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Taiwan is located at the junction of the tropical and subtropical climate zones adjacent to the Eurasian continent and Pacific Ocean. The island frequently experiences typhoons that engender severe natural disasters and damage. Therefore, efficiently estimating typhoon rainfall in Taiwan is essential. This study examined the efficacy of typhoon rainfall estimation. Radar images released by the Central Weather Bureau were used to estimate instantaneous rainfall. Additionally, two proposed neural network-based architectures, namely a radar mosaic-based convolutional neural network (RMCNN) and a radar mosaic-based multilayer perceptron (RMMLP), were used to estimate typhoon rainfall, and the commonly applied Marshall&ndash, Palmer Z-R relationship (Z-R_MP) and a reformulated Z-R relationship at each site (Z-R_station) were adopted to construct benchmark models. Monitoring stations in Hualien, Sun Moon Lake, and Taichung were selected as the experimental stations in Eastern, Central, and Western Taiwan, respectively. This study compared the performance of the models in predicting rainfall at the three stations, and the results are outlined as follows: at the Hualien station, the estimations of the RMCNN, RMMLP, Z-R_MP, and Z-R_station models were mostly identical to the observed rainfall, and all models estimated an increase during peak rainfall on the hyetographs, but the peak values were underestimated. At the Sun Moon Lake and Taichung stations, however, the estimations of the four models were considerably inconsistent in terms of overall rainfall rates, peak rainfall, and peak rainfall arrival times on the hyetographs. The relative root mean squared error for overall rainfall rates of all stations was smallest when computed using RMCNN (0.713), followed by those computed using RMMLP (0.848), Z-R_MP (1.030), and Z-R_station (1.392). Moreover, RMCNN yielded the smallest relative error for peak rainfall (0.316), followed by RMMLP (0.379), Z-R_MP (0.402), and Z-R_station (0.688). RMCNN computed the smallest relative error for the peak rainfall arrival time (1.507 h), followed by RMMLP (2.673 h), Z-R_MP (2.917 h), and Z-R_station (3.250 h). The results revealed that the RMCNN model in combination with radar images could efficiently estimate typhoon rainfall.

This study was initiated to validate a tomato powdery mildew, caused by Leveillula taurica (Lev.) Arn., forecast model in Egypt. Environmentally acceptable control may be achieved using a weather based forecasting system due to the reduced use of agrochemicals and their residues in both environment and food. A computerized disease forecast model for tomato powdery mildew (Egy. Tom-PM) as named by the authors was validated during the 2006 and 2007 growing seasons. The micrometeorological parameters monitored hourly in tomato cano- py were ambient air temperature, relative humidity, leaf wetness duration, wind speed and global radiation using a wireless real-time automatic Agro-weather station model (Adcon Telemetry model A733, add wave). Results indicated that Egy.Tom-PM forecast model accurately defined disease risk appearance and reduced number of sprays in both years compared with the routine application (10D) and (7D) sprays in 2006 and 2007 respectively. Basic roles of system analysis for model validation and evaluation are discussed.

The performance of a two-dimensional numerical model of flood hydraulics is tested for a major event in Carlisle, UK, in 2005. This event is associated with a unique data set, with global positioning system surveyed wrack lines and flood extent surveyed 3 weeks after the flood. The simple finite-volume model is used to solve the two-dimensional Saint-Venant equations over an unstructured mesh of 30 000 elements representing channel and floodplain, and allowing detailed hydraulics of flow around bridge piers and other influential features to be represented. The simple finite-volume model is also used to corroborate flows recorded for the event at two gauging stations. Calibration of Manning's n is performed with a two-stage strategy, with channel values determined by calibration of the gauging station models, and floodplain values determined by optimising the fit between model results and observed water levels and flood extent for the 2005 event. The root mean square error for the calibrated model compared with surveyed water levels is of the order of ±0·4 m, the same order of magnitude as the estimated error in the survey data. The study demonstrates the ability of unstructured mesh hydraulic models to represent important hydraulic processes across a range of scales, with potential applications to flood risk management.

Stochastic daily weather time series models (‘‘weather generators’’) are parameterized consistent with both local climate and probabilistic seasonal forecasts. Both single-station weather generators, and spatial networks of coherently operating weather generators, are considered. Only a subset of parameters for individual station models (proportion of wet days, precipitation mean parameters on wet days, and daily temperature means and standard deviations) are found to depend appreciably on the seasonal temperature and precipitation outcomes, so that extension of the single-station models to coherent multisite weather generators is straightforward. The result allows stochastic simulation of multiple daily weather series, conditional on seasonal forecasts. Example applications of spatially integrated extreme daily precipitation and snowpack water content are used to illustrate the method.

The Fiering model for the simultaneous generation of synthetic flows at a key station and one statellite station is reconsidered. After pointing out some inadequacies in Fiering's analysis, the model is reformulated. It is then shown how its parameters may be chosen so as to preserve the historical monthly (say) means and variances at each station, and also the first-serial correlation at each station and the simultaneous cross-correlation. The complete correlation structure of the model is obtained. It is further shown how to preserve the historical skewness coefficients of each flow. Finally, a possible extension involving two satellite stations is briefly mentioned.

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An ideal station model can be said to be one in which the synoptic observations are rapidly and efficiently plotted in such a way that (1) the analyst or forecaster can ascertain, with a minimum of thought or effort, the complete three-dimensional distribution of atmospheric conditions, and the time changes of that distribution, and (2) all meteorological observations made in a given region during a given time interval are readily available to the analyst or forecaster.