Improved Simulation of Monsoon Depressions and Heavy Rains from Direct and Indirect Initialization of Soil Moisture over India

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By dniyogi@purdue.edu, Raghavendra Raju Nadimpalli, Krishna K Osuri1, Kumar Ankur2, H P Nayak3, U C Mohanty3, A K Das4

1. Dept. of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, India 2. Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Alabama, USA 3. School of Earth Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, India 4. Numerical Weather Prediction Division, India Meteorological Division, New Delhi, India

Each file contains various meteorological fields such as air temperature, rainfall, moisture, rainfall, and surface fields (soil moisture and soil temperature).

Version 1.0 - published on 22 Jun 2020 doi:10.4231/5XRA-JZ80 - cite this Content may change until committed to the archive on 22 Jul 2020

Licensed under CC0 1.0 Universal

Brochure_LDAS-release.jpg

Description

This study investigates the impact of direct versus indirect initialization of soil moisture and soil temperature (SM/ST) on monsoon depressions (MDs) and heavy rainfall simulations over India. SM/ST products obtained from high-resolution, land data assimilation system (LDAS) are used in the direct initialization of land surface conditions in the ARW modeling system. In the indirect method, the initial SM is sequentially adjusted through the flux-adjusting surface data assimilation system (FASDAS). These two approaches are compared with a control experiment (CNTL) involving climatological SM/ST conditions for eight MDs at 4-km horizontal resolution.

The surface fields simulated by the LDAS run showed the highest agreement, followed by FASDAS for relatively dry June cases, but the error is high (~15-30%) for the relatively wet August cases. The moisture budget indicates that moisture convergence and local influence contributed more to rainfall. The surface-rainfall feedback analysis reveals that surface conditions and evaporation have a dominant impact on the rainfall simulation, and these couplings are notable in LDAS runs. The contiguous rain area (CRA) method indicates better performance of LDAS for very heavy rainfall distribution, and the location (ETS>0.2), compared to FASDAS and CNTL. The pattern error contributes the maximum to the total rainfall error, and the displacement error is more in August cases’ rainfall than that in June cases. Overall analyses indicated that the role of land conditions is significantly high in the drier month (June) than a wet month (August), and direct initialization of SM/ST fields yielded improved MD and heavy rain simulations.

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Version 1

The data repository contains the data simulated by WRF from three different experiments i.e CNTL (with climatological SMST), LDAS (with hrldas produced SMST) and FASDAS (with flux adjusted indirect SMST fields). Each file contains various meteorological fields such as air temperature, rainfall, moisture, rainfall, and surface fields (soil moisture and soil temperature). The data sets are used to compute and plot the various graphics and the diagnostic analysis such as surface-rainfall feedback and moisture budget etc,  presented in the research article entitled "Improved Simulation of Monsoon Depressions and Heavy Rains from Direct and Indirect Initialization of Soil Moisture over India

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