In 2013, Indian summer monsoon witnessed a very heavy rainfall event (>30 cm/day) over Uttarakhandin north India, claiming more than 5000 lives and property damage worth approximately 40 billionUSD. This event was associated with the interaction of two synoptic systems, i.e., intensified subtropicalwesterly trough over north India and north-westward moving monsoon depression formed over the Bayof Bengal. The event had occurred over highly variable terrain and land surface characteristics. Althoughglobal models predicted the large scale event, they failed to predict realistic location, timing, amount,intensity and distribution of rainfall over the region. The goal of this study is to assess the impactof land state conditions in simulating this severe event using a high resolution mesoscale model. Theland conditions such as multi-layer soil moisture and soil temperature fields were generated from HighResolution Land Data Assimilation (HRLDAS) modelling system. Two experiments were conductednamely, (1) CNTL (Control, without land data assimilation) and (2) LDAS, with land data assimilation(i.e., with HRLDAS-based soil moisture and temperature fields) using Weather Research and Forecasting(WRF) modelling system. Initial soil moisture correlation and root mean square error for LDAS is 0.73and 0.05, whereas for CNTL it is 0.63 and 0.053 respectively, with a stronger heat low in LDAS. Thedifferences in wind and moisture transport in LDAS favoured increased moisture transport from ArabianSea through a convectively unstable region embedded within two low pressure centers over Arabian Seaand Bay of Bengal. The improvement in rainfall is significantly correlated to the persistent generation ofpotential vorticity (PV) in LDAS. Further, PV tendency analysis confirmed that the increased generationof PV is due to the enhanced horizontal PV advection component rather than the diabatic heatingterms due to modified flow fields. These results suggest that, two different synoptic systems merged bythe strong interaction of moving PV columns resulted in the strengthening and further amplificationof the system over the region in LDAS. This study highlights the importance of better representation ofthe land surface fields for improved prediction of localized anomalous weather event over India.
Volume 129, 2020
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