In 2013, Indian summer monsoon witnessed a very heavy rainfall event (>30 cm/day) over Uttarakhand in north India, claiming more than 5000 lives and property damage worth approximately 40 billion USD. This event was associated with the interaction of two synoptic systems, i.e., intensified subtropical westerly trough over north India and north-westward moving monsoon depression formed over the Bay of Bengal. The event had occurred over highly variable terrain and land surface characteristics. Although global 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 impact of land state conditions in simulating this severe event using a high resolution mesoscale model. The land conditions such as multi-layer soil moisture and soil temperature fields were generated from High Resolution Land Data Assimilation (HRLDAS) modelling system. Two experiments were conducted namely, (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 0.73 and 0.05, whereas for CNTL it is 0.63 and 0.053 respectively, with a stronger heat low in LDAS. The differences in wind and moisture transport in LDAS favoured increased moisture transport from Arabian Sea through a convectively unstable region embedded within two low pressure centers over Arabian Sea and Bay of Bengal. The improvement in rainfall is significantly correlated to the persistent generation of potential vorticity (PV) in LDAS. Further, PV tendency analysis confirmed that the increased generation of PV is due to the enhanced horizontal PV advection component rather than the diabatic heating terms due to modified flow fields. These results suggest that, two different synoptic systems merged by the strong interaction of moving PV columns resulted in the strengthening and further amplification of the system over the region in LDAS. This study highlights the importance of better representation of the land surface fields for improved prediction of localized anomalous weather event over India.
In this study, a comprehensive investigation is carried out to examine the sensitivity of tropospheric relative humidity (RH) on monsoon depressions (MDs) under a changing climate regime through surrogate climate change approach over the Indian region. Composite analysis of four MDs show a persistent warming (RH2+) and cooling (RH2−) throughout the troposphere in the sensitivity experiments. In-depth analysis of a MD over the Arabian Sea (AS) exhibits sustained warming for RH2+, which is accredited to 2.6% increase in stratiform clouds accounting for 13% increment in heating, whereas 5% increment in convective clouds hardly contribute to total heating. Frozen hydrometeors (graupel and snow) are speculated to be the major contributors to this heating. Stratiform clouds showed greater sensitivity to RH perturbations in the lower troposphere (1000–750 hPa), albeit very less sensitivity for convective clouds, both in the lower and mid-troposphere (700–500 hPa). Precipitation is enhanced in a moist situation (RH2+) owing to positive feedbacks induced by moisture influx and precipitation efficiency, while negative feedbacks suppressed precipitation in a dry troposphere (RH2−). In a nutshell, it is inferred that under moist (dry) situations, it is highly likely that intense (weak) MDs will occur in the near future over the Indian region.
Sea Surface Temperature (SST) is crucial for the development and maintenance of a tropical cyclone (TC) particularly below the storm core region. However, storm data below the core region is the most difficult to obtain, hence it is not clear yet that how sensitive the radial distribution of the SST impact the storm characteristic features such as its inner-core structures, translational speed, track, rainfall and intensity particularly over the Bay of Bengal. To explore the effects of radial SST distribution on the TC characteristics, a series of numerical experiments were carried out by modifying the SST at different radial extents using two-way interactive, triply-nested, nonhydrostatic Advanced Weather Research and Forecast (WRF-ARW) model. It is found that not only the SST under the eyewall (core region) contribute significantly to modulate storm track, translational speed and intensity, but also those outside the eyewall region (i.e., 2-2.5 times the radius of maximum wind (RMW)) play a vital role in defining the storm's characteristics and structure. Out of all the simulated experiments, storm where the positive radial change of SST inducted within the 75 km of the storm core (i.e., P75) produced the strongest storm. In addition, N300 (negative radial changes at 300 km) produced the weakest storm. Further, it is found that SST, stronger within 2-2.5 times of the RMW for P75 experiment, plays a dominant role in maintaining 10 m wind speed (WS 10), surface entropy flux (SEF) and upward vertical velocity (w) within the eyewall with warmer air temperature (T) and equivalent potential temperature (θ e) within the storm's eye compared to other experiments.
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