Welcome to the first issue of Meteorological Applications under my Editorship. Although I became Editor in July last year, John Thornes continued to oversee the production of issues up to the end of Volume 12. Firstly I should like to thank John for all his hard work and enthusiasm in steering the Journal over the past five years. I am delighted that he has agreed to remain on the Editorial Board, so that his experience and skills will still be available. You will also see from the list on the inside front cover that several members of the Editorial Board have left the Journal, and I should like to thank them, too, for their contributions to Meteorological Applications. I am also pleased to welcome the new members to the Board.

Two high resolution quantitative precipitation forecasts with different levels of realism are evaluated. Classical scores (bias, correlation and scores based on contingency tables) confirm that the two forecasts do not have the same quality. A multi-scale extension of these scores has then been made to produce a validation for hydrological purposes. Rainfall fields are integrated over surfaces of various scales. For better simulation, scores indicate an increase in the quality of the simulated precipitation for larger surfaces (typically more than 100 km2): the localisation errors are reduced by the aggregation. This helps to determine the usefulness of such forecasts for hydrological purposes.

In this study of tropical storm impacts in Central America, the relationship between physical variables (available in ‘real time’) and damage is explored, and a simple tool for early approximate evaluation of the impact is developed. Land track and energy dissipation appear as the most interesting parameters that modulate the hurricane impact. Because of the difficulty of attaching a monetary estimate to the damage caused in a large number of cases (as is required in a statistical approach), an ‘Impact Index’ based on the logarithm of casualties is introduced. Thereafter, within a subset of events in which damage in monetary terms is known, a rough link between damage and the Impact Index is derived.

Shortly after a new event, as soon as land track and energy dissipation are known, either by means of an empirical equation or using a contour graph, the Impact Index may be determined. Another empirical equation allows a rough estimate of damage in monetary units, but because this estimate is based on a limited number of cases, it must be treated with caution. The methodology is tested for a small set of independent cases.

Vulnerability to tropical cyclones depends not only on natural factors but also on sociopolitical conditions. A coupled sociological and environmental approach is believed to be the best way to improve the early impact estimate methodology.

Wind direction is a circular variable. This makes the algorithms used to find its standard deviation different from that of the linear variables. In particular, the requirement for storing all the data points before the standard deviation can be computed limits the storage capacity and puts great strain on remote data acquisition systems. Various algorithms have therefore been developed to estimate the standard deviation in order to reduce the number of terms stored. The following work consists of a comparative analysis of such estimators together with the parameters used. It emerges that some of the assumptions adopted to produce the equations being analysed do not hold in practice, even though this does not affect significantly the performance of the estimators that depend on them. On the other hand, the parameter that has the best trend with the algorithm adopted is the magnitude of the vector to the centre of gravity of the system. However, such a result gives rise to some concerns since it does not account for the ‘vectorial’ nature of the angle being treated.

This paper reports on work that demonstrates that weather radar can yield remarkably accurate rainfall measurements in Scotland. Analysis of data during 1999 and 2000 shows Nimrod radar data to have no consistent error bias and a 24% mean error in storm rainfall totals. For rivers such as the Ruchill Water in the Perthshire Highlands this highlights the fact that the observations and quality control corrections utilised by the Met Office Nimrod system may offer particular benefits in certain flood warning applications. Assessment of wind data indicates that smaller errors occur in Nimrod radar observations during lower wind speeds (less than 15 knots) and with an easterly airflow. However, errors may also be attributed to occasions when the Numerical Weather Prediction model fails to represent the wind conditions correctly at ground level.

Two models were used to assess the effects of coastal characteristics on radar propagation in ducting conditions in the Persian Gulf. The NCAR/Penn State MM5 model simulated atmospheric conditions at a 5-km horizontal spatial and hourly temporal resolution on a day on which observations of ducts existed. The output from this model was input to the AREPS propagation model to produce radar coverage over coastal areas. Four factors influenced radar propagation: the sea breeze; coastal configuration; orography; and ambient wind. The sea breeze alone allowed propagation to extend about 100 km inland in a layer 200 m deep. When the breeze was aided by a following ambient wind the propagation layer extended for 150 km and was 400 m deep. A coastal indentation caused differences in depth and intensity of propagation over a distance of about 30 km parallel to the coast in which the indentation occurred. Steep near-coastal orography blocked radar propagation.

The moist convective instability over the Arabian Sea during the Arabian Sea Monsoon Experiment 2002 is studied using high resolution radiosonde data over the Arabian Sea off the west coast of India and inland at Goa (15°21′N, 73°51′E). The vertical structure of the lower troposphere is discussed in terms of the profiles of conserved variables and the time series of convective available potential energy (CAPE), mixed-layer depth, lifting condensation level and level of free convection. Analysis shows that the Arabian Sea within 200 km of the west coast of India is characteristically a low-CAPE region irrespective of whether it is a good or bad monsoon season. Little or no variability in the mean (0–500 hPa) lapse rate during July and August indicates the quasi-equilibrium state of the tropical atmosphere during the summer monsoon with an approximate balance between the convective and large-scale forces. Large-scale forcing for the ascent of surface air seems to be of prime importance for triggering deep convection and rainfall over the East Arabian Sea. Case studies of precipitating convection over inland at Goa during the monsoon indicate a moistening and warming of the lower troposphere following the convection.

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Paraná river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Atmospheric pollution models are generally evaluated using measures of the difference between predicted and observed concentrations. These statistics can be inadequate as they tend to be sensitive to small errors in the timing of peak events that are generally of little consequence from a health point of view. This paper presents a technique for evaluating atmospheric carbon monoxide models based on the statistical properties of the uptake of carbon monoxide, rather than relying on traditional model evaluation measures. A simple semi-empirical atmospheric carbon monoxide model is used as an example to illustrate the approach.