Economic impacts from the near record warm and snow-free winter of 2001–2 in the United States were assessed to ascertain their dimensions and relevance to issues like climate prediction and climate change. Unusual impacts resulted and embraced numerous sectors (heating/energy use, construction, tourism, insurance, government, and retail sales). Many outcomes were gains/benefits totalling $19.6 billion, with losses of $8.2 billion. Some economists identified the sizable positive impacts as a factor in the nation's recovery from an on-going recession stemming from the terrorist attacks on 11 September 2001. Understanding the impacts of such a winter reveals how climate predictions of such conditions could have great utility in minimising the losses and maximising the gains. The results also have relevance to the global warming issue since most climate models project future average winter temperature and snowfall conditions in the United States to be similar to those experienced in 2001–2.

Wind farming has grown rapidly in Europe over the last decade. All European countries with shallow coastal waters and strong mean coastal wind speeds are planning or already constructing offshore wind farms. Large parts of the North Sea and Baltic Sea are being investigated to see whether they are suitable for offshore wind parks. In this study we show how satellite images taken by space-borne radar sensors can be used to determine mesoscale wind fields and thus help in the task of planning offshore wind farms. High-resolution synthetic aperture radar (SAR) images acquired by the European remote sensing satellite ERS-2 showing single wind turbines are investigated. The methods for retrieving high-resolution wind fields from SAR images are explained and statistical comparisons of wind speed and wind direction at the two offshore wind park sites in the North Sea, Horns Rev and Butendiek, are given.

Over the last decade, research has greatly increased into the use of Geographical Information Systems (GIS) in a variety of applications that involve the processing of climatological and meteorological data. This upsurge of interest is related directly to the fall in price of ‘commercial off-the-shelf’ GIS products together with large advances in computer processing ability. Add to this the proliferation of the Internet and the result is a set of fast real-time bespoke solutions and visualisations for many national meterological services and end-users.

An important phase in a nowcasting system is the diagnosis of the forecast variables. This work focuses on the diagnosis of precipitation. The Nimrod automatic nowcasting system at the Met Office (UK) has long used Meteosat visible and infrared data to supplement the data it receives from the UK weather radar network to produce rainfall analyses. With the advent of Meteosat Second Generation (MSG) attention has focused on how best to use the larger range of spectral information from MSG to improve the rainfall analyses. Earlier work at the Met Office had suggested artificial neural networks (ANNs) to be a useful tool for such applications. Pending the availability of data from MSG, ANNs were used to process data from appropriate visible and infrared channels on the MODIS instrument. Sixty daytime winter cases were collected, and Nimrod radar rainfall analyses provided ‘ground truth’ for both training and testing the ANNs. The optimal combination of MODIS channels was investigated and it was found that almost all the skill in rain/no rain discrimination was provided by the radiance values from six selected spectral channels. A notable result was that the 1.64 μm channel had no value as a discriminator when used alone, but produced a large increase in skill when used in conjunction with a visible channel. The ANN with MODIS data was found to outperform the corresponding Nimrod look-up table technique applied to Meteosat data. Application of the technique to SEVIRI data is proposed, as is extension to other seasons.

In Part II of this study the ECMWF Ensemble Prediction System (EPS) is used to study the probabilistic predictability of three major European 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/French storm of October 1987 (Great October storm). Common to all these storms is their severity that caused large loss of life and widespread damage.

In Part I of this study it has been found that deterministic predictability of the Dutch and Hamburg storms amount to 48 and 84 hours, respectively. Here, it is shown that the ensemble forecasts supplement the deterministic forecasts. The large number of members in the 48 and 84 hour ensemble forecasts of the Dutch and Hamburg storms, respectively, suggest that at this forecast range and for these storms the sensitivity of the forecasts to analysis and model uncertainties is rather small. From these results, therefore, it is argued that reliable warnings (i.e. low probability for the occurrence of a forecast failure) for the Dutch and Hamburg storms could have been issued 48 and 84 hours, respectively, in advance, had the current ECMWF EPS been available.

For the Great October storm it has been found in Part I of this study that short-range and medium-range forecasts of the intensity and track of the storm were very skilful with a high-resolution model of the ECWMF model. The actual timing of the storm, however, was difficult to predict. Here, it is shown that the EPS is capable of predicting large forecast uncertainties associated with the timing of the Great October storm up to 4 days in advance. It is argued that reliable warnings could have been issued at least 96 hours in advance had the ECMWF EPS been available.

From the results presented in this study it is concluded that an Ensemble Prediction System is an important component of every early warning system for it allows an a priori quantification of the probability of the occurrence of severe wind storms.

The use of numerical weather forecast model data as a source of data for soil moisture modelling was tested. Results show that the potential evaporation calculated using the Penman-Monteith equation can be estimated accurately using data obtained from the output of a high resolution numerical atmospheric model (HIRLAM, High Resolution Limited Area Model). The mean bias error was 0.26 mm for a 36-hour sum and the root mean square error was 2.14 mm. The evaporation obtained directly from HIRLAM was systematically smaller because this direct model output represents the real evaporation rather than the potential evaporation. The precipitation forecasts were less accurate. When the accuracy of parameters required for the calculation of potential evaporation were studied for one station, no serious bias was found. When two different irrigation models (AMBAV and SWAP) were run over one summer using either measured or HIRLAM data as the input, the results given by the models were quite similar regardless of input data source. The largest differences between the model outputs were caused by the formulation of crop and soil characteristics in the irrigation models.

A wetness duration experiment was carried out within a lily field situated adjacent to coastal dunes in the Netherlands. A within-canopy model was applied to simulate leaf wetness in three layers, with equal leaf area indices, within the canopy. This simulation model is an extension of an existing model. It appeared that in most cases leaf wetness started in the uppermost layer followed by the middle and bottom layer, respectively. The same occurred during the early morning drying process. Just after sunrise the upper layer started to dry, followed by the middle and bottom layer, respectively. The longest leaf wetness duration occurred in the bottom layer. The calculated leaf wetness durations were within 10 minutes of the results obtained using a leaf wetness sensor.

The COST Action 719 started in 2001 and presently 20 European countries are participating. The main objectives of the Action are to establish interfaces between GIS and meteorological data, assess the availability, contents and accessibility of meteorological and climatological data sets and encourage and foster European co-operation.

The tasks are carried out within three working groups concentrated on issues such as data access and availability, methods of spatial interpolation and developing recommendations for standardised GIS applications.

The applications that have been adopted mainly focus on three parameters, i.e. precipitation, temperature and energy balance for which three demonstration projects have been formulated.

It is expected that the Action will result in recommendations for better and more cost-effective production of state-of-the-art meteorological and climatological information. Also an improvement of the co-operation between European countries in the application of GIS in the field of meteorology, climatology and environmental sciences should be achieved together with better-trained personnel within the operational and scientific divisions of national meteorological services.

Additionally, the development of a visualisation system for climate data sets for internet applications is under preparation. This paper provides information concerning the work in progress on the demonstration projects made within COST 719.

In an effort to save money during the summer of 2003, Northern Illinois University (NIU) administrators instituted a four-day working week and stopped air conditioning buildings for the three-day weekends (Friday through Sunday). Shutting down the air conditioning systems caused a noticeable drop in electricity usage for that part of the campus that features in our study, with estimated total electricity savings of 1,268,492 kilowatt-hours or 17% of the average usage during that eight-week period. NIU's air conditioning systems, which relied on evaporative cooling to function, were sensitive to dew point levels. Greatest savings during the shutdown period occurred on days with higher dew points. An examination of the regional dew point climatology (1959–2003) indicated that the average summer daily dew point for 2003 was 14.9°C (58.8°F), which fell in the lowest 20% of the distribution. Based on the relationship between daily average dew points and electrical usage, a predictive model that could estimate electrical daily savings was created. This model suggests that electrical savings related to any future three-day shutdowns over summer could be much greater in more humid summers. Studies like this demonstrate the potential value of applying climatological information and of integrating this information into practical decision-making.

The Internet is increasingly used as a means for exchanging large amounts of spatial data. Professional data centres have found secure ways to share their data resources with peers and third-party users, either as a free service or as a commercial activity. Ranging from a simple download of tables to online queries of distributed SQL databases, the role of the Internet is undeniable. It is obvious that extended security measurements are needed in order to share data over the Internet, such as a DMZ or ‘demilitarized zone’ and firewalls. Increasingly, dedicated server-side applications contribute to a straightforward data retrieval procedure.

There are many cases where observed orographic enhancement has been plausibly attributed to the seeder feeder mechanism. To date, however, there have been few examples of radar data that clearly illustrate the concept of process, in the manner that the data presented here do. In this case study, vertical pointing radar data collected on the windward side of New Zealand's Southern Alps show two separated precipitating systems, with shallow rainfall enhanced by snowfall from aloft. Simple analysis of the synoptic conditions and rainfall data showed that this interaction did result in orographic enhancement. Further evidence of a seeder feeder event is given by simultaneous scanning radar data, which show a marked increase in raining area at the onset of the snowfall.

This study aims to describe the synoptic features that caused the water level in the Río de la Plata estuary to rise above critical levels between December 1989 and December 1998. Floods in the estuary can seriously affect the land beside the river from Punta Indio (35.22°S, 57.17°W) to the Paraná delta, including the lowlands of Buenos Aires City.

Surface pressure patterns associated with floods in the Río de la Plata estuary were obtained by using Quartimax rotated T-mode Principal Component Analysis (PCA) of 1000 hPa geopotential heights. The PCA patterns and 1000 hPa composite and anomaly fields show two main causes for overflows in the Río de la Plata estuary. First, the presence of a surface anticyclone, located south of Buenos Aires province and over northern Patagonia; and, second, cyclogenesis over northeastern Argentina or over Uruguay. The two synoptic features are often present simultaneously.

Two representative points were selected in the area under study: one over the continent at the Aeroparque meteorological station (34.34°S, 58.25°W) and another over the ocean between 36°S, 56°W and 36°S, 50°W. Predominant 1000 hPa wind directions associated with overflows were SE at the former location and SSW at the latter.

Based on the analysis of the obtained fields, the relationship between the estuary overflow and blocking situations and/or positive pressure anomalies over southern South America and adjacent seas was studied. The zonal circulation index (I), used to detect blocking actions, was found to be useful for identifying synoptic situations related to the estuary swelling. The probability of water level rises in the Río de la Plata with a blocking or I>0 at 70°W is 0.48, 0.72, 0.78 and 0.73, for summer, autumn, winter and spring, respectively. When I>0 is observed over the Atlantic at 40°W the probability of flooding is 0.16 for the whole year, but it decreases to 0.028 in autumn, winter and spring.

These results and weather charts from different numerical prediction models show that alerts of possible Río de la Plata estuary overflow can be released five days in advance.

Emerging developments in geographic information systems and distributed computing offer a roadmap towards an unprecedented spatial data infrastructure in the climate sciences. Key to this are the standards developments for digital geographic information being led by the International Organisation for Standardisation (ISO) technical committee on geographic information/geomatics (TC211) and the Open Geospatial Consortium (OGC). These, coupled with the evolution of standardised web services for applications on the internet by the World Wide Web Consortium (W3C), mean that opportunities for both new applications and increased interoperability exist. These are exemplified by the ability to construct ISO-compliant data models that expose legacy data sources through OGC web services. This paper concentrates on the applicability of these standards to climate data by introducing some examples and outlining the challenges ahead. An abstract data model is developed, based on ISO standards, and applied to a range of climate data–both observational and modelled. An OGC Web Map Server interface is constructed for numerical weather prediction (NWP) data stored in legacy data files. A W3C web service for remotely accessing gridded climate data is illustrated. Challenges identified include the following: first, both the ISO and OGC specifications require extensions to support climate data. Secondly, OGC services need to fully comply with W3C web services, and support complex access control. Finally, to achieve real interoperability, broadly accepted community-based semantic data models are required across the range of climate data types. These challenges are being actively pursued, and broad data interoperability for the climate sciences appears within reach.

This paper describes the evaluation of three diagnostic wind models by direct comparison with wind field data. The models are the California Meteorological Model (CALMET), the Mass Consistent model (MCSCIPUF) associated with the Second Order Closure Integrated Puff (SCIPUFF) transport/dispersion model, and the Stationary Wind Field and Turbulence (SWIFT) model. The evaluation follows previous works by Chang, Franzese & Hanna, who compared the same three models, and by Bradley & Mazzola who evaluated SWIFT coupled with SCIPUFF. As with SWIFT, MCSCIPUF is incorporated in the Hazard Prediction and Assessment Capability (HPAC), while CALMET is linked with the California Puff model (CALPUFF), another transport and dispersion model. The Dipole Pride 26 (DP26) experiments, performed at the US Department of Energy (DOE) Nevada Test Site, are used as the source of the wind data. They provide a comprehensive set of meteorological data with wide-ranging atmospheric stability conditions over a complex terrain. Model calculations were compared with measured data in two phases. The first phase uses complete sets of data from eight locations (the 8M phase) as model inputs, and thus tests the ability of the models to reproduce input conditions. In the second phase, five of the measured wind sites are withheld from input, and instead used for validation of model calculations (the 3M phase). In the first phase, the errors were found (with some exceptions) to be quite small. In the second phase, mean absolute errors were found to be of the order of 1 ms−1 and 30°, with only small differences among models in terms of performance.

The story of numerical weather prediction (NWP) is a great story. It deals with the first successful attempts to see into the chaotic future, to predict non-period events. It is to a large extent a story of computers, mathematics and numerical schemes. But the development cannot be fully understood unless non-mathematical and non-technological facts are also included. Meteorological understanding, political considerations and human emotions are difficult to quantify, but they make the picture complete and the narrative more interesting for readers outside the ‘NWP community’.

To obtain representative relationships between rain intensity and radar reflectivity and make an accurate estimation of the rainfall amount in the Baiu season (the most important rainy season in eastern Asia), the precipitation types (convective, stratiform and mixed types) must first be classified. This is because the mechanism and efficiency of precipitation show distinct variations according to rain regimes. An objective classification method, bright band fraction (BBF) that focuses on the vertical structure of radar reflectivity field, was improved and applied to the Baiu precipitation of 1995 in the Kanto Plain, Japan. Samples of convective rainfall were further divided into patterns using an index effective efficiency (Ee) that represents the echo top height. For those types (and patterns) that had been divided, Z-R relations were established using a statistical method called the window probability matching method (WPMM). The shapes of the Z-R relations show a natural matching of the probability density function of radar reflectivity with that of rain intensity recorded by rain gauge. The results show that the classification of precipitation types is reasonable, and Z-R relations corresponding to the precipitation types are representative. Some facts which need special attention in the application of the WPMM are also revealed.

A multimodel short-range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration program on improved high temperature forecasting during the summer of 2003 is evaluated. Results from this short-range ensemble system indicate that using the past complete 12 days of forecasts to bias correct today's forecast yields ensemble mean forecasts of 2-m temperature, 2-m dewpoint temperature, and 10-m wind speed that are competitive with or better than those available from any of the model output statistics presently generated operationally in the United States. However, the bias-corrected ensemble system provides more than just the ensemble mean forecast. Probabilities produced by this system are skilful and reliable, and have been found to be valuable when evaluated in a cost-loss model. The ensemble appears to provide better guidance for more unlikely events, such as very warm temperatures, that likely have the greatest economic significance. Industries that are sensitive to the weather, such as power companies, transportation and agriculture, may benefit from the probability information provided. Thus, it is possible to develop post-processing for short-range ensemble forecasting systems that is competitive with or better than traditional post-processing techniques, thereby allowing the rapid production of useful and accurate guidance forecasts of many near surface variables.

Daily rainfall records at 50 sites across southern Ontario are used to investigate the association of summer regional rainfall with outgoing longwave radiation (OLR) and moisture convergence, from 1991 to 2000. The results suggest that OLR values correspond well to heavy rainfall amounts when rain occurs over more than 90% of the study region. The average regional daily precipitation of 12.1 mm/day corresponds to 213.0 W/m2 of OLR values in the study region. Also, OLR is more significantly associated with the regional daily rainfall amount than moisture convergence in the atmospheric layers between 1000 hPa and 300 hPa although both are associated with precipitation. The study suggests that when a low OLR departure centres over Ontario, the region tends to have extremely heavy rainfall and flooding. The results of this study indicate that OLR is a useful tool for satellite detection of extremely heavy rainfall and possible flash flooding during the summer season over southern Ontario.

We verified the Bureau of Meteorology's seasonal rainfall forecasts for 262 townships throughout Australia, from its inception in June 1997 to May 2005. The results indicate that the forecasting system had low skill. Brier Skill Score and the receiver operating characteristic values were uniformly close to the no skill value. Forecast variances were consistently small. The overall observed variance was 0.0048, 2.1% of the variance of a perfect system. The estimate of the gradient of the outcome against forecast was 0.42 and was imprecise. Definitive statements about bias cannot be made. The value of the forecasts for decision-makers was estimated using value score curves, calculated for six forecast scenarios. All curves indicated that no economic benefit could have been reliably derived by users of the seasonal rainfall forecasts, with the exception of users with decisions triggered by a small shift in the forecast from climatology, in which case small economic gains may have occurred. Small value scores were associated with the observed forecast variance, not the observed bias. We examined the expected change in value scores associated with any future increase in forecast variance. This showed that a moderate increase from the observed variance would bring limited benefits. Substantial value to a broad range of users will only occur with a large increase in forecast variance. To deliver this, new lead indicators with markedly better predictive characteristics may need to be developed for the seasonal rainfall forecasting system.

The Institute of Meteorology and Water Management (IMWM) manages the national hydrological-meteorological service, which maintains the measurement network, processes data, and issues warnings, reports and announcements. There are five divisions of IMWM located around Poland. Each division includes numerous stations and scientific-research departments. The data gathered, processed and analysed by IMWM are space-related; therefore spatial information systems are indispensable for data processing and visualisation. In this paper we discuss the development of the model for GIS applications in the national hydro-meteorological service in Poland (IMWM).