Fang Li, Zhan Xiwu, Hain Christopher R, Yin Jifu, Liu Jicheng, Schull Mitchell A
(National Oceanic and Atmospheric Administration) NOAA/ (National Environmental Satellite, Data, and Information Service) NESDIS/ (Center for Satellite Applications and Research) STAR, 5830 University Research Court, College Park, MD 20740, USA.
Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Court, College Park, MD 20740, USA.
Remote Sens (Basel). 2018;10(4):625. doi: 10.3390/rs10040625. Epub 2018 Apr 18.
Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an "observation operator" that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation.
近期研究表明，卫星观测的陆地表面热红外（TIR）信息（如皮肤温度）具有独特价值，且将陆地表面温度（LST）同化到陆地表面模型（LSMs）中以改善陆气水和能量交换模拟具有可行性。在本研究中，实施了两种不同类型的LST同化技术，并比较了这些技术带来的益处。其中一种技术是使用集合卡尔曼滤波器（EnKF）数据同化（DA）工具直接同化LST。另一种是使用大气-陆地交换反演模型（ALEXI）作为“观测算子”，该模型基于实际蒸散与潜在蒸散之比（fPET）将LST反演结果转换为土壤湿度（SM）代理值，然后将其同化到LSM中。虽然目前大多数研究在将LST直接同化以及将ALEXI SM代理值同化到离线LSMs方面都取得了一些成功，但通过耦合数值天气预报（NWP）模型同化TIR信息的潜在影响尚不清楚。在本研究中，采用了一个半耦合的陆地信息系统（LIS）和天气研究与预报（WRF）系统，以评估两种不同技术同化来自美国国家海洋和大气管理局（NOAA）地球同步环境监测卫星（GOES）的TIR观测数据对WRF模型预报的影响。美国国家航空航天局（NASA）的LIS配备了多种LSMs和先进的数据同化工具（如集合卡尔曼滤波器（EnKF）），它获取WRF模型运行的大气强迫数据，通过同化基于LST或TIR的SM生成更新的初始陆地表面条件，并将其返回给WRF以初始化预报。使用通过TIR数据同化每日更新的初始化数据进行的WRF预报，与地面气象观测和再分析产品进行了对比评估。结果发现，基于LST的SM同化的WRF预报与地面气象观测的一致性比直接LST同化或不进行陆地TIR数据同化的预报更好。
