Performance Evaluation of High-Resolution Land Data Assimilation System (HRLDAS) Over Indian Region
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The present study evaluates the skill of a High-Resolution Land Data Assimilation System (HRLDAS) in simulating soil moisture (SM), soil temperature (ST) and sensible heat flux (SHF) for the Indian region (5°–39°N; 60°–100°E). The HRLDAS framework uses uncoupled Noah Land Surface Model (LSM) that integrates near-surface atmospheric parameters and land surface parameters from observations and analysis for the period January 2001–October 2013 at 20 km spatial resolution. The HRLDAS takes about 1 year to reach its quasi-equilibrium state for clay soil. The HRLDAS simulated ST and SM reasonably agree with the in situ observations. The simulated ST shows a negative bias in the monsoon season over the Gujarat, Mandla, and Kharagpur. The SM is under-estimated and the under-estimation increases with soil depth at Kharagpur, India. The negative bias in TRMM precipitation forcing causes under-estimation of SM. The simulated SM shown higher saturation point than observations. The daytime SHF has positive bias during the pre-monsoon, monsoon seasons and agrees well with observations in the post-monsoon season at Ranchi, India. The Noah 1D sensitivity experiments revealed that there is a need to revisit soil field capacity and porosity parameter for improving the skill of the HRLDAS.
KeywordsLand data assimilation soil moisture soil temperature sensible heat flux
The Indian Space Research Organization (ISRO) and Ministry of Earth Sciences (MoES), Government of India is sincerely acknowledged for providing financial support to conduct this research. Department of Science and Technology (DST) and MoES, Govt. of India is acknowledged for providing financial support in establishment and maintenance of the micro-meteorological tower facility. Global Modeling and Assimilation Office (GMAO) and Goddard Earth Science Data and Information Service Center (GES DISC) is sincerely acknowledged for providing Modern Era Retrospective-Analysis for Research and Applications (MERRA) and Global Land Data Assimilation (GLDAS) data, respectively. National Centers for Environmental Prediction (NCEP) is acknowledged for providing NCEP final analysis. National Aeronautics Space Administration is acknowledged for 3 hourly satellite rainfall estimates. Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) is acknowledged for providing in situ observations. The authors are very much thankful to Dr. Manoj Kumar for providing Ranchi flux observation. The first author is indebted to IIT Kharagpur for providing facilities to conduct his PhD work. The first author is very much grateful to late Dr. M. Mandal for his guidance and support at the initial stage of this research. The authors are thankful to the anonymous reviewers for their valuable suggestions and comments.
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