Reale Oreste, McGrath-Spangler Erica L, McCarty Will, Holdaway Daniel, Gelaro Ronald
Global Modeling and Assimilation Office, NASA, Greenbelt, Maryland, USA.
Weather Forecast. 2018 Aug;33(4):909-931. doi: 10.1175/waf-d-17-0175.1. Epub 2018 Jun 20.
A simple adaptive thinning methodology for Atmospheric Infrared Sounder (AIRS) radiances is evaluated through a combination of Observing System Experiments (OSEs) and adjoint methodologies. The OSEs are performed with the NASA Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast model. In addition, the adjoint-based forecast sensitivity observation impact technique is applied to assess fractional contributions of sensors in different thinning configurations. The adaptive strategy uses a denser AIRS coverage in a moving domain centered around tropical cyclones (TCs), sparser everywhere else. The OSEs consist of two sets of data assimilation runs that cover the period from September 1st to 10 November 2014, with the first 20 days discarded for spin-up. Both sets assimilate all conventional and satellite observations used operationally. In addition, one ingests clear-sky AIRS radiances, the other cloud-cleared radiances, each comprising multiple thinning strategies. Daily 7-day forecasts are initialized from all these analyses and evaluated with focus on TCs over the Atlantic and the Pacific. Evidence is provided on the effectiveness of this simple TC-centered adaptive radiance thinning strategy, in full agreement with previous theoretical studies. Specifically, global skill increases, and tropical cyclone representation is substantially improved. The improvement is particularly strong when cloud-cleared radiances are assimilated. Finally, the article suggests that cloud-cleared radiances, if thinned more aggressively than the currently used clear-sky radiances, could successfully replace them with large improvements in TC forecasting and no loss of global skill.
通过观测系统实验（OSE）和伴随方法相结合，对一种用于大气红外探测器（AIRS）辐射率的简单自适应稀疏化方法进行了评估。OSE使用美国国家航空航天局戈达德地球观测系统（GEOS，版本5）的数据同化和预报模型来进行。此外，基于伴随的预报敏感性观测影响技术被用于评估不同稀疏配置下传感器的分数贡献。自适应策略在以热带气旋（TC）为中心的移动区域使用更密集的AIRS覆盖，在其他地方则更稀疏。OSE包括两组数据同化运行，涵盖2014年9月1日至11月10日期间，前20天的数据被舍弃用于初始化。两组都同化了业务上使用的所有常规和卫星观测数据。此外，一组摄取晴空AIRS辐射率，另一组摄取云清除后的辐射率，每组都包含多种稀疏策略。所有这些分析都被用于初始化每日7天的预报，并重点对大西洋和太平洋上空的热带气旋进行评估。证据表明这种以热带气旋为中心的简单自适应辐射率稀疏化策略是有效的，这与之前的理论研究完全一致。具体而言，全球技能提高，热带气旋的表现得到显著改善。当同化云清除后的辐射率时，这种改善尤为明显。最后，文章表明，如果云清除后的辐射率比目前使用的晴空辐射率更积极地进行稀疏化，那么它们可以成功取代晴空辐射率，在热带气旋预报方面有大幅改善，且不会损失全球技能。
