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首页> 外文会议>Remote sensing and modeling of the atmosphere, oceans, and interactions III >Downscaled Multi-model Superensemble and Probabilistic Forecasts of Seasonal Rains Over the Asian Monsoon Belt
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Downscaled Multi-model Superensemble and Probabilistic Forecasts of Seasonal Rains Over the Asian Monsoon Belt

机译：亚洲季风带季节性降雨的缩减多模式超级组合和概率预报

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This is a study on seasonal climate forecasts for the Asian Monsoon region. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state of the art coupled atmosphere-ocean models. A downscaling component, with respect to observed rainfall estimates uses data sets from TRMM and a dense rain gauge distriburion; this enables the forecasts of each model to be bias corrected to a common 25 km resolution. The downscaling statistics for each model, at each grid location is developed during a training phase of the model forecasts; the forecasts from all of the member models use the downscaling coefficients of the training phase. These forecasts are next used for the construction of a multimodel superensemble. A major result of this paper is on the climatology of the model rainfall. From the downscaled multimodel superensemble which shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology.
机译：这是对亚洲季风地区季节性气候预报的研究。这项研究的独特之处在于，可以使用多达16种最先进的大气-海洋耦合模型的预测结果。关于观测到的降雨估算，降尺度部分使用来自TRMM的数据集和密集的雨量计分布；这样就可以将每个模型的预测值偏差校正为25 km的通用分辨率。在模型预测的训练阶段，将开发每个网格位置的每个模型的按比例缩小统计数据；所有成员模型的预测都使用训练阶段的缩减系数。这些预测接下来将用于构建多模型超级集合。本文的主要结果是模型降雨的气候学。从缩小的多模型超级合奏中可以看出，与观测到的气候学相关性接近1.0。这项高技能对于解决降雨异常预报非常重要，该预报是根据与观测（而不是基于模型）气候学的偏差定义的。

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