利用非线性观测算子的WRFDA系统同化1D+4D-VAR资料
1D+4D-VAR data assimilation of lightning with WRFDA system using nonlinear observation operators
摘要：
本文讨论了在两种情况下的恶劣天气同化数据从地球网络总闪电网络（ENTLN）的影响。来自ENTLN的数据可以替代即将推出的GOES Lightning Mapper（GLM）。我们使用天气研究和预报（WRF）模式和变分同化技术在9公里的空间分辨率。主要目标是研究未来GLM观测闪电的潜在影响。以前的努力同化闪电观测主要利用轻推的方法。我们开发了三种更复杂的方法，3D-VAR WRFDA和1D+nD-VAR（n= 3，4）WRFDA计划，目前正在考虑由国家环境预测中心（NCEP）和海军研究实验室（NRL）的业务实施。本研究使用对流可用位能（CAPE）作为闪电数据和模型变量之间的代理。为了测试上述方案的性能，我们评估的结果分析和预测的降水相比，从控制实验和验证他们对NCEP第IV阶段降水的质量。结果表明，同化闪电观测改善降水统计同化窗口期间和3-7小时后。1D+4D-VAR方法表现最好，在两种情况下，在同化窗口期间，与对照相比，降水均方根误差显著改善了25%和27.5%。这一发现证实了变分nD-VAR（n= 3，4）同化的闪电观测，提高了湿度分析和预报的准确性。最后，我们简要讨论了固有的局限性，在目前的闪电同化计划，其影响，以及可能的方法来改善它们。
英文摘要：
This paper addresses the impact of assimilating data from the Earth Networks Total Lightning Network (ENTLN) during two cases of severe weather. Data from the ENTLN serve as a substitute for those from the upcoming launch of the GOES Lightning Mapper (GLM). We use the Weather Research and Forecast (WRF) model and variational data assimilation techniques at 9 km spatial resolution. The main goal is to examine the potential impact of lightning observations from the future GLM. Previous efforts to assimilate lightning observations mainly utilized nudging approaches. We develop three more sophisticated approaches, 3D-VAR WRFDA and 1D+nD-VAR (n=3,4) WRFDA schemes that currently are being considered for operational implementation by the National Centers for Environmental Prediction (NCEP) and the Naval Research Laboratory (NRL). This research uses Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. To test the performance of the aforementioned schemes, we assess the quality of resulting analysis and forecasts of precipitation compared to those from a control experiment and verify them against NCEP stage IV precipitation. Results demonstrate that assimilating lightning observations improves precipitation statistics during the assimilation window and for 3-7 h thereafter. The 1D+4D-VAR approach performs best, significantly improving precipitation root mean square errors by 25% and 27.5% compared to the control during the assimilation window on the two cases. This finding confirms that the variational nD-VAR (n=3,4) assimilation of lightning observations improves the accuracy of moisture analyses and forecasts. Finally, we briefly discuss limitations inherent in the current lightning assimilation schemes, their implications, and possible ways to improve them.
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