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Remote Sens. 2018, 10(4), 625;

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

(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
NASA Marshall Space Flight Center, Redstone Arsenal, Huntsville, AL 35812, USA
Author to whom correspondence should be addressed.
Received: 12 March 2018 / Revised: 13 April 2018 / Accepted: 14 April 2018 / Published: 18 April 2018
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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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. View Full-Text
Keywords: land surface temperature; data assimilation; ensemble Kalman filter (EnKF); ALEXI land surface temperature; data assimilation; ensemble Kalman filter (EnKF); ALEXI

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Fang, L.; Zhan, X.; Hain, C.R.; Yin, J.; Liu, J.; Schull, M.A. An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches. Remote Sens. 2018, 10, 625.

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