A simple and pragmatic method utilising the difference between analysed near-surface and Meteosat IR temperatures (Δ T) is presented and applied with the aim of identifying and removing non-precipitation echoes in weather radar composite imagery. Despite inherent deficiencies in these multisource data, such as lower spatial and temporal resolutions relative to the radar data, Δ T is demonstrated to efficiently identify efficiently those areas void of potentially precipitating clouds, and to remove radar echoes in them. A set of 243 manually analysed composites from the summer of 2000 was used to evaluate the method. False alarm rates (FAR), percent correct (PC) and Hanssen-Kuipers skill (HKS) scores were calculated from standard contingency tables for five echo classes: weak, strong, land, sea, and all. FAR was lowered in all classes, PC was generally raised by a few percent to be over 95%, while HKS either remained unchanged or was slightly lowered through the application of Δ T. These results indicate that Δ T successfully removes a significant amount of non-precipitation, sometimes at the expense of a small amount of true precipitation. This penalty is larger over sea, which indicates that the method may need to be tuned differently for land and sea environments. This method may act as a foundation on which improvements to radar data quality control can be made with the introduction of new and improved satellite instrumentation such as that found on board the Meteosat Second Generation platform. However, this type of method should remain complementary to improved signal processing and radar data analysis techniques.

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.

An attempt is made to establish a probable link between mean September upper-air temperatures at Indian stations and the ensuing winter monsoon rainfall over eight individual meteorological subdivisions of southern India where the winter monsoon accounts for a large percentage of mean annual rainfall. For this purpose, linear correlations between the winter monsoon rainfall of the subdivisions and the mean September upper-air temperatures and vertical temperature differences at the Indian stations have been evaluated using the data set for the period 1959 to 1988. The inter-correlations between the significant parameters were calculated and then tested to derive multiple linear regression equations containing the best combination of predictor variables. There is a significant positive relationship between the winter monsoon rainfall over Tamilnadu and the mean temperature difference at 500–300 hPa levels at Nagpur in September. Secondly, the winter monsoon rainfall over Coastal Andhra Pradesh is significantly negatively correlated with the mean September temperature over Visakhapatnam at 700 hPa. The most significant inverse relationship exists between the winter monsoon rainfall over Rayalaseema and the mean September temperature over Visakhapatnam at 850 hPa. By using significant parameters, algorithms have been formulated and tested for each subdivision for six succeeding years, 1989–94. These results assisted to some extent in the prediction of winter monsoon rainfall over subdivisions of South India. Finally, stationarity is tested by evaluating another set of multiple linear regression equations using the data set for the period 1965–94 and then testing the algorithms on the preceding six years.

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.

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.

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.

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.

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.

The observation and analysis of the difficulties and differences, as well as possible errors, in the information output by automated stations is a complicated process. In observing the present weather the human observer is, for example, able to code many different codes at the same time. A period of mixed winter precipitation events is recognised as a significant challenge in assessing the reliability of automated Present Weather Sensors (PWS). The period of difficult winter conditions presented in this paper was unique in the whole two-year test period. Earlier studies have mostly been based only on liquid precipitation.

This paper presents two basic ideas: the first is to compare many simultaneous human-observed codes with one PWS code; the second is to use logistic regression to compare the observations. A wide variety of weather variables are used as candidates for ‘methodical error’.

Estimates of the downward global solar and long-wave radiations are commonly made using simple models. We have tested the estimates produced by a number of these simple models against the values predicted by the radiative transfer model used in a climate model in order to determine their suitability for global applications. For clear sky, two simple models were comparable, but under cloudy conditions a combination of a clear-sky model based on the Angstrom-Prescott equation (which deals with the downwelling solar radiation) with a cloud transmissivity utilising total cloud fraction proved best. The lowest root mean square errors were 27 W m−2 for clear-sky global solar radiation and 90 W m−2 for cloudy conditions. For downward long-wave radiation in clear-sky conditions, the model of Garratt (1992) performed best with a root mean square error of 24 W m−2. However, in cloudy conditions the model of Idso & Jackson (1969) performed best with a root mean square error of 22 W m−2, and, as it performs nearly as well as that of Garratt (1992) in clear-sky conditions, it is probably the best choice.

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).

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.

This paper describes a new model developed by the Met Office to predict night-time light levels. The Met Office Night Illumination Model (MONIM) predicts light levels both in the visible (photopic) range and in the waveband to which night vision goggles (NVGs) are sensitive. The model will be used operationally for support of night-time flying operations. The model is described in detail and its light-level forecasts are compared with observations.

In strong baroclinic flows, small errors in the initial conditions may grow rapidly and lead to large forecast errors even at very short ranges. It is therefore of crucial importance that these initial conditions are determined carefully. In this paper we use two different but complementary methods to improve the operational analysis of a storm that caused loss of life and much material damage in France on 27 December 1999. The first method is based on the adjoint technique: an adjoint model-run provides the sensitivity of one aspect of the forecast (the cost function) to the initial conditions. We then apply a correction whose shape is the sensitivity so that the corrected initial state better fits the observations. The second method is based on the Quasi-Geostrophic Potential Vorticity (QGPV) inversion. A misfit between the model analysis and satellite pictures (water vapour Meteosat images, for example) leads to the subjective modification of some of the features of the potential vorticity field. The analysis is modified using the wind and temperature corrections provided by the inversion of the QGPV correction. Both methods significantly improve the forecast and may provide valuable guidance for forecasters in real time.

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.

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 clear sky UV-index is expressed as a function of two predictable quantities: the solar zenith angle and total ozone. This function is derived by fitting the measurements of total ozone and the UV-index obtained from two instruments, one in the mid-latitudes and one in the tropics. The shape of the function was chosen so that it represents the essentials of the underlying physics. This new function gives good results for all solar zenith angles between 0° and 90° and a wide range of total ozone values.

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.

A three-day mesoscale numerical simulation has been performed over the narrow Salento peninsula (south-eastern Italy) during summer conditions characterised by weak synoptic forcing. These atmospheric conditions favour the development of complex sea-breeze systems and convergence zones on the peninsula. The aim of this work is to investigate the ability of an atmospheric mesoscale model to reproduce the surface fields of meteorological variables in the presence of local-scale forcing and breeze circulations, which are fundamental in applications such as air pollution modelling and nowcasting.

The modelled fields have been compared with available surface measurements and sodar data. Results indicate that the model can simulate the general mean wind field in a realistic way. The diurnal evolution of the wind is well reproduced and the maximum deviations mostly occur during the night, being associated with calm conditions.

Statistical analysis indicates that the typical mean bias is found to be about 1 m s-1 for hourly averaged wind speed, less than 20° for wind direction and about 1°C for temperature. The root mean square error (rmse) varies from 1 to 3 m s-1 for wind speed, from 50° to 70° for wind direction, and is about 2.4°C for temperature. All the values of the numerical indexes are within ranges which are characteristic of those found for other state-of-the-art models applied to similar cases studies.

Despite a good overall agreement between predictions and observations, some discrepancies were found in the individual profiles due both to the limited spatial representation of the local details and to the complex wind field which makes the space–time matching between the model and the observations quite critical.

The structures of the thermal mixed layer and the breeze convergence zone are similar to numerical studies relative to more idealised conditions.

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.