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Verification of ensemble forecasts of Mediterranean high-impact weather events against satellite observations

机译：根据卫星观测结果验证地中海高影响天气事件的总体预报

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摘要

Ensemble forecasts at kilometre scale of two severe storms over the Mediterranean region are verified against satellite observations. In complement to assessing the forecasts against ground-based measurements, brightness temperature (BT) images are computed from forecast fields and directly compared to BTs observed from satellite. The so-called model-to-satellite approach is very effective in identifying systematic errors in the prediction of cloud cover for BTs in the infrared window and in verifying the forecasted convective activity with BTs in the microwave range. This approach is combined with the calculation of meteorological scores for an objective evaluation of ensemble forecasts. The application of the approach is shown in the context of two Mediterranean case studies, a tropical-like storm and a heavy precipitating event. Assessment of cloud cover and convective activity using satellite observations in the infrared (10.8 μm) and microwave regions (183-191 GHz) provides results consistent with other traditional methods using rainfall measurements. In addition, for the tropical-like storm, differences among forecasts occur much earlier in terms of cloud cover and deep convective activity than they do in terms of deepening and track. Further, the underdispersion of the ensemble forecasts of the two high-impact weather events is easily identified with satellite diagnostics. This suggests that such an approach could be a useful method for verifying ensemble forecasts, particularly in data-sparse regions.
机译：根据卫星观测结果，对地中海地区两次大暴风雨的千米级集合预报进行了验证。作为对基于地面测量的评估进行评估的补充，亮度温度（BT）图像是根据预测字段计算的，并直接与从卫星观测到的BT进行比较。所谓的“从模型到卫星”方法在识别红外窗中BTs的云量预测中以及在微波范围内验证BTs的对流活动时非常有效。这种方法与气象得分的计算相结合，可以对总体预报进行客观评估。该方法的应用在两个地中海案例研究的背景下得以展示，这是一场热带风暴和一次强降水事件。使用卫星在红外（10.8μm）和微波区域（183-191 GHz）的观测评估云量和对流活动，其结果与使用降雨测量的其他传统方法一致。此外，对于类似热带的风暴，预报的差异在云量和对流活动方面比在加深和追踪方面要早得多。此外，利用卫星诊断程序可以轻松地识别出两个高影响天气事件的集合预报的欠散。这表明，这种方法可能是验证总体预报的有用方法，尤其是在数据稀疏地区。

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