An interesting case of cyclogenesis over the western Mediterranean Sea, accompanied by extraordinary convective activity, is described in this paper. The event took place between 18 and 21 July 2001. Its synoptic features are examined on the basis of quasi-geostrophic theory using numerical analyses from the global model of Deutscher Wetterdienst GME and from the ECMWF. Particularly remarkable is the large amount of latent heat released in the condensation process, which produces significant modifications of the flow. These effects are evident both in the fields of relative vorticity as well as potential vorticity in the upper levels. The infrared Meteosat satellite pictures are used to follow the evolution of the upper-level cloudiness associated with the process and its connection with the values of vorticity and winds at 300 hPa. Radar pictures, reflectivity and radial velocity from Doppler surveillance radar in the Veneto region allowed us to outline the structure of the mesoscale convective system – a squall line that developed during the night of 19–20 July over Italy, in the Padana Valley. The features of this system were compared with a conceptual model proposed by Houze et al. (1989). The vertical wind shear, as inferred from the radial velocity pictures, was likely to play an important role in the development of the storm. Finally, an analysis of the vertical stability in the pre-storm environment was performed using sounding data from Udine, 20 July 2001, 00 UTC. The analysis shows the importance in the storm development of the potential instability released by ascent during the transit of the baroclinic system, a process that was investigated using the vertical profile of equivalent potential temperature and an estimate of ascending motion given by the ECMWF model.

A nonhydrostatic version of Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) is used to study the effects of the horizontal model resolution and orography while simulating an active western disturbance (WD) that affected northwest India from 21 to 25 January 1999. Two numerical experiments are conducted with six combinations of two factors: horizontal model resolution and topography. National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysed data are used for the initial and boundary conditions. Simulation results indicate that the distribution and the rate of simulated precipitation due to a WD over northwest India is highly sensitive to the horizontal model resolution and topography. The model with finer resolution (30 km) is better able to estimate effects of mesoscale forcing on precipitation over the selected domain. The amount of precipitation simulated over the coarse domain is much less than the observed precipitation owing to the model's unrealistic representation of orographic effects and mesoscale forcing. Simulated terrain, vertical velocity, wind and streamline at different horizontal model resolutions are presented. The detailed structure and distribution of wind speed are simulated in the finer domain. Simulated vertical velocity and precipitation are less in the second experiment when a flat topography is used across the domain, which indicates that topography plays a significant role in modulating the WD. Sensitivity of the horizontal model resolution for precipitation is assessed and it is found that the finer domain of the model simulation gives better results.

Accurate forecasts of minimum surface temperature during winter help in the prediction of cold-wave conditions over northwest India. Statistical models for forecasting the minimum surface temperature at Delhi during winter (December, January and February) are developed by using the classical method and the perfect prognostic method (PPM), and the results are compared. Surface and upper air data are used for the classical method, whereas for PPM additional reanalysis data from the National Center of Environmental Prediction (NCEP) US are incorporated in the model development. Minimum surface temperature forecast models are developed by using data for the winter period 1985–89. The models are validated using an independent dataset (winter 1994–96). It is seen that by applying PPM, rather than the classical method, the model's forecast accuracy is improved by about 10% (correct to within ±2 °C).

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.

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.

In this paper, an adaptive Kalman filtering procedure is developed and applied to 2-metre temperature and 10-metre wind-speed forecasts in Iceland. The goal is to reduce the systematic bias and improve the accuracy of the local forecasts derived from a numerical weather prediction model and, in addition, to produce reliable prediction intervals. The method consists of adding two algorithms to the traditional Kalman filter procedure that adaptively estimate the noise statistics individually at each forecast location and each time step. The tested data set shows that the method is able to remove the systematic errors and to quantify the prediction uncertainty in a consistent manner.