Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms
Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms
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DOI：
10.1175/mwr-d-19-0163.1
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发表时间：
2019-11
影响因子：
3.2
通讯作者：
Yunji Zhang;D. Stensrud;Fuqing Zhang
中科院分区：
文献类型：
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作者：
Yunji Zhang;D. Stensrud;Fuqing Zhang
本研究探讨了同化红外（IR）亮温（BT）观测从地球同步卫星联合径向速度（Vr）和反射率（Z）观测多普勒天气雷达在集合卡尔曼滤波（EnKF）数据同化系统的对流允许集合分析和预测2017年6月12日横跨怀俄明州和内布拉斯加州的龙卷风超级单体雷暴事件的好处。虽然雷达观测以高保真度对三维风暴结构进行采样，但BT观测在风暴内雷达观测尚不可用时提供关于降水粒子形成之前的云的信息，并且还提供关于雷暴外部环境的信息。为了更好地了解每种观测类型的优势和局限性，分别和联合同化了卫星和多普勒雷达观测，并将集合分析和预报与现有观测进行了比较。结果表明，同化BT观测有可能增加预报和预警的前置时间的恶劣天气事件相比，雷达观测，也可能补充稀疏的地面观测在某些地区所揭示的概率预测中气旋路径初始化从EnKF分析作为不同的时间。此外，同化的BT和Vr观测结果产生最好的集合预报，提供更高的信心，提高精度，和更长的前置时间的概率预测中层中气旋。
This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when in-storm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones.