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This study assesses a Continuous Data Assimilation (CDA)dynamical-downscaling algorithm for enhancing the simulation of the Indiansummer monsoon (ISM) system. CDA is a mathematically rigorous technique thathas been recently introduced to constrain the large-scale features ofhigh-resolution atmospheric models with coarse spatial scale data. It issimilar to spectral nudging but does not require any spectral decomposition forscales separation. This is expected to be particularly relevant for ISM, whichinvolves various interactions between large-scale circulations and regionalphysical processes. Along with a control simulation, several downscalingsimulations were conducted with the Weather Research and Forecasting (WRF)model using CDA, spectral (retaining different wavenumbers) and grid nudgingfor three ISM seasons: normal (2016), excess (2013), and drought (2009). Thesimulations are nested within the NCEP Final Analysis and the model outputs areevaluated against the observations. Compared to grid and spectral nudging, thesimulations using CDA produce enhanced ISM features over the Indiansubcontinent including the low-level jet, tropical easterly jet, easterly windshear, and rainfall distributions for all investigated ISM seasons. The majorISM processes, in particular the monsoon inversion over the Arabian Sea,tropospheric temperature gradients and moist static energy over central India,and zonal wind shear over the monsoon region, are all better simulated withCDA. Spectral nudging outputs are found to be sensitive to the choice of thewavenumber, requiring careful tuning to provide robust simulations of the ISMsystem. In contrast, control and grid nudging generally fail to well reproducesome of the main ISM features.

Enhanced Simulation of the Indian Summer Monsoon Rainfall Using Regional Climate Modeling and Continuous Data Assimilation