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The application of numerical weather prediction(NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous(yes/no), and probabilistic techniques over Iran for the period 2008–16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation.The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation,NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations.Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality. 相似文献
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Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems.Recently,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public use.This study evaluated the ensemble forecasts of three C3S models over the period 1993-2017 in Iran’s eight classified precipitation clusters for one-to three-month lead times.Probabilistic and non-probabilistic criteria were used for evaluation.Furthermore,the skill of selected models was analyzed in dry and wet periods in different precipitation clusters.The results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill scores.All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons.Moreover,with increasing lead time,the forecast skill of the models worsened.In terms of forecasting in dry and wet years,the forecasts of the models were generally close to observations,albeit they underestimated several severe dry periods and overestimated a few wet periods.Moreover,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models.In general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran.For the clusters considered in Iran and for the long-range system versions considered,the Météo France model had lower skill than the other models. 相似文献
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The integration of a two-dimensional, raster-based rainfall–runoff model, CASC2D, with a raster geographical information system (GIS), GRASS, offers enhanced capabilities for analysing the hydrological impact under a variety of land management scenarios. The spatially varied components of the watershed, such as slope, soil texture, surface roughness and land-use disturbance, were characterized in GRASS at a user-specified grid cell resolution for input into the CASC2D model. CASC2D is a raster-based, single-event rainfall–runoff model that divides the watershed into grid cell elements and simulates the hydrological processes of infiltration, overland flow and channel flow in response to distributed rainfall precipitation. The five-step integration of CASC2D and GRASS demonstrates the potential for analysing spatially and temporally varied hydrological processes within a 50 square mile semi-arid watershed. By defining possible land-use disturbance scenarios for the watershed, a variety of rainfall–runoff events were simulated to determine the changes in watershed response under varying disturbance and rainfall conditions. Additionally, spatially distributed infiltration outputs derived from the simulations were analysed in GRASS to determine the variability of hydrological change within the watershed. Grid cell computational capabilities in GRASS allow the user to combine the scenario simulation outputs with other distributed watershed parameters to develop complex maps depicting potential areas of hydrological sensitivity. This GIS–hydrological model integration provides valuable spatial information to researchers and managers concerned with the study and effects of land-use on hydrological response. 相似文献
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