Meteosat infra-red imagery for the Great Storm of October 1987 is analysed to show a series of very shallow arc-shaped and smaller chevron-shaped cloud features that were associated with damaging surface winds in the dry-slot region of this extra-tropical cyclone. Hypotheses are presented that attribute these low-level cloud features to boundary-layer convergence lines ahead of wind maxima associated with the downward transport of high momentum from overrunning, so-called sting-jet, flows originating in the storm's main cloud head.

This paper presents a scheme that classifies reflectivity measurements from ground-based volume scanning radar into convective and stratiform precipitation types. The algorithm developed in this paper uses the neural network approach as a tool for classification. The algorithm is trained and validated on radar data obtained from 18 Weather Surveillance (WSR-88D) Doppler Radar sites in the southern United States, sampled over the period December 1997 to October 1999. For the training and validation of the neural network, classification information from a hybrid approach based on Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) classification products and low-level ground radar reflectivity fields is taken as reference. The approach utilises PR's ability to observe the vertical structure of storms with high resolution (250 metres) and WSR-88D's ability to observe the low-level horizontal reflectivity magnitude and spatial variability. The method was applied to an extended dataset consisting of storm cases associated with squall line convection, mesoscale convective system, and widespread stratiform precipitation with embedded convection. The scheme is shown to have an overall 75% probability of detection, 18% false alarm rate, 67% critical success ratio, and 0.554 in prediction correlation (Cramer's V coefficient). It has an overall 4% overestimation and 27% underestimation of stratiform and convective rain areas, respectively. The classification results are compared to two current methods and improvements are shown in terms of the above statistical measures and through visual comparison with PR classification fields. The improved classification scheme derived herein will help establish more accurate radar reflectivity-rainfall relationships and improve the retrieval of diabatic heating in different cloud systems.

The ECMWF Ensemble Prediction System 2-metre temperature forecasts are affected by systematic errors due mainly to resolution inadequacies. Moreover, other errors sources are present: differences in height above sea level between the station and the corresponding grid point, boundary layer parameterisation, and description of the land surface. These errors are more marked in regions of complex orography. A recursive statistical procedure to adapt ECMWF EPS-2metre temperature fields to 58 meteorological stations on the Mediterranean island of Sardinia is presented. The correction has been made in three steps: (1) bias correction of systematic errors; (2) calibration to adapt the EPS temperature distribution to the station temperature distribution; and (3) doubling the ensemble size with the aim of taking into account the analysis errors. Two years of probabilistic forecasts of freezing are tested by Brier Score, reliability diagram, rank histogram and Brier Skill Score with respect to the climatological forecast. The score analysis shows much better performance in comparison with the climatological forecast and direct model output, for all forecast timse, even after the first step (bias correction). Further gains in skill are obtained by calibration and by doubling the ensemble size.

To obtain a high-resolution wind field and to model snow drift over complex topography, the Swiss Federal Institute for Snow and Avalanche Research (SLF) uses the atmospheric model ARPS (Advanced Regional Prediction System, University of Oklahoma). ARPS accesses horizontally homogeneous initial fields. Until recently, this model was driven by atmospheric soundings recorded at sites far away from the actual model domain. In order to optimise the initial conditions of ARPS and to provide the option of producing real forecasts, a new downscaling method has been developed, which can also be applied to other uses. Based on the vertical structure of the atmosphere produced by the operational mesoscale forecast model aLMo (Alpine Model, MeteoSwiss), an artificial sounding is created for a specified location within the ARPS grid. Some of the techniques applied include model bias adjustment (based on aLMo verification studies), the introduction of horizontal filters, the assimilation of surface observation data and the vertical displacement of the boundary layer, motivated by the distinct topographies of the ARPS and the aLMo model. The combination of the methods differs from parameter to parameter. This paper describes details of the downscaling technique and gives an example of the application of the method.

The daily maximum and minimum temperatures in Melbourne, Australia, for the period 1981–1990 are examined in this article by means of fractional integration techniques. Using a parametric testing procedure (Robinson 1994), the results show that the time series for both datasets can be specified in terms of fractionally integrated statistical models: the maximum temperatures with orders of integration slightly smaller than 0.5 and the minimum temperatures with values slightly higher. Thus, the temperatures seem to be stationary in the case of the maximum values and non-stationary for the minimum ones, but mean reverting in both cases. Moreover, no significant trends in the model were found, implying that there is no evidence of climatic change in the data, at least for the period 1981–1990.

A high pressure system between 20°S and 35°S, moving from east to west, passed over the Andes mountains on 27 and 28 February 1997. Some days earlier, on 20 February, another centre of anticyclonic vorticity had moved westward without crossing the Andes. These events developed during a South American Seesaw System (SASS) event that had been displaced southward from its climatological position.

This southward displacement of the local circulation pattern was accompanied by intense convective activity during the positive phase of the event, which favoured the development of strong storms in north and central Argentina. The unusual configuration of the motion field gave rise to atypically high temperatures in the mid-latitude Argentinian region.

Both anticyclonic systems reached the mountain barrier: one from the north and the other from the south. The analysis of the factors involved in this phenomenon reveals that the static stability in each system behaved differently near the mountain barrier. Advection of potential vorticity was responsible for the westward motion of the system, while the radiative processes influenced the production of cyclonic and anticyclonic potential vorticity which balanced out during each day.

In Part I of this study recent versions of the ECWMF Integrated Forecasting System (IFS) are used together with historical observational data to carry out reanalyses and deterministic reforecasts of three major north-west European wind storms of the twentieth century. The storms considered are the Dutch storm of 1 February 1953, the Hamburg storm of 17 February 1962, and the British October storm of 1987 (Great October Storm). Common to all these storms is their severity, which caused large loss of life and widespread damage.

Reanalysis of the storms is based on a 3D-Var and 4D-Var assimilation scheme at a horizontal resolution of T$_L$159 ($\approx$125 km) and T$_L$511 ($\approx$50 km), respectively. Similarly, two different horizontal resolutions (T$_L$159 and T$_L$511) are used to investigate the deterministic predictability of these storms. The lower-resolution system is exactly that used in the ERA-40 reanalysis project. The high-resolution system is a more recent version of the ECMWF IFS.

It is shown that the basic characteristics of the Dutch and Hamburg storms that gave rise to the storm surge are well predicted by the single deterministic forecasts up to about 48 and 84 hours, respectively, in advance.

Our capability to predict the Great October Storm is more difficult to assess. On the one hand, even recent versions of the ECMWF IFS underestimate the severity of the storm in the very short-range (12–24-hour forecasts started at 12 UTC 15 October 1987). On the other hand, the high-resolution version of the ECMWF IFS provides excellent deterministic forecasts of the track and intensity of the storm up to 96 hours in advance. However, there are errors in the timing of the storm (12 hours for the 96 hour forecast).

From the results presented in this study it is concluded – bearing in mind the limited number of cases considered – that with the current ECMWF forecasting system reliable deterministic predictions of some European wind storms are possible several days in advance.

Theories are good for the intellect, but are no more useful than a bit of practical experience. Perhaps the most significant thing about them is that they have been accorded quite unjustified status by engineers.