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An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches

机译：使用两种不同的数据同化方法评估陆地热红外观测对区域天气预报的影响

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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.
机译：最近的研究表明，卫星观测的地表热红外（TIR）信息（例如皮肤温度）的独特价值以及将地表温度（LST）吸收到地表模型（LSM）中以改善对地表温度的模拟的可行性。大气中的水和能量交换。在这项研究中，实施了两种不同类型的LST同化技术，并比较了该技术带来的好处。其中一种技术是使用集成卡尔曼滤波器（EnKF）数据同化（DA）实用程序直接同化LST。另一种是将大气-土地交换反演模型（ALEXI）用作“观测算子”，该模型根据实际蒸散量与潜在蒸散量（fPET）的比率将LST检索结果转换为土壤水分（SM）代理，然后将其同化进入LSM。尽管大多数最新研究表明，在将LST直接同化和将ALEXI SM代理直接同化到离线LSM方面都取得了一些成功，但尚不清楚通过耦合数值天气预报（NWP）模型对TIR信息进行同化的潜在影响。在这项研究中，采用了半耦合土地信息系统（LIS）和天气研究与预报（WRF）系统来评估两种不同技术对NOAA GOES卫星的TIR观测结果同化对WRF模型预报的影响。 NASA LIS配备了多种LSM和先进的数据同化工具（例如，集合卡尔曼滤波器（EnKF）），可从WRF模型运行中获取大气强迫数据，并通过任一LST-同化来生成更新的初始地面条件。或基于TIR的SM，然后将其返回给WRF以初始化预测。使用每日更新的初始化和TIR数据同化的WRF预测是针对地面天气观测和再分析产品进行评估的。结果发现，与基于LST的SM同化的WRF预报比与具有直接LST的同化或不具有陆地TIR数据同化的WRF预报与地面天气观测的一致性更好。

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