Methods to calculate extreme wind speeds are described and reviewed, including ‘classical’ methods based on the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD), and approaches designed specifically to deal with short data sets. The emphasis is very much on the needs of users who seek an accurate method to derive extreme wind speeds but are not fully conversant with up-to-date developments in this complex subject area. First, ‘standard’ methods are reviewed: annual maxima, independent storms, r-largest extremes with the GEV distribution, and peak-over-threshold extremes with the GPD. Techniques for calculating the distribution parameters and quantiles are described. There follows a discussion of the factors which must be considered in order to fulfil the criterion that the data should be independent and identically distributed, and in order to minimize standard errors. It is commonplace in studies of extreme wind speeds that the time series available for analysis are very short. Finally, therefore, the paper deals with techniques applicable to data sets as short as two years, including simulation modelling and methods based on the parameters of the parent distribution.

This study identifies the use of the sky view factor (SVF) in urban climate studies. In addition, it relates air temperature differences to the SVF and examines these differences with respect to the height at which fish-eye photographs are taken for the calculation of the SVF. The study focuses on night-time air temperature patterns within the urban canopy using data collected during clear, calm nights from sixteen permanent stations and from car measurements. Fish-eye photographs taken at two levels (2 m above ground and at ground level) are compared and shown to be statistically different. The results of the study performed in Göteborg, Sweden, indicate a fairly strong relationship between air temperature and SVF. The permanent stations used indicate that it is better to use fish-eye photographs taken at ground level. The relationship is determined by means of regression analysis. The SVF variation in urban areas and the importance of SVF in relation to other central parameters such as thermal admittance are also discussed.

Errors and uncertainty in radar estimates of precipitation result both from errors in the basic measurement of reflectivity and from attempts to relate this to the precipitation falling at the ground. If radar data are to be used to their full potential, it is essential that effective measures are taken to mitigate these problems. The automatic processing of radar data that forms part of the UK Met. Office's Nimrod system addresses a number of specific sources of error. These include the identification and removal of spurious echoes resulting from anomalous propagation of the radar beam, errors resulting from variations in the vertical profile of reflectivity and radar sensitivity errors. Routine verification of the surface precipitation estimates has been undertaken, largely through comparison with rain gauge observations, over a range of timescales, which has allowed the benefits of the quality control and correction processes to be quantified. Although the improvement derived varies according to the dominant synoptic situation, an average reduction in the root-mean-square difference between gauge and radar data of 30% can be achieved.

The dry intrusion is a coherent region of air descending from near tropopause-level. It often has a clear signature in satellite imagery, especially in the water vapour channel, where it is seen as a ‘dark zone’. Parts of dry intrusions are characterised by high potential vorticity and, upon approaching a low-level baroclinic zone, rapid cyclogenesis may be expected to ensue. The leading edges of dry intrusions are defined by cold θw-fronts (moisture fronts). In some places the dry intrusion undercuts rearward-ascending warm air to give an ana-cold front. In other places it overruns the warm air to produce an upper cold θw-front in advance of the surface cold front. Here the dry intrusion is associated with the generation of potential instability and its eventual release as showers or thunderstorms. Identification of dry intrusions provides the forecaster with additional nowcasting evidence that is especially helpful when issuing severe weather warnings. The identification of water vapour dark zones associated with dry intrusions can also form the basis of methods for validating NWP models. Through their relationship to high potential vorticity, they can provide guidance for bogussing NWP models in situations of potentially severe weather. This article provides an introduction to the structure and behaviour of dry intrusions and their relationship to other aspects of extra-tropical cyclones.

A new intensity-scale method for verifying spatial precipitation forecasts is introduced. The technique provides a way of evaluating the forecast skill as a function of precipitation rate intensity and spatial scale of the error. Six selected case studies of the UK Met Office now-casting system NIMROD are used to illustrate the method.

Precipitation forecasts from mesoscale numerical weather prediction (NWP) models often contain features that are not deterministically predictable and require a probabilistic forecast approach. However, some forecast providers still refrain from a probabilistic approach in operational forecasting because existing methods are associated with substantial costs. Therefore, a pragmatic, low-budget postprocessing procedure is presented that derives probabilistic precipitation forecasts from deterministic NWP model output. The methodology looks in the spatio-temporal neighbourhood of a point to get a set of forecasts and uses this set to derive a probabilistic forecast at the central point of the neighbourhood. For the sake of low implementation costs and low running costs, the procedure does without ensemble simulations, historical error statistics on the operational interaction of a forecaster. The procedure is applied to the output of the mesoscale model LM, the regional part of the operational modelling system of the German Weather Service (DWD). The probabilistic postprocessed forecast (PPPF) outperforms the deterministic direct model output in terms of forecast consistency, forecast quality and forecast value.

Surface precipitation-rate estimates derived from radar data are potentially of considerable value to high-resolution Numerical Weather Prediction (NWP) models. This paper describes a scheme developed to assimilate precipitation rates derived from the UK weather radar network into the UK Met. Office Mesoscale Model, with the aim of improving the analysis and forecast of precipitation. It is based on 'latent heat nudging', in which the model profiles of latent heating are scaled by the ratio of observed and model precipitation rates. This causes the model to adjust so that the diagnosed precipitation rate agrees more closely with observations.

The assimilation algorithm is outlined, and the results of a trial of the scheme are described. The scheme brings an increase in forecast skill for precipitation distribution in the first six to nine hours of the forecast, a conclusion supported both by objective verification against radar data and subjective assessment of 14 forecasts. The main benefit was found to occur in frontal cases. The scheme was implemented operationally on 16 April 1996.

Soil/vegetation–atmosphere transfer schemes (SVATS) require only a few primary parameters as inputs (leaf area index, vegetation fraction and albedo). The object of this paper is to describe methods used to derive these parameters from surface cover maps, climate maps and NDVI datasets. The resolution of these new surface parameters is 1 km at the global scale. The ECOCLIMAP database is available online to the research community at http://www.cnrm.meteo.fr/gmme/PROJETS/ECOCLIMAP/page_ecoclimap.htm. An application of this strategy for urban use at a finer scale (30 m resolution) is presented here for the city of Marseille.

A suite of methods that have been proposed for statistical post-processing of ensemble forecasts based on historical verification data (i.e. ensemble-MOS methods) are compared with each other, and with direct probability estimates using ensemble relative frequencies, in the idealised Lorenz '96 setting. The three most promising methods are logistic regressions predicting probabilities associated with selected quantiles, ensemble dressing (a kernel density estimation approach), and linear regressions with non-constant prediction errors that depend on the ensemble variance.

An approach to evaluating probability forecasts for dichotomous events, based on their economic value over all possible cost/loss ratio decision problems, is proposed. The resulting Value Score (VS) curve shows non-dimensionalised relative economic value as a function of the cost/loss ratios for different decision-makers, over their full meaningful range. The VS curve is similar in terms of computational mechanics and graphical display to the Relative Operating Characteristic (ROC) curve, but the ROC curve is shown to be insensitive to either conditional or unconditional biases and thus to reflect potential rather than actual skill. The possibility of collapsing the VS curve into a single scalar score is addressed, and it is shown that the results can depend very strongly on the assumed distribution of cost/loss ratios in the community of forecast users.

In order to decide whether or not a weather service supplier is giving good value for money we need to monitor the quality of the forecasts and the use that is made of the forecasts to estimate their value. A number of verification statistics are examined to measure the quality of forecasts - including Miss Rate, False Alarm Rate, the Peirce Skill Score and the Odds Ratio Skill Score - and a means of testing the significance of these values is presented. In order to assess the economic value of the forecasts a value index is suggested that takes into account the cost-loss ratio and forecast errors. It is suggested that a combination of these quality and value statistics could be used by weather forecast customers to choose the best forecast provider and to set limits for performance related contracts.

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.

Rainfall events in the United Kingdom during the twentieth century have been surveyed and those identified as extreme by the Flood Studies Report (1975) standards have been examined for common features. Events of duration up to 60 hours were considered in order to investigate those that could cause flash floods. More than half of the 50 events identified were short-period convective storms. The rainfall events were classified by meteorological situation, location and season, allowing the identification of conditions under which extreme rainfall occurred.

Suitable conditions for extreme rainfall were split into three categories: orographic, frontal and convective. The frontal and convective classes were then divided into two sub-classes according to whether significant embedded instability was present in the frontal cases and the nature of the convection in the convective cases. The study revealed a lot of commonality between the cases. For example, all of the orographic events occurred in winter in moist west to southwest airflows, and 80% of the frontal cases involved a slow-moving depression to the south or east and also a slow moving frontal system. A conceptual airflow diagram has been developed for some of the frontal cases. The key result, however, was the discovery that each category of meteorological situation occupied a unique space in a rainfall amount versus duration diagram for each extreme event. This offers exciting opportunities for applying the results of this study and a framework for studying future events.

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 of estimating the economic value of weather forecasts for decision-making is described. This method has recently been used for user-oriented verification of probability forecasts, but is here applied to aid forecast users in optimising their decision-making from probability forecasts. Value may be calculated in the same way for either probability forecasts or deterministic forecasts, and thus provides the user with a direct comparison of the value of each in terms of money saved, which is more relevant than most standard verification scores. The method is illustrated using site-specific probability forecasts generated from the ECMWF ensemble prediction system and deterministic forecasts from the ECMWF high-resolution global model. It is found that for most forecast events and for most users the probability forecasts have greater value than the deterministic forecasts from a higher resolution model.

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.

A physical discussion of vorticity and its change by stretching and tilting is presented. The discussion naturally leads on to the conservation of quasi-geostrophic potential vorticity, q, and the flow associated with anomalies in q. The extension to full potential vorticity (PV) and the associated conservation and inversion properties is made. The use for illustrating and understanding synoptic developments of maps of PV on isentropic surfaces and potential temperature (θ) on PV surfaces is shown by considering two recent examples in the North Atlantic-Europe regions. The first is a summer situation and the second is the major blocking in January 1996. Concluding comments about the role of diabetic processes and other possible uses of these diagnostics are made.

During the past decade hydrologists have become increasingly aware of the problems of fluvial flood prediction during periods of intense convection, particularly in urbanised catchments whose rainfall-runoff responses tend to be rapid. Existing approaches to deterministic, short-range rainfall prediction are often deficient in their treatment of convective precipitation because they cannot resolve individual convective clouds or effectively model their evolution. In 1994 the UK Met. Office established a joint R&D programme with the Environment Agency (responsible for flood prediction in England and Wales) to explore the benefits of an Object-Oriented conceptual Model (OOM) of convection in the nowcasting of fluvial floods. This involved the development of an automated nowcasting system (GANDOLF) designed to run the OOM during episodes of air mass convection. This paper describes the structure and function of the GANDOLF system and compares the performance of the OOM with that of two other precipitation models routinely used by Thames Region of the Agency.

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.

Using a statistical approach this paper focuses on an analysis of spatial air temperature variations and their relation to urban land use. The work is based on data collected during an 18-month period at 30 sites in Göteborg, Sweden. Measurements show intra-urban air temperature differences of up to 9 °C in the urban district, and the main purpose of this study is to determine if these variations are statistically significant. A stepwise multiple regression analysis confirmed that surface cover is important for governing air temperature differences in the area. Information on land use and surface cover was gathered from a continuously updated land use database, the Master Plan of Göteborg, and a separate site description analysis. The site description analysis includes a test of three methods using aerial or fish-eye photos for characterisation of surface cover in the urban district. The results show statistically significant temperature variations between different land use/land cover categories on a diurnal basis and for all weather conditions. The importance of the results for urban planning application is discussed.