Next Article in Journal
Multi-Temporal DInSAR to Characterise Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu (DR Congo)
Next Article in Special Issue
Optimizing Window Length for Turbulent Heat Flux Calculations from Airborne Eddy Covariance Measurements under Near Neutral to Unstable Atmospheric Stability Conditions
Previous Article in Journal
Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images
Previous Article in Special Issue
Projecting Climate and Land Use Change Impacts on Actual Evapotranspiration for the Narmada River Basin in Central India in the Future
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(4), 625; https://doi.org/10.3390/rs10040625

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

1
(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
2
Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Court, College Park, MD 20740, USA
3
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)
Full-Text   |   PDF [22984 KB, uploaded 3 May 2018]   |  

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. View Full-Text
Keywords: land surface temperature; data assimilation; ensemble Kalman filter (EnKF); ALEXI land surface temperature; data assimilation; ensemble Kalman filter (EnKF); ALEXI
Figures

Graphical abstract

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top