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

Li Fang,Xiwu Zhan,Jifu Yin,Christopher R Hain,Mitchell A Schull,Jicheng Liu

doi:10.3390/rs10040625

Abstract

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.

Highlights

Land surface temperature (LST, or “skin temperature”), as a key parameter of the Earth’s surface energy balance, is one of critical variables in weather and climate models [1,2,3]
The largest impact can be detected over the southwest after Atmosphere-Land Exchange Inversion model (ALEXI) soil moisture (SM) assimilation, in the Southern Mountain Region and Southern West Coast, where surface temperature forecast drops on the order of 2 K–3 K and relative humidity rises around 3%
Accurate forecasts of numerical weather prediction models rely on the quality of the initialization of land surface state variables

Summary

Introduction

Land surface temperature (LST, or “skin temperature”), as a key parameter of the Earth’s surface energy balance, is one of critical variables in weather and climate models [1,2,3]. Accurate LST information is of great value to potentially improving the surface-atmosphere water and energy balance and upper level temperature and humidity, and all in turn to improve the accuracy of weather and climate forecasts. Real-time satellite products are capable of providing spatially-continuous observations of surface parameters while accurately capturing the dynamics of surface conditions. Satellite retrievals of LST are widely available from thermal infrared (TIR) sensors onboard on polar-orbiting and geostationary platforms. TIR observations from the GOES series are collected to be assimilated into the weather forecast model

Objectives

Results

Conclusion