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.

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.

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.

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.

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.

Since the start of field station manufacturing approximately 25 years ago, the configuration of Road Weather Information System (RWIS) stations and the type of sensors used has changed very little. Little is known about the variation of climatic variables from the 2 m level, where instruments are normally placed, down to the road surface. The ‘Road Climate Room’ can be defined as the volume of air, road and surroundings influencing the conditions on the road. This study attempts to describe the Road Climate Room both theoretically and experimentally. Mobile measurements as well as a permanent station were used. At the permanent station detailed temperature measurements above and beside the road were made. These were related to other climatic variables such as wind, cloudiness, humidity and ground heat flux. The results show that the most important processes occur below the 10 cm level. The air column above this level is well represented by the 2 m level temperature sensor. For most situations the cooling of the air closest to the ground is much more intense in the vegetation than over the road. The temperature difference can be as large as 8 °C and can be represented statistically with high determination by wind and cloudiness. The heat storage in the road is a key factor for keeping the temperature of the road high throughout a full diurnal cycle. The cold air in the vegetation can be considered as a potential source for cold air drainage onto the road surface in terrain where that is a problem. No advection affecting the road surface was observed at the permanent station. This suggests that the Road Climate Room can be defined as being well within the internal boundary layer of the road. The mobile measurements, however, show that advection from the surroundings is likely to have taken place, which makes the definition of the Road Climate Room more difficult. Further studies are needed to fully understand the complicated processes.

Used to predict icy stretches of road during winter nights, thermal mapping is the measurement and visualisation of road surface temperatures for road ice prediction. From its development in the 1970s, the accuracy of thermal mapping has been subject to numerous systematic and random errors. One source of error is the effect of traffic, which is known to have a considerable influence on road surface temperatures. However, as the effects have never been fully quantified, the full implications for thermal mapping are unknown. In this study, consistent temperature differences of around 1.5 °C were found across the different lanes and carriageways of a multi-laned road. The thermal differences are significant and can be directly attributed to traffic heat fluxes.

The Meteorological Office of the United Kingdom (UKMO) began its operational NWP in November 1965. This marked the start of very successful activity that would gradually bring it to the forefront of NWP development, where it stands today.