Convective precipitation events in northern Italy during 1996 and 1997 are analysed using two infrared-based geosynchronous satellite rainfall estimation methods to verify the level of applicability of the techniques for operational applications in the area, their quantitative results, and relative performances. The Negri–Adler–Wetzel (NAW) and the convective stratiform technique (CST) are applied to METEOSAT's thermal infrared (IR) data. C-band radar reflectivity fields detail the vertical and horizontal structure of the cloud systems, and radar rainfall data are retrieved. Satellite rain areas are checked against simultaneous radar rainfall retrievals through a contingency analysis procedure. A semi-quantitative analysis is presented. Positive brightness temperature differences between water vapour and thermal IR channels are also examined and related to the storms' development stage and rainrate. Results show that NAW and CST perform reasonably in delimiting rain areas during active convection and care should be used in the initial and final development stage when statistical parameters lose most of their significance. NAW tends to overestimate rainfall while CST approaches more closely radar measurements. Most common errors arise from considering only portions of the storm, contamination from cold non-precipitating cloud, and merging of two or more cloud masses of independent origin. Operational applications, though not completely quantitative, are also possible, including positive values of the difference between water vapour and IR brightness temperature.

The risk of dam failure in India, coupled with better analysis of extreme storm events, has led to the estimation of revised one-day probable maximum precipitation (PMP). For some water conservation projects where the catchment area is very large, there is a need to have estimates of PMP for durations of both two and three days. These are provided in this paper. It was found that the two-day PMP over the Indian region varied from 100 cm over the central peninsula to over 290 cm in eastern India. For a duration of three days the corresponding values are 120 cm and 370 cm. For water management projects which include impounding reservoirs, an estimate has been made of the probable maximum flood (PMF) based on the new PMP estimates. The results of the design flood estimates were compared with existing estimates at the same sites and in most cases were found to be considerably higher. A comparison of the revised estimates of PMF and recorded floods in India shows that the trend of the rates of runoff in relation to catchment area differ by an average of 38% for an area of 1000 km2 and 46% for an area of 10 000 km2. These results show that some dams in India may be unsafe in the event of the probable maximum flood.

A method for classifying different types of slipperiness on roads in Sweden is described. Using this method it is possible to survey road conditions in different areas and between different years to optimise winter road maintenance. Winter road maintenance in Sweden is generally undertaken by the national road administration to improve winter-time road conditions, thereby keeping up the traffic flow and decreasing the accident rate. As a number of different types of slipperiness may develop on roads in winter, each due to a specific set of meteorological variables, maintenance work can be a complicated task. With the proposed classification method it becomes easier for the winter maintenance personnel to analyse information on road conditions and survey the distribution of road slipperiness in a region. The classification is performed with an expert system using meteorological data from the Swedish Road Weather Information System. The road condition is classified as good or as one out of ten different types of slipperiness on roads. Road conditions during three different winter periods are analysed. The results show that variations in climate produce substantial differences in annual road condition characteristics. The output from the expert system classifying road slipperiness is compared with recorded winter road maintenance reports. Maintenance action took place on 49% of all occasions when road conditions were classified as slippery.

During 1994/95 a UK Overseas Development Administration (now Department for International Development - DFID) funded project was undertaken to predict ozone amount over Punta Arenas in southern Chile. A low-cost satellite receiver was installed to receive the digital data stream from the NOAA series of weather satellites. The ozone channel of the TIROS Operational Vertical Sounder instrument on the NOAA satellites was used to obtain maps of stratospheric ozone for the area around Punta Arenas in near real time. Also, a simple model was developed to predict the amount of column ozone from the forecast 100 hPa temperatures obtained from the UK Meteorological Office. These techniques used together made it possible to issue timely warnings to the general public of Punta Arenas before the Antarctic ozone hole moved across southern South America during the Austral spring.

The structure and track of monsoon depressions over India during the summer monsoon have been simulated using a double-nested limited-area numerical weather prediction model. Four distinct cases of monsoon depressions that formed over the Bay of Bengal and adjoining areas have been studied. Initial conditions for the simulations are from either the European Centre for Medium Range Weather Forecasts, Reading, UK or the National Centre for Medium Range Weather Forecasting, New Delhi, India. The model is integrated for up to 48 hours for each case and the results are compared with verification fields. Forecasts of mean sea level pressure and low-level wind indicate that the location of the centres of the depressions and their track could be predicted satisfactorily even though the magnitude of the central pressure is slightly too high. Temperature forecasts show close agreement with the verification analyses and the distribution of precipitation is well simulated. The vertical cross-sections of temperature and wind forecasts show the correct vertical structure. RMS errors of the mean sea level pressure, wind and temperature indicate that the model could simulate the large-scale fields reasonably well. RMS errors of the tracks of the depressions confirm the fact that the high-resolution nested grid model can predict the tracks of the depressions with reasonable accuracy.

A ground-based network of Global Positioning System (GPS) receivers can be used to determine the excess propagation path of the GPS signals. Using surface pressure data it is possible to derive the excess path caused by water vapour. This quantity is often referred to as the wet delay. We have studied the relationship between the wet delay and the integrated precipitable water vapour (IPWV), based on more than 120 000 radiosonde profiles from 38 sites in Europe. We have studied four different models based on the surface temperature, the latitude of the site, and the time of the year in different combinations. The accuracies of the models have been evaluated and also compared with the accuracy resulting from the use of published model parameters derived from radiosonde data acquired in the United States. The models using the surface temperature data yielded the smallest relative root mean square errors of 1.14% and 1.15% respectively. We find that the accuracy can sometimes be further improved, typically down to 1.02%, by adjusting the values of the model parameters to the area or site of interest.

A method has been developed for retrieval of wind velocity profiles from satellite sounder radiances using a neural network technique. Each wind velocity represents a mean value for a layer approximately centred at that standard height or pressure level. The neural network input vector includes sounder radiances from the Geostationary Operational Environmental Satellite (GOES), plus ancillary information such as latitude and longitude. A set of ‘co-incident’ rawinsonde soundings provide the ‘truth’ data set. In the initial results root mean square errors for the standard levels from 850 to 300 hPa have about the same magnitude as those for current methods such as tracking of moisture features in GOES imagery. As with more traditional methods, the largest root mean square error occurs near the tropopause. Although the results to date appear highly promising, considerable further work is needed before we have an operationally capable technique.

The aim of this work is to present a methodology to downscale weather forecasts (i.e. to give regional-scale forecasts based on synoptic-scale forecasts from a numerical model). This methodology consists of the regionalisation of a large zone into thermally homogeneous meteorological regions followed by a study of how different types of weather affect them. Once the meteorological behaviour of each region is determined, cokriging is used to predict its temperature knowing the current temperature and type of weather expected (i.e. based on forecasts from a numerical model). Thus, from a methodological point of view, it is shown that geostatistical prediction techniques can be used in the meteorological sciences by combining classical multivariate statistics and space-time prediction techniques. To illustrate this methodology, the maximum and minimum temperatures at 54 observatories in Catalonia in winter and synoptic data from Barcelona's airport have been used. The method proposed in this paper, which represents an improvement in temperature prediction compared to that given by classical statistical methods, allows the forecasting of temperature. This case study shows that forecasts can be easily downscaled and that, to produce daily forecasts, just a pocket calculator is needed. The results are presented for a one-day forecast, but this technique can be applied to forecasts over a four-day period.

Camelot is a database system designed for output from the climate model run by the Hadley Centre of the UK Meteorological Office. It contains metadata for climate model output held in a tape archive and on the workstation system used for analysis. The system is required because of the large and rapidly growing volume of archive data and the great variety of uses of the climate model. Camelot enables the user to find out about what has been archived and to retrieve data selectively to the workstations with specifications given in terms of metadata rather than files, without recopying data already held online. The PV-WAVE analysis environment used on the workstation system also allows the user to access the data by reference to its metadata.