In this study, profiles of temperature and humidity (<250 cm above the road and 5 m into the surroundings) have been used to examine the development of temperature differences in the air layer close to the road. Temperature, humidity and wind profiles were measured, together with net radiation and observations of road surface state, at a test site at Road 45, Surte, Sweden. Measured temperature differences were compared with present weather, preceding weather, surface status, wind direction and other parameters thought to be important for the development of temperature differences. The results showed that large temperature differences (1-3 °C between 250 cm and 10 cm above the road) occurred when there was a high risk of slipperiness caused by hoarfrost, snow or ice on the road. The temperature differences between different levels were associated with the exchange of humidity and temperature between the air layer and the road surface. The 10 cm level reflected the surface processes well. Higher levels were influenced by the surroundings because of turbulence and advection. This study emphasises the need for measurements to be taken at a height and place that reflects the processes at the road surface.

Radar reflectivity information is often displayed in two dimensions, making it difficult to extract the structural characteristics of convective storms. The maximum radar reflectivity and the vertical profile of liquid water distribution in a vertical column of a convective cell is used to determine a structural and intensity classification of the cell. The application of this Storm-Structure-Severity classification is demonstrated by investigating a tornado event on 15 November 1998 in Harrismith, South Africa, by using volume-scanned reflectivity data from the S-band MRL-5 radar. The regions of convective development, decay and mesocyclone evolution are identified with this classification. The method is able to extract the three-dimensional structural information of convective storms embedded in volume-scanned reflectivity data and present it in a simple display format.

Anomalous propagation (anaprop), analogous to the upper mirage in the visual wavelengths, is still a major problem in radar meteorology. This phenomenon assumes particular importance in automatic recognition and estimation of rainfall. Anaprop echoes from terrain features such as hills and coasts often give echoes up to 50-60 dBZ, equivalent to heavy rain or hail in severe thunderstorms. Anaprop echoes from sea waves may be comparable in strength to those from moderate precipitation and also form similar patterns. Based on the evidence that the vertical reflectivity profile of precipitation is quite different from the anaprop profile, two methods for anaprop identification are presented. The method proposed by the Servizio Meteorologico Regionale (SMR, Italy) simply uses the operational scan procedure to discriminate between precipitation and anaprop. At the Swedish Meteorological and Hydrological Institute an ‘ad hoc’ scan strategy has been developed in order to obtain much more detail of the lowest reflectivity profile. A number of statistical parameters have been used to achieve a better discrimination between precipitation, land and sea clutter. A number of case studies, representing different echo intensities and patterns, and including a case of anaprop with embedded precipitation, are presented to assess the impact of these methods.

Three three-dimensional trajectory models (LAGRANTO, TRAJKS and FLEXTRA), all driven with analysis wind fields from the European Centre for Medium-Range Weather Forecasts, are intercompared. The comparison has three parts: first, a case study of strong ascent in a warm conveyor belt is performed; second, a large set of back trajectories from the tropopause region over Europe and the mid-latitude Atlantic Ocean is investigated; third, a set of low-level trajectories is compared. The intercomparison shows that all three models have been implemented correctly. The degree of model accordance depends on the interpolation methods used. Deviations between the results from a single model using different interpolation schemes are of the same magnitude as the deviations of different models. If all models use linear spatial interpolation, their respective trajectories closely agree with each other, with deviations of 2% or less for the average distance between the starting and the ending positions in the free atmosphere after 48 h. Close to the surface, where the differences in the model formulations are largest, average horizontal position deviations may be up to 10%. Compared with other sources of errors, such as inaccuracies in the wind fields or insufficient temporal and spatial resolution of the data set, these differences are much smaller. Non-linear spatial interpolation leads to stronger vertical motions than linear interpolation and, in the case study, enhanced the quality of the results.

The object of this study is to examine the possibility of developing a model that can determine road surface adjustment times to new conditions after weather changes. Situations where the weather changes from clear to overcast conditions are studied in order to produce a worst-case scenario. One hypothesis tested is that the decrease in road surface temperature (RST) differences between shaded and sun-exposed stations on clear days can be used to determine the reduction in RST difference on days with weather changes. A relationship that describes the variation in RST difference as a function of time of day and season is developed. For clear days a fourth-order polynomial can describe the relationship between RST differences and time of day. The coefficient in the polynomial depends on maximal solar elevation. Validation of the model for clear days showed that the deviation between modelled and measured values varied between 0.3 and 0.5 °C. It was also possible to use the clear day relationship to predict the decrease in RST differences for situations with weather changes, but during these situations the deviations between modelled and measured values were higher and also tended to show a positive bias.

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

The breakpoint format for rainfall data, by recording the rainrate and the times when the rainrate changes, consists of variable length periods during which rain of constant intensity fell. It is compact and retains a level of detail equivalent to that found in high temporal resolution data. However, breakpoint data are available only at the relatively few locations where automatic raingauges produce pluviographs. On the other hand, radar observations of rainfall provide precipitation data at a high spatial density but at poor time resolution. Advantage can be taken of this high spatial density to give more locations where breakpoints are available through interpolation between the radar images to provide precipitation data at sufficient temporal resolution for the extraction of breakpoints. The interpolation scheme, based on the PARAPLUIE method, detects the movement and evolution of precipitating echoes to provide additional interpolated radar images. Breakpoints, extracted at pixels of the images that contain raingauges, showed similar overall accumulations and characteristics when compared with those of the collocated raingauges. The similarity is particularly so for gauges within a 50 km radius of the radar where the spatial resolution of the radar and the temporal resolution of the raingauges are equivalent.

The World Health Organisation (WHO) and the World Meteorological Organisation (WMO) have jointly recommended that the UV Index (UVI) should be used to inform the public about possible health risks due to overexposure to solar radiation, especially skin damage. To test the current operational status of measuring and modelling techniques used in providing the public with UVI information, this article compares cloudless sky UVIs (measured using five instruments at four locations with different latitudes and climate) with the results of 13 models used in UVI forecasting schemes. For the models, only location, total ozone and solar zenith angle were provided as input parameters. In many cases the agreement is acceptable, i.e. less than 0.5 UVI. Larger differences may originate from instrumental errors and shortcomings in the models and their input parameters. A possible explanation for the differences between models is the treatment of the unknown input parameters, especially aerosols.

The Met. Office currently operates a high resolution mesoscale model that provides guidance on the likely formation of radiation fog on the spatial scale of 30-40 km. A project has been under way to build a Site-Specific Forecast Model (SSFM) using a one-dimensional version of the Met. Office Unified Model as a basis, with suitable modifications to its surface treatment driven by output from three-dimensional NWP models. The SSFM has been applied to the forecasting of radiation fog as part of the European Commission sponsored project 4MIDaBLE (4Dimensional Met. Information DataBase Linked across Europe). The first part of this paper describes the model formulation and discusses its behaviour simulating radiation fog in idealised cases and its sensitivity to geostrophic wind speed and initial humidity. In addition to the expected sensitivity to wind and humidity, it has been found that the treatment of surface exchange processes can have a small but significant impact on the formation of fog, its formation and clearance times and its depth.

For over two decades operational rainfall estimations from geostationary satellites have represented an ambitious aspiration of scientists and an identified need of operational meteorologists. A wide variety of infrared and combined visible and infrared methods have been proposed for the identification of suitable relationships between satellite-observed cloud top radiative features and rainfall at the ground. Microwave-based retrievals, however, correlate rainfall and internal cloud microphysical features more successfully. The most significant limitation, however, is the indirect character of the retrieval that correlates microphysical and dynamical cloud characteristics with rain amounts at ground level. METEOSAT Second Generation signals a new era for geostationary satellites with its new 12 channel imager SEVIRI and 15 minute full-disk image repeat cycle. SEVIRI is expected to contribute significantly to a better characterisation of clouds and atmospheric stability by means of improved infrared calibration, radiometric performances, imaging frequency and multispectral image analysis. The significant increase of multispectral cloud observations is expected to provide new data for the improvement of rainfall estimations from geostationary orbit. The anticipated progress from enhanced imaging frequency and multispectral data for the definition of new techniques is discussed. Considerations for operational applications, chiefly for nowcasting, are also provided as they are the main goal of the satellite. Future developments and synergies with other geostationary and polar orbiting instruments, passive and active, are finally considered as the ultimate strategy for more accurate instantaneous rainfall estimations at all latitudes.

The variation of road surface temperature across a road network is influenced regionally by meteorological parameters and locally by geographical parameters. A fast and reliable technique is described which allows the continuous collection of high resolution, geographical data including the sky-view factor which is suitable for use in road climate modelling studies. Then, by use of regression analysis, the relative importance of five geographical parameters (altitude, topography, sky-view factor, landuse and road construction) is assessed with respect to road surface temperature and atmospheric stability. Results show that sky-view factors dominate surface temperatures at high atmospheric stability whereas altitude becomes increasingly important as stability decreases. Finally, a statistical road surface temperature model is discussed with the ability to explain up to 75% of the variation of residual road surface temperatures in the study area entirely by the interaction of geographical parameters.

During the International Cirrus Experiment (ICE), cirrus cloud properties over the North Sea were observed from local surface-based and aircraft instruments. In order to place these measurements in the context of the general space-time evolution of the cirrus systems and smaller cloud elements, Meteosat images were used to study the evolution of the clouds along their trajectories. The trajectories were established with techniques derived from those used to compute cloud motion winds from geostationary satellite images. Thermal infrared (IR) and water vapour (WV) channel tracking was performed to take advantage of the properties of each channel: better contrast of the clouds over the surface in the IR, and better discrimination of upper layers in the WV channel. Although long tracks, up to 60 hours, could be observed, they did not correspond to the same cirrus elements, or even the same system along the track. The evolution of the brightness temperatures along the tracked elements showed periodic fluctuations. The representativeness of these fluctuations in terms of cloud life cycle is discussed, as it may be related to the quality of the tracking.

River flow forecasting using rainfall predictions from a mesoscale weather prediction model in combination with a physically-based rainfall disaggregation model incorporating small-scale topographic variability is demonstrated. Rainfall predicted by the UK Met Office Mesoscale Model on a 16.8 km grid is disaggregated onto a 2 km grid using a rainfall model which adds the effect of small-scale topography. River flow is calculated by a distributed rainfall-runoff model using the output from the rainfall model. A thunderstorm event on 7 June 1996 over the Brue catchment in Somerset, England is used to evaluate the models. The rainfall model successfully forecasts the band-shaped rainfall field within the catchment and the error in the total amount of flow during the storm is only -12%. An error of -40% in the peak flow is attributed to the treatment of convective clouds in the model.

This is the second part (statistical approach) of a work concerning the relationship between heavy rain and cyclonic centres in the western Mediterranean. Using a statistical approach we seek to verify the indirect role of the cyclone centres in locating, triggering or focusing heavy rain: a cyclonic centre – even if neither strong nor deep – may contribute to the low-level flow organisation and so to the creation or intensification of a low-level warm and wet current that can feed and sustain convective rain in favourable environmental conditions. The foundation for the statistical approach is several Several databases maintained and updated by the Spanish Institute of Meteorology (INM). These databases of cyclonic centres, heavy rain and strong wind events, and mesoscale convective systems (MCSs), all covering the western Mediterranean or part thereof, can be cross-referenced by looking for simultaneity between heavy rain events (or MCSs) and cyclone centres in the vicinity. We have found that in most of the heavy rain events (around 90%) there is a cyclonic centre in the vicinity, usually located so that its presence favours the creation or intensification of a feeding flow of Mediterranean air towards the area affected by heavy rain. The same occurs with the MCSs.

A new ice prediction strategy is presented based on the numerical modelling of surveyed geographical parameters. This approach enables the thermal projection of road surface temperatures across the road network entirely by model predictions and without the need for thermal maps. The influence of eight geographical parameters (latitude, altitude, sky-view factor, screening, roughness length, road construction, traffic density and topography) is investigated by means of sensitivity analyses. The sky-view factor is highlighted as the dominant control on road surface temperature, particularly at high levels of atmospheric stability. A numerical road weather model incorporating all eight parameters was run over 20 nights using forecast and retrospective meteorological data. The model has the ability to explain up to 72% of the variation in road surface temperature purely by thermally projecting surface temperature using geographical variables. Retrospective results produce an average r.m.s. error of 1 °C which is comparable to existing UK road weather models.

Some meteorological and geo-orographical factors related to cloud-to-ground flashes in Castilla-Leon (Spain) are studied. The relationship between flash density and the number of days with thunderstorms (thunderdays) is analysed, and a clear dependence and geographical variability is found. It is also shown that flashes have an average delay of four hours with respect to the time of the beginning of convection. The effect on flashes of dry static instability and moisture content is analysed by classifying days into four types according to the average values of the 850-700 hPa temperature difference and the 850 hPa dew-point temperature. A similar classification using the 700 hPa wind direction is used to analyse the effect of the mean-level flow on flashes. The results suggest that the daily number of flashes is mainly affected by the moisture content. Finally, a relationship between the cloud-to-ground flash density and geo-orographical factors (altitude, latitude and longitude) is deduced using multiple linear analysis.

Many northern countries use a road weather information system (RWIS) with a network of stations to monitor winter road conditions. Present station locations were selected after field investigations of micro- and local-climate conditions (e.g. using thermal mapping). This paper describes an approach to optimally locate and equip the stations in order to best identify conditions hazardous to road transport. This is achieved using multiple regression analysis of observed data and correlation with location meta-data. A geographical information system (GIS) is used to develop quantitative and objective descriptions of station locations by using knowledge of local and regional climate variations. Road climate is described using a slipperiness classification, in which weather situations are classified into ten types of slipperiness from the meteorological variables collected at RWIS stations. The relationships between quantified locations and data on road slipperiness in southern Sweden during one winter are analysed. The results show that the spatial patterns for different types of slipperiness are significantly related to local parameters. The three most prevalent types are analysed in detail: snowfall on a frozen road surface, hoarfrost and low visibility, and strong formation of hoarfrost.

Advanced four-dimensional air traffic management tools necessitate very accurate prediction of wind and temperature in the very short term. A method based on optimal interpolation of observational data was developed and tested within the framework of Eurocontrol's PHARE programme and the 4MIDaBLE programme under the auspices of the European Commission. It takes advantage of the growing density of automated aircraft reports to update background forecasts and produce nowcasts valid twenty minutes to a couple of hours ahead with an improved accuracy. The emergence of surveillance techniques such as the Automatic Dependent Surveillance based on the use of aircraft-to-aircraft and aircraft-to-ground data links and the Aeronautical Telecommunications Network is generating much interest in nowcasting techniques because they may facilitate the acquisition of observational data on selected areas with a given observation density. The basis of the nowcasting method is presented along with different evaluations, one of which lasted several months, using routine aircraft data over France. The evaluation shows that operational forecasts can be improved by taking into account the latest aircraft observations, but it is necessary to select aircraft data to obtain a positive response.

The Met. Office currently operates a high resolution mesoscale model that provides guidance on the likely formation of radiation fog on the spatial scale of 30-40km. A project has been under way to build a Site-Specific Forecast Model (SSFM) using a one-dimensional version of the Met. Office Unified Model as a basis, with suitable modifications to its surface treatment and, driven by output from three-dimensional NWP models. The SSFM has been applied to the forecasting of radiation fog as part of the European Commission sponsored project 4MIDaBLE (4Dimensional Met. Information DataBase Linked across Europe). This paper describes the influence of site observations on the model evolution for two real fog cases. This requires a method of forcing the SSFM via coupling to three-dimensional NWP model output; such a method is described. It is shown that when observational data at the site of interest is incorporated into the model's initial state humidity measurements provide the greatest impact on model evolution. However, their influence is short-lived (typically less than 6 hours) implying the importance of initialisation time in the correct forecast of radiation fog.

During November and December 1998, ten cyclonic disturbances over the North Atlantic and Mediterranean area were studied. Features in potential vorticity (PV) fields were tracked in the analyses and short-range forecasts of the Spanish version of the HIRLAM model (HIRLAM-INM) and the ECMWF model. They were superimposed on the corresponding Meteosat water vapour (WV) images with the aim of ascertaining the qualitative relationship between Meteosat WV imagery and PV fields that might be used to assess NWP model behaviour. Two cases predicted incorrectly by HIRLAM-INM were studied in detail: a rapidly deepening depression over the Atlantic (26–27 November 1998) and a cut-off low process over the western Mediterranean (3–4 December 1998). For evaluating HIRLAM-INM model behaviour, absolute vorticity at 2 PVU surface, positive PV anomalies at 500 hPa as well as pseudo WV images were compared with Meteosat WV pictures. Being synthetic products of the numerical model, the pseudo WV images were used to indicate the strength of the relationship between the Meteosat WV imagery and the PV fields, and to indicate whether any mismatches correspond to real NWP model errors. It is demonstrated that the HIRLAM-INM phase error at 3–4 December 1998 was clearly seen in the comparison between Meteosat and pseudo WV images.