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首页> 外文期刊>ournal of the Meteorological Society of Japan >Displaced Ensemble Variational Assimilation Method to Incorporate Microwave Imager Brightness Temperatures into a Cloud-resolving Model
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Displaced Ensemble Variational Assimilation Method to Incorporate Microwave Imager Brightness Temperatures into a Cloud-resolving Model

机译：将微波成像仪亮度温度纳入云解析模型的位移集合变分同化方法

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The goal of the present study is to develop a method to assimilate Microwave Imager (MWI) brightness temperatures (TBs) into Cloud-Resolving Models (CRMs). To address the non-linear relationship of TBs to the state variables of CRM and the flow-dependency of the CRM forecast error covariance, we adopted an ensemble-based variational data assimilation method. However, there often exist large-scale displacement errors of rainy areas between the observation and CRM forecasts. In such cases, ensemble-based data assimilation can give erroneous analysis, particularly for observed rain areas without forecasted rain. In order to solve this problem, we propose ensemble-based assimilation that uses ensemble forecast error covariance with displacement error correction. Based on this idea, we developed a data assimilation method that incorporates the MWI TBs into the CRM developed by the Japan Meteorological Agency (JMANHM). This method consists of a displacement error correction scheme and an ensemble-based variational assimilation scheme. In the displacement error correction scheme, we obtained the optimum displacement that maximized the conditional probability of TB observation given the displaced CRM variables. In the assimilation scheme, we derived a cost function in the displaced ensemble forecast error subspace. Then, we obtained analyses of CRM variables by non-linear minimization of the cost function. In order to see the impact of the above MWI TB assimilation method on CRM analyses and forecasts, we performed assimilation experiments to incorporate TMI (TRMM Microwave Imager) low-frequency TBs (10, 19, and 21 GHz with vertical polarization) into the CRM for a typhoon case around Okinawa (9th June 2004). The results of the experiments show that the assimilation of TMI TBs alleviated the large-scale displacement errors and improved CRM forecasts. The displacement error correction also avoided misinterpretation of MWI TB increments due to precipitation displacements as those from other variables in the assimilation scheme.
机译：本研究的目的是开发一种将微波成像仪（MWI）亮度温度（TBs）吸收到云解析模型（CRM）中的方法。为了解决结核菌与CRM状态变量之间的非线性关系以及CRM预测误差协方差的流量依赖性，我们采用了基于集合的变异数据同化方法。但是，在观测和CRM预测之间经常会出现雨区的大规模位移误差。在这种情况下，基于集合的数据同化可能会提供错误的分析，尤其是对于没有预测降雨的观测降雨区域。为了解决此问题，我们提出了基于整体的同化方法，该方法使用整体预测误差协方差和位移误差校正。基于此想法，我们开发了一种数据同化方法，将MWI TB纳入了由日本气象厅（JMANHM）开发的CRM中。该方法包括位移误差校正方案和基于整体的变分同化方案。在位移误差校正方案中，我们获得了最佳位移，该位移在给定了CRM变量的情况下最大化了TB观测的条件概率。在同化方案中，我们在置换集合预测误差子空间中导出了一个成本函数。然后，我们通过成本函数的非线性最小化对CRM变量进行了分析。为了了解上述MWI TB同化方法对CRM分析和预测的影响，我们进行了同化实验，将TMI（TRMM微波成像仪）低频TB（垂直极化的10、19和21 GHz）纳入了CRM （2004年6月9日）。实验结果表明，TMI TB的同化减轻了大规模的位移误差并改善了CRM预测。位移误差校正还避免了由于降水位移引起的MWI TB增量与同化方案中其他变量的误解。

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