Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

IF 6.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Advances in Atmospheric Sciences Pub Date : 2023-12-06 DOI:10.1007/s00376-023-2340-2

Rong Kong, Ming Xue, Edward R. Mansell, Chengsi Liu, Alexandre O. Fierro

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引用次数: 0

Abstract

Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series” (GOES-R) Geostationary Lightning Mapper (GLM) flash extent density (FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter (GSI-EnKF) framework were previously developed and tested with a mesoscale convective system (MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational (EnVar) hybrid data assimilation (DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar (PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.

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在GSI 3DVar、EnKF和混合En3DVar中同化GOES-R同步闪电地图闪度密度数据用于一次超级细胞风暴的分析和短期预报

在网格点统计插值集成卡尔曼滤波(GSI-EnKF)框架内，同化地球静止运行环境卫星“r系列”(GOES-R)地球静止闪电绘图仪(GLM)闪光范围密度(FED)数据的能力之前已经开发并在中尺度对流系统(MCS)情况下进行了测试。在本研究中，进一步开发了这种能力，以便在GSI集成-变分(EnVar)混合数据同化(DA)框架中吸收GOES GLM FED数据。用3DVar和纯En3DVar (PEn3DVar，使用100%集合协方差，不使用静态协方差)同化GLM FED数据的结果与EnKF/DfEnKF同化超级单体风暴的结果进行了比较。本研究的重点是验证新实现的正确性和评估性能，而不是比较不同数据处理方案中的FED数据处理性能。只检查了3DVar和pEn3DVar的结果，并与EnKF/DfEnKF进行了比较。对单次FED观测的同化表明，PEn3DVar分析增量的幅度和水平程度普遍大于EnKF，这主要是由于EnFK/DfEnKF和PEn3DVar采用不同的定位策略以及观测算子中霰质量的积分限制所致。总体而言，PEn3DVar的预测性能与EnKF/DfEnKF相当，表明正确实现。

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来源期刊

Advances in Atmospheric Sciences 地学-气象与大气科学

CiteScore

9.30

自引率

5.20%

发文量

154

审稿时长

6 months

期刊介绍： Advances in Atmospheric Sciences, launched in 1984, aims to rapidly publish original scientific papers on the dynamics, physics and chemistry of the atmosphere and ocean. It covers the latest achievements and developments in the atmospheric sciences, including marine meteorology and meteorology-associated geophysics, as well as the theoretical and practical aspects of these disciplines. Papers on weather systems, numerical weather prediction, climate dynamics and variability, satellite meteorology, remote sensing, air chemistry and the boundary layer, clouds and weather modification, can be found in the journal. Papers describing the application of new mathematics or new instruments are also collected here.