Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations
Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations
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通过使用加性系综扰动同化天气雷达反射率来改进短期降雨预报
DOI：
10.1029/2018jd028723
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
and E. Sato
中科院分区：
文献类型：
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作者：
Yokota;S.;H. Seko;M. Kunii;H. Yamauchi;and E. Sato
为了通过直接同化雷达反射率来改善短期降雨预报，与降雨相关的大气变量应该根据它们与反射率的相关性进行修改。然而，很难估计这种相关性。集合卡尔曼滤波器可以通过集合预报来估计相关性，尽管这种估计仅限于在分析点至少有一个成员预报降雨时。为了有效地同化反射率，即使在没有降雨预报的点，我们建议增加集合反射率扰动，这是与大气变量，集合卡尔曼滤波同化之前。在本研究中，这种相关性计算在整个计算域，包括降雨区。我们将该程序应用于2012年5月6日和2013年9月2日发生的两个龙卷风超级单体的水平网格间隔为1 km的同化实验，并且我们通过修改风、温度和水蒸气成功地改善了短期降雨预报。
To improve short‐term rainfall forecasts through direct assimilation of radar reflectivity, atmospheric variables associated with rainfall should be modified based on their correlation with reflectivity. However, it is difficult to estimate such correlations. The ensemble Kalman filter can estimate the correlation by means of ensemble forecasts, although the estimation is limited to when rainfall is forecast by at least one member at analysis points. To assimilate reflectivity effectively even at points at which no rainfall is forecast, we suggest adding ensemble reflectivity perturbations, which are correlated with atmospheric variables, before ensemble Kalman filter assimilation. In the present study, this correlation is calculated in the whole computational domain including the rainfall regions. We apply this procedure to assimilation experiments with 1‐km horizontal grid interval for two tornadic supercells that occurred on 6 May 2012 and on 2 September 2013, and we succeed in improving short‐term rainfall forecasts by modifying wind, temperature, and water vapor.