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Land surface sensitivity of monsoon depressions formed over Bay of Bengal using improved high-resolution land state
Institution:1. Marine and Atmospheric Sciences Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India;2. Former student of Department of Atmospheric Sciences, Cochin University of Science and Technology, Cochin, India. Presently in Indian Institute of Tropical Meteorology, Pune, India;1. Departament de Física de la Terra i Termodinàmica, Facultat de Física, Universitat de València, Doctor Moliner 50, 46100 Burjassot, Valencia, Spain;2. Departament de Geografia, Facultat de Geografia i Història, Universitat de València, Avda. Blasco Ibáñez 28, 46010 Valencia, Spain
Abstract:Monsoon depressions (MDs) constitute a large fraction of the total rainfall during the Indian summer monsoon season. In this study, the impact of high-resolution land state is addressed by assessing the evolution of inland moving depressions formed over the Bay of Bengal using a mesoscale modeling system. Improved land state is generated using High Resolution Land Data Assimilation System employing Noah-MP land-surface model. Verification of soil moisture using Soil Moisture and Ocean Salinity (SMOS) and soil temperature using tower observations demonstrate promising results. Incorporating high-resolution land state yielded least root mean squared errors with higher correlation coefficient in the surface and mid tropospheric parameters. Rainfall forecasts reveal that simulations are spatially and quantitatively in accordance with observations and provide better skill scores. The improved land surface characteristics have brought about the realistic evolution of surface, mid-tropospheric parameters, vorticity and moist static energy that facilitates the accurate MDs dynamics in the model. Composite moisture budget analysis reveals that the surface evaporation is negligible compared to moisture flux convergence of water vapor, which supplies moisture into the MDs over land. The temporal relationship between rainfall and moisture convergence show high correlation, suggesting a realistic representation of land state help restructure the moisture inflow into the system through rainfall-moisture convergence feedback.
Keywords:Land surface parameterization  Indian monsoon depressions  High resolution land data assimilation (HRLDAS)
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