Weather and climate are among the major risk factors with the potential to impact upon farming performance and management. Extreme weather and climate events such as severe droughts, floods, or temperature shocks can often strongly impede sustainable farming development, particularly in the tropics and sub-tropics. Factors such as climate variability and change can contribute to the vulnerability of individual farms, as well as that of entire rural communities. These factors can also have particular impact on regional and global food security. Recent weather and climate research efforts have demonstrated the importance of targeted forecasting and scenario analysis as a way to increase the overall preparedness of farmers and farm business managers, which contributes substantially to the improvement of overall outcomes.

Locally applicable information about climate and soil properties can help farmers identify opportunities and reduce risks associated with changing to new land uses. This article describes techniques for preparing high-resolution regional maps and GIS surfaces of agriculturally relevant climate parameters. Ways of combining these climate surfaces with soil data and information about the physical requirements of crops to identify areas likely to be the most suitable for new high-value crops are then outlined. Innovative features include methods for merging observations from temporary climate stations installed for one to two years in conjunction with longer-term climate station observations to improve input data for the maps, and techniques for mapping quantiles of climatic factors that may constrain agricultural operations. Examples are the expected ‘one-in-five year’ first and last frost dates, and the ‘one-in-five year’ lowest and highest seasonal rainfalls. The use of night-time satellite infrared observations to improve spatial resolution of frost hazard maps is also described. Typical standard errors of these climate mapping techniques are summarised. The benefits of ongoing consultation with local farmers and local government staff during the design and implementation of climate/soil/crop potential studies are described. These include optimising products to meet local needs, quality control of the resulting maps and GIS surfaces through local knowledge, and improved uptake of information by users. Further applications of techniques described in this paper include products useful to the energy sector, preparation of daily gridded climate data estimates for use in water quality and plant growth modelling, and development of regional climate change scenarios.

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.

A system has been developed and made operational at the Met Office for the real-time diagnosis of soil state and surface hydrology. It is based on the Met Office Surface Exchanges Scheme (MOSES) modified to take account of unresolved soil and topographic heterogeneity when calculating surface runoff by incorporating a Probability Distributed Moisture (PDM) scheme developed by the Centre for Ecology and Hydrology. The implementation of MOSES-PDM in the Met Office's Nimrod nowcasting system is described. High resolution soil characteristics and land cover data, together with Nimrod's analyses of precipitation amount and type, cloud cover and near-surface atmospheric variables are used to drive MOSES-PDM. Hourly values of snowmelt, runoff, net surface radiation, evaporation, potential evaporation, soil temperature, soil moisture and soil moisture deficit are calculated on a 5 km grid.

Four satellite rain estimations based on microwave (MW), infrared (IR) or combined MW-IR techniques are compared with the BOlogna Limited Area Model (BOLAM) rain forecast for a severe weather event (8–13 November 2001) over the western Mediterranean Sea.

Two of the investigated multi-channel MW rainfall algorithms use data from the Tropical Rainfall Measuring Mission (TRMM). The Frequency Difference Algorithm relies on data from the TRMM Microwave Imager (TMI) and the other one combines data from the Precipitation Radar (PR) with those from the nine-channel radiometer TMI, called PR Adjusted TMI Estimations of Rainfall (PATER) algorithm. The pure IR Rain Estimator uses geostationary IR METEOSAT data and the combined Naval Research Laboratory algorithm uses both MW data from low orbiting satellites and IR data from the geostationary orbit.

Validation results, computed over a common grid, which is independent of the different field of view sizes of the applied data sets, indicate that there is generally a better performance for heavy rain (> 6 mm h−1) than for light rain (<1 mm h−1). Both MW algorithms perform rather similarly, although PATER shows some rain detection problems due to thick aerosol loads originating from the desert. The BOLAM model presents a good agreement with the MW and only a minor location error of a heavy rain area was detected. Both IR-based algorithms have problems in identifying the correct rainy areas compared to MW. Overall, the results suggest that there are advantages in combining both techniques – the well-known rain physics of the MW channels with the high temporal resolution of IR algorithms – to retrieve precipitation from satellite data.

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.

Decision-making in agricultural production is a complex process in which many risks need to be con-sidered for an informed decision to be made. In many parts of the world, weather and climate are one of the biggest production risks and uncertainty factors impacting on agricultural systems performance and management. Farmers around the world, especially those in the developing countries, have been trying to adapt to the variable weather and climate conditions through various risk-management strategies. Improved weather and climate information, supplied to the farming community in a timely manner, can greatly assist the farmers in their operational decisions.

A method of identifying sea-breeze fronts by remote sensing (visible and infrared data from the GOES-8 satellite) was applied to north-eastern Brazil. The aim of the study was to estimate the frequency of observed sea-breeze fronts and the average distance they penetrate inland at 1800 UTC. During the dry season (between September and December) in 2000, the highest frequency of sea-breeze fronts was recorded on the northern fringe of the study area. The sea-breeze fronts penetrated furthest inland between September and November, with a maximum distance from the coast at Ceará of 100 km.

A scheme for the detection of fog and low stratus over land during daytime based on data of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument is presented. The method is based on an initial threshold test procedure in the MODIS solar bands 1–7 (0.62–2.155 μm). Fog and low stratus detection generally relies on the definition of minimum and maximum fog and low stratus properties, which are converted to spectral thresholds by means of radiative transfer calculations (RTC). Extended sensitivity studies reveal that thresholds mainly depend on the solar zenith angle and, hence, illumination-dependent threshold functions are developed. Areas covered by snow, ice and mid-/high-level clouds as well as bright/hazy land surfaces are omitted from the initial classification result by means of a subsequent cloud-top height test based on MODIS IR band 31 (at 12 μm) and a NIR/VIS ratio test. The validation of the final fog and low stratus mask generally shows a satisfactory performance of the scheme. Validation problems occur due to the late overpass time of the TERRA platform and the time lag between SYNOP and satellite observations. Apparent misclassifications are mainly found at the edge of the fog layers, probably due to over-or underestimation of fog and low stratus cover in the transition zone from fog to haze.

An assimilation of radar reflectivity into a numerical weather prediction (NWP) model with a horizontal resolution of 2.8 km is presented and applied to three severe convective events. The suggested assimilation method takes into account differences between the model and radar-derived precipitation in modifying vertical profiles of water vapour mixing ratio in each model time step by the nudging approach. Version 3.9 of the LM COSMO (Local Model COSMO) – NWP model used in this study includes the explicit formulation of the cloud and rain processes involved. Two variants of the assimilation technique are designed and outputs of their implementation are compared. The first variant makes use of the ground data only, while the second utilises vertical profiles of precipitation water. Both variants provide an improvement of precipitation forecast in comparison with outputs of the control run without assimilation procedures applied. When the assimilated radar data indicate initial precipitation near an expected storm, the NWP model is capable of forecasting basic features of the storm development two to three hours ahead. Three case studies are presented. In one, the assimilation method that takes into account the vertical structure of the precipitation water yields better results than the others which utilise ground data only. However, for the remaining two case studies both types of the assimilation method produce comparable results.

A model to deduce cloudiness from automatic measurements of synoptic and road weather stations during winter nights is introduced. The (height-adjusted) cloud amount of each station can be determined from near-surface temperature measurements and precipitation information only. By using the difference between the air temperature at surface level and the air temperature 2 m above ground, a correlation coefficient is achieved of up to 0.91 between the calculated cloud amount and observations. The model is applied to a dense network in central Switzerland in order to obtain a two-dimensional cloud map by interpolation. With the help of a tracking algorithm, the displacement of cloud patterns is nowcasted. The procedure was tested using data from winter 2003–4. It works successfully with a forecast range up to about 90 minutes. The results are used to predict a change in surface temperature (in cases with changing cloudiness), and thus allow nowcasting of slippery roads or ground frost.

Large urban centres located within confining rugged or complex terrain can frequently experience episodes of high concentrations of lower atmospheric pollution. Metropolitan Phoenix, Arizona (United States), is a good example, as the general population is occasionally subjected to high levels of lower atmospheric ozone, carbon monoxide and suspended particulate matter. As a result of dramatic but continuous increase in population, the accompanying environmental stresses and the local atmospheric circulation that dominates the background flow, an accurate simulation of the mesoscale pollutant transport across Phoenix and similar urban areas is becoming increasingly important. This is particularly the case in an airshed, such as that of Phoenix, where the local atmospheric circulation is complicated by the complex terrain of the area.

Within the study presented here, a three-dimensional time-dependent mesoscale meteorological model (HOTMAC) is employed for simulation of lower-atmospheric flow in Phoenix, for both winter and summer case-study periods in 1998. The specific purpose of the work is to test the model's ability to replicate the atmospheric flow based on the actual observations of the lower-atmospheric wind profile and known physical principles. While a reasonable general agreement is found between the model-produced flow and the observed one, the simulation of near-surface wind direction produces a much less accurate representation of actual conditions, as does the simulation of wind speed over 1,000 metres above the surface. Using the wind and turbulence output from the mesoscale model, likely particle plume trajectories are simulated for the case-study periods using a puff dispersion model (RAPTAD). Overall, the results provide encouragement for the efforts towards accurately simulating the mesoscale transport of lower-atmospheric pollutants in environments of complex terrain.

Challenges in linking meteorological and climatological information with a wide range of farming decisions are addressed in this paper. In particular, while a considerable amount of weather and climate information is now available for farmers, some types of information under development or already operational, particularly climate forecasting, formation, may be ill-suited for use by farmers for their decision-making. Case studies show it is particularly important for those key farm decisions that are amenable to weather and climate information to be identified clearly so that weather and climate information can be better tailored to suit farming decisions. A participatory approach provides farmers with ownership of the processes associated with development of weather and climate information and facilitates advances in linking climate and weather information and forecasts to farm decisions. Decision-support systems provide useful output when used with farmer discussion groups. Developing appropriate interdisciplinary systems to connect climate, weather, and agronomic information, especially including forecasting systems, with farm management is needed if uptake of weather and climate information by farmers is to be successful. Provision of output of climate change scenario and trend information to aid long-term strategic farm management decisions needs to be considered, especially in regions where more vulnerable farming zones exist.

This paper presents an analysis of rainfall amounts in Greece. For this purpose, monthly and annual rainfall data from 36 representative Greek weather stations were analysed statistically. Rainfall is represented by a standardised annual departure calculated as the ratio of the annual departure to the standard departure. The departures were calculated for two regional subdivisions. Clear evidence of changes in Greece's rainfall patterns over the past 25 years were found. The effect of changes on climatological rainfall means (‘normals’) is addressed and possible shifts in the rainy season were investigated. The secular variability of long time-series of annual rainfall amounts was also examined. In addition, the five-year running means of annual and monthly rainfall data and ‘normals’ were plotted. Comparisons of the means from the ‘old’ and ‘new’ 10-, 20- and 30-year, CLINO (Climatological Normal) period 1961–90 (the latest global standard normal period) and from the periods 1900–49 (‘old’ regime) and 1950–99 (‘new’ regime), as well as examination of the secular trend, shows that there is evidence that some parts of Greece have had shifts in their rainfall regime towards drier conditions. In general, rainfall amounts began to decline in the 1980s, a trend which has continued to the present. Most years between 1980 and 1999 experienced below to well-below ‘normal’ rainfall. Discrepancies between the ‘old’ and ‘new’ mean values for the amounts of rainfall and differences in the duration of rainy season suggest that ‘normal’ rainfall data for various water supply, planning, engineering and agricultural national projects should be calculated for the last 30 years. This is mostly true because of indications of a changing climate in Greece, a fact that requires more recent updating of the climatological ‘normals’.

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.

When high pressure is located near the Korean peninsula, a diffluent wind regime generally occurs over the Yellow Sea. At night or early morning, diffluent westerly winds occur on the western side of the Korean peninsula near Inchon city and encounter a combined land breeze and katabatic easterly offshore wind, resulting in conditions ranging from calm to a moderate westerly wind near the coast. Nocturnal radiational cooling of the land surface and the moisture laden westerly winds can cause air near the coast to become saturated, resulting in coastal advection fog. During the day, on the other hand, the synoptic-scale westerly wind is reinforced by a westerly sea breeze and is further reinforced by a westerly valley wind directed upslope towards the mountain top. Even if the resulting intensified onshore wind could transport a large amount of moisture from the sea over the land, it would be very difficult for fog to form because the daytime heat flux from the ground would develop the convective boundary layer inland from Inchon city sufficiently to reduce significantly the moisture content of the air. Therefore, fog does not generally form in situ over the inland coastal basin. When an area of cold sea water (10 °C average) exists approximately 25–50 km offshore and the sea surface temperature increases towards the coast, air parcels over the cool sea surface are cooled sufficiently to saturation, resulting in the formation of advection sea fog. However, at the coast, nocturnal cooling of the ground further cools the advected moist air driven by the westerly wind and causes coastal advection fog to form.

A ten-year climatology has been prepared of dust days for central eastern Australia, from January 1995 to February 2004, using reports from the Australian Bureau of Meteorology's observational network. It was found that there had been a total of 55 dust days during this ten-year period. Of these, 43 dust days were associated with cold fronts: 24 were classified as embedded in the zonal westerlies in the Great Australian Bight (GAB) or in westerlies derived from low pressure systems in the GAB, whereas the remaining 19 were associated with cold fronts over eastern Australia where high pressure systems in the GAB generated strong post-frontal south to southeast winds. Three detailed case studies of dust storm generation and transport were modelled using a sophisticated coupled wind erosion prediction system. The model predictions in all three cases were found to be broadly consistent both with satellite images highlighting dust and the synoptic observations that reported dust. Antecedent land surface conditions are found to be critically important in generating dust, and their representation in the integrated prediction system is subsequently important in providing skilful model predictions of dust transport.

Agrometeorological information and services from the National Meteorological and Hydrological Services (NMHSs) are increasingly being demanded by the farming community to cope more efficiently with climate variability and the increasing incidence of extreme meteorological events such as droughts, floods, frosts and wind erosion. While considerable advances have been made in the collection, archiving and analysis of weather and climate data, their transformation into information that can be readily used by the farm sector has lagged behind, especially in developing countries where such information needs are the greatest. One of the important reasons is the lack of adequate interaction with the user community in assessing the appropriate dissemination and communication procedures that can enhance the value of the agrometeorological information and services. A brief review of the present status of dissemination and communication of agrometeorological information by the NMHSs and associated agencies in different regions around the world is presented. A description of the user communities for agrometeorological information and their varying needs is also presented. Opportunities and challenges in the dissemination and communication of agrometeorological information by the NMHSs are described with suitable examples which emphasize that continued improvements are necessary to make agrometeorological information more accessible and useful to the user community.

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.

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.