Environment Agency of the UK, explores the difficulties in assigning a value to the data and attempts to review the value ascribed to such data by drawing on some UK and wider examples.

Despite considerable improvements in numerical models in recent years, weather events associated with extratropical cyclones continue to present forecasters with a challenge. For most cyclogenesis events model forecasts display, at the least, subtle errors in track and intensity of the system. Occasionally, errors can be significant - a depression can deepen suddenly and unexpectedly, with major implications for wind strength as well as other weather elements. Many of the weather events within extratropical cyclones are essentially mesoscale and, on occasions, a single cyclone can be associated with several simultaneous ‘severe’ events such as gales, heavy rain, snow and thunderstorms. This paper describes how the forecaster can add value to numerical model products by using information from a wide variety of sources such as satellite and radar imagery, as well as by monitoring hourly surface observations. Examples are shown in which the forecaster has to make adjustments to model predictions, reconcile conflicting model guidance, and add the all-important mesoscale detail.

Four satellite rain estimations based on microwave (MW), infrared (IR) or combined MW-IR techniques are compared with the BOlogna Limited Area Model (BOLAM) rain forecast for a severe weather event (8–13 November 2001) over the western Mediterranean Sea.

Two of the investigated multi-channel MW rainfall algorithms use data from the Tropical Rainfall Measuring Mission (TRMM). The Frequency Difference Algorithm relies on data from the TRMM Microwave Imager (TMI) and the other one combines data from the Precipitation Radar (PR) with those from the nine-channel radiometer TMI, called PR Adjusted TMI Estimations of Rainfall (PATER) algorithm. The pure IR Rain Estimator uses geostationary IR METEOSAT data and the combined Naval Research Laboratory algorithm uses both MW data from low orbiting satellites and IR data from the geostationary orbit.

Validation results, computed over a common grid, which is independent of the different field of view sizes of the applied data sets, indicate that there is generally a better performance for heavy rain (> 6 mm h−1) than for light rain (<1 mm h−1). Both MW algorithms perform rather similarly, although PATER shows some rain detection problems due to thick aerosol loads originating from the desert. The BOLAM model presents a good agreement with the MW and only a minor location error of a heavy rain area was detected. Both IR-based algorithms have problems in identifying the correct rainy areas compared to MW. Overall, the results suggest that there are advantages in combining both techniques – the well-known rain physics of the MW channels with the high temporal resolution of IR algorithms – to retrieve precipitation from satellite data.

The south-west monsoon (June–September) is the major rainy season over India. Information about the occurrence of precipitation and the expected quantity at a specific place is important in many sectors of human activity. In this study, objective methods are developed to forecast the probability of precipitation (POP) and provide the quantity of precipitation forecast (QPF) over Delhi. As the onset of the monsoon at Delhi is around 30 June, the models are developed for the months of July, August and September (JAS) using surface and upper-air data for the period 1985–90 and tested with data from JAS for 1994 and 1995. A multiple linear regression equation is developed to forecast the POP and multiple discriminant analysis is used to produce the QPF in terms of one of four groups (0.1–1.0; 1.1–10.0; 10.1–30.0; and ≥ 30.1 mm). The QPF model is used only if precipitation is expected to occur (the POP forecast is turned into a categorical forecast). The categorical forecasts based on the POP exhibit positive skill scores consistently with both the development and independent data sets. The model for QPF also performed satisfactorily

An acoustic array antenna which has a narrow main lobe and suppressed sidelobes is studied. The directivity function formed by the multiple point sources is used to estimate the aperture of an array and the spacing between the transducer elements to make a desired beam. Then the aperture tapering based on a Chebyshev distribution and the effect of an acoustic shield are evaluated to suppress the sidelobe radiation. The total performance of the phased array system has been examined by comparison with tower instruments. The field test shows that the measured beam pattern agrees with the calculated one and the sodar-derived winds have a correlation of 0.96 and a standard deviation of differences of about 0.47-0.52 m s-1 compared with in situ measurements. These statistical values are as good as or even better than those from the previous experiment.

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.

A knowledge of minimum temperature during winter and maximum temperature during summer is a very useful for individuals, as well as for organisations whose workers and machines have to operate in the open, e.g. the armed forces, railways, roadways, tourism, etc. Accurate forecasts of minimum temperature during winter help in the prediction of cold-wave conditions and those of maximum temperature during summer help in the prediction of heat-wave conditions over northern India. Models for forecasting the minimum temperature during December and the maximum temperature during May at Delhi have been developed using surface and upper-air meteorological data from 1984-89. The results of testing the models on independent data from recent years (1994-95) are presented. The results are encouraging and more than 80% of the forecasts are correct within ±2 °C. Possible reasons for large deviations are also investigated.

On 16 May 2000, a low pressure system produced very intense winds over the Río de la Plata (RP) river, giving rise to floods within the region. In order to study this event, the synoptic evolution of the cyclone of 14-17 May 2000 is documented using conventional observations and the NCEP analysed data sets. The synoptic-dynamic overview reveals that the system starts as a usual cyclogenesis on the Chilean coast. The low level cyclone favours a convectively unstable environment within the central Argentinean region triggering convective storms and, consequently, a large amount of latent-heat release. The latent heat and the vorticity advection both play an important role in determining the depth of the system and its movement. Another unusual feature of this event is the north-eastward displacement of the cyclone, which is attributed to the interaction with other low-pressure systems located in the South Atlantic Ocean.

The purpose of this paper is to show how already existing Doppler radar information, provided, for example, by the German Weather Service's (DWD) radar network, can be combined to wind vector fields using multiple-Doppler techniques. At the same time, an alternative to monostatic multiple-Doppler measurements, called a bistatic Doppler radar network, is introduced to measure wind velocity within areas which are not covered by the multiple-Doppler technique or where wind fields close to the earth's surface are required. Advantages, disadvantages, principles and measurement characteristics of the two systems are specified. Additionally, a recommendation is given to arrange each instrument optimally within the network in order to measure wind velocities close to the ground over large areas with high spatial resolution and low error variance. Both systems are applicable either to derive wind fields throughout weather radar networks as shown using the DWD weather radar network, or to act as the basis for weather warning and advisory systems in operation, for instance, at Frankfurt/Main International Airport. Concepts for both applications are given.

Hailfall is one of the most dangerous and damaging phenomena associated with severe thunderstorms. Its remote identification is still under investigation, especially by means of polarimetric radar data. The need to use polarimetric measurements is a limit to operational implementation of the techniques developed up till now. The routine sampling of radar allows the computation of the differential hail signal HDR (Aydin et al., 1986) every 15 minutes. The assessment of the HDR is performed by comparison with the ground truth from a hailpad network. The spatial resolution of this network is quite coarse (one pad per 16 km2) for the collection of all the hail shafts, as they generally have a small horizontal extent and are short lived. Such bounds have been considered in the analysis of four large hailfalls recorded in 1997. This study reveals also the limitation on radar hail detection arising from the frequent occurrence of strong differential attenuation. Nevertheless, to identify regions where a sharp rain-hail transition occurs, polarimetric measurements work much better than algorithms based only on horizontal reflectivity. Results show that use of HDR gives a high probability of correct hail detection as well as an acceptable number of false alarms.

A wetness duration experiment was carried out within a lily field situated adjacent to coastal dunes in the Netherlands. A within-canopy model was applied to simulate leaf wetness in three layers, with equal leaf area indices, within the canopy. This simulation model is an extension of an existing model. It appeared that in most cases leaf wetness started in the uppermost layer followed by the middle and bottom layer, respectively. The same occurred during the early morning drying process. Just after sunrise the upper layer started to dry, followed by the middle and bottom layer, respectively. The longest leaf wetness duration occurred in the bottom layer. The calculated leaf wetness durations were within 10 minutes of the results obtained using a leaf wetness sensor.

Precipitation forecasting experiments have been carried out with HIRLAM, a numerical weather prediction model. The model has been run with three different horizontal gridlengths: 22, 11 and 5.5 km. An attempt has been made to estimate the appropriate magnitude of the horizontal diffusion, which is used to control small-scale noise in the model, by looking at kinetic energy spectra. It is shown that, for the higher resolutions, smoothing the orography gives smoother precipitation patterns. The small-scale precipitation resulting from runs with the original orography has negligible extra skill compared to the smoothed orography runs. The results show that the model is able to forecast good precipitation amounts, even with 22 km gridlengths. No significant improvements occur when the higher horizontal resolutions are used. Experiments have been performed using the tendencies of the physical parameterisations computed on a coarser grid than that of the dynamics. The resulting precipitation patterns are very similar and this indicates a more economical way of integrating the model.

The significance of the atmospheric stability approach in models for estimating evapotranspiration is studied by considering the errors encountered when simply adopting neutral instead of actual stability conditions. Maize, cotton and wheat crops grown in 10 hectare fields (central Greece) were used to estimate actual and maximum evapotranspiration for both stability considerations, employing a model based on the equations of Shuttleworth & Wallace (1985). The required meteorological data were recorded in each field continuously, whereas the leaf area index and the plant height were measured weekly. Results show that when estimation of cumulative evapotranspiration losses is attempted, the choice of the atmospheric stability approach is of no importance (error ≤ 2%), as over- and under-estimation of evapotranspiration are counterbalanced when stable and unstable situations are (under the climatic conditions of experimentation) successively interchanged during the cultivating period. When, however, daily estimations are required, the actual stability conditions have to be taken into account, as the daily evapotranspiration rate estimated by regarding the atmospheric stability as neutral may deviate from the corresponding ‘actual’ rate by as much as 50 – 80%.

Clear air turbulence (CAT) at high altitude remains a hazard to aviation which can result in passenger injury and aircraft damage. Two limited surveys of CAT events over South Africa, 1993-1995 (inclusive) and 1998, are used to illustrate the most likely synoptic conditions under which CAT can be expected. A case study of CAT associated with an upper-air trough and a mountain wave is presented. The study also evaluates the effectiveness of the Ellrod Turbulence Index (ETI) derived from model data provided by the UK Met. Office. A forecast of ETI derived from the Global Spectral Model of the United States National Center for Environmental Prediction (NCEP) is also reviewed.

Correlation between weather radar reflectivity and precipitation data collected by rain gauges allows empirical formulae to be obtained that can be used to create continuous rainfall surfaces from discrete data. Such surfaces are useful in distributed hydrologic modelling and early warning systems in flood management. Because of the spatial relationship between rain gauge locations and radar coverage area, GIS provides the basis for data analysis and manipulation. A database of 82 radar stations and more than 1500 rain gauges in continental USA was compiled and used for the continuous downloading of radar images and rain data. Image sequences corresponding to rain events were extracted for two randomly selected radar stations in South and North Carolina. Rainfall data from multiple gauges within the radar zone of 124 nautical miles (nm) (∼230 km) were extracted and combined with corresponding reflectivity values for each time interval of the selected rain event. Data were normalised to one-hour intervals and then statistical analysis was applied to study the potential correlation. Results of regression analysis showed a significant correlation between rain gauge data and radar reflectivity values and allowed derivation of empirical formulae.

An operational system for diagnosing probability of convective development and structure from NWP model outputs over the British Isles has been developed in the Met Office. The system originated as a research and development project and so for historical reasons is now known as the Convection Diagnosis Project (CDP). The system takes account of expected model errors in temperature and humidity and allows for forcing from sub-grid scale mechanisms on a 1-km scale that are important for mid-latitude convection. A NWP convection scheme is used to determine grid-mean parameters from a range of forcings according to probability of occurrence. The grid-mean values are then used in a simple and variable object-oriented life-cycle model of a convective cloud to diagnose shower structure and expected movement. Using the life-cycle model, a range of probabilistic forecast outputs are produced four times a day from T+6 to T+36 hours ahead for individual grid squares and for larger pre-defined regions. Case studies and rigorous objective verification, making use of an automatic method for identifying convective cloud from radar and satellite data, have shown that the CDP outputs complement the standard NWP outputs and can provide additional useful information in a forecasting environment.

A superensemble forecast that consists of six operational general circulation models is tested to simulate weather parameters over the equatorial South-East Asian region. The forecast technique, developed at Florida State University, is a unique ensemble that has proved to be one of the better prognosticators for weather forecasting. In this paper, the root mean square errors of the superensemble precipitation for a case of a severe tropical rainfall event during the winter monsoon of 2001 were smaller than the other global operational member or ensemble models used, indicating a skilled forecast. This trait is also reflected in the equitable threat score which indicates the association of the common rainfall threshold distribution between the forecast and observed best rainfall analysis from the Tropical Rainfall Measurement Mission (TRMM) and Special Sensor Microwave Imager (SSM/I) dataset, where although it is slightly underpredicted for light rain thresholds, its results are still superior to other individual member or even the ensemble mean forecasts. Higher correlation between the superensemble forecast and the observed is maintained for up to 3-day forecasts, which indicates its potential as a reliable and good forecast tool for predicting equatorial precipitation.

In this paper we review work undertaken on sea clutter illustrated by previously unpublished observations made with an S-band radar during a research project in South Wales carried out during the winter of 1976/77. The implications for detection with C-band Doppler radar are considered.