High Resolution Soil Moisture and Temperature assimilation over Indian Monsoon region under National Monsoon Mission (NMM) Project: Impact study on Monsoon Depressions

Monsoon Depressions (MD) are the most important rain producing systems among others during south west monsoon season (June–September) over India. The Indian monsoon region (IMR) is recognized as one of the major ‘hot spots' for soil moisture variations that significantly influence precipitation. As of now, high resolution gridded soil moisture and soil temperature (SM/ST) observations do not exist at regional scales, particularly for the Indian domain and incorporation/assimilation of point SM/ST observations in high resolution mesoscale model is complicated because of spatiotemporal variations in land surface conditions such as topography, soil texture, and vegetation characteristics. Therefore, an attempt is made to prepare SM/ST gridded profiles with depth on the exact forecast-model grid points of the study domain. For this purpose, a High-Resolution Land Data Assimilation System (HRLDAS, hereafter LDAS), based on Noah land surface model (LSM) is utilized to prepare the SM/ST fields. The atmospheric forcing data (such as 2m temperature and mixing ratio, 10m winds, surface pressure, model elevation, surface total rainfall rate, downward short wave radiation at surface and, downward long wave radiation)are derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis (Rienecker et al. 2011) of National Aeronautics and Space Administration (NASA). Rainfall analysis is obtained from TRMM data. The initial land surface fields are derived from global data assimilation system (GDAS) analyses of National Centers for Environmental Prediction (NCEP). The land surface characteristics such as land use/land cover, soil texture, and green vegetation fraction are derived from United States Geological Survey (USGS). The Advanced Research Weather Research and Forecasting (ARW) model is used to simulate a number of MDs during 2007–2013over the Indian region. A set of two runs are conducted by deriving surface conditions from climatological SM/ST (known as CNTL experiment) and HRLDAS-based SM/ST (LDAS). Results indicate that the HRLDAS provides more realisticheterogeneity in SM/ST fields and can be improved the overall simulation of MDsif used as surface conditions as compared with that of climatological fields. The movement of MDs in CNTL runs is resulted in larger errors. However, the error associated with MD movementis significantly reduced with the incorporation of HRLDAS-based SM/ST profiles at different depths. An improvement of about 26%, 25%, and 24% at 24, 48, and 72 hour forecast length are achieved, respectively. LDASrun improved the portioning of the surface energy fluxes and the boundary layer instability with high equivalent potential temperature and strong updrafts at the correct time as compared to CNTL run. It also improved the vertically integrated moisture transport in the boundary layer with the better representation of underlying surface conditions. Hence, high resolution land surface conditions are essential for improved simulation of heavy rainfall episodes both in terms of location and intensity during monsoon season. In addition to the above objective and following the conclusions of earlier studies that the rainfall is more crucial for accurate preparation of SM/ST products, HRLDAS is initialized with another rainfall data sources, viz., gridded CMORPHS analyses. Detailed verification of these SM/ST profiles (pointwise and gridded) in comparison to SM/ST data based on TRMM rainfall will be carried out with the available in-situ and satellite-based observations. Impact of these two datasets on simulation of MDs is also discussed during the conference.