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Creators/Authors contains: "Osuri, Krishna K."

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  1. Abstract

    This study aims to (i) prepare a premonsoon thunderstorms database, (ii) understand the thunderstorm frequency, duration and intensity and (iii) composite analysis of dynamic and thermodynamic processes related to thunderstorms over India. The thunderstorm associated rainfall varies across India. Hence, a percentile‐based approach is implemented with the integrated multi‐satellite retrievals for global precipitation measurement (IMERG) dataset at 0.1° resolution to identify thunderstorms for 2001–2021. The 93rd percentile appears to be better for thunderstorm detection, with a success ratio of 82% (642 events are confirmed out of 786 detected) in eastern India. Further analysis indicated that 84% of the detected thunderstorms in eastern and northeastern India are associated with lightning activity. Based on this long‐term (2001–2021) thunderstorm data, the highest frequency of thunderstorms (40–45 events·year−1) is observed over the western foothills of the Himalayas, the northeast region, and the west coast of Kerala. The thunderstorm duration in the eastern and northeastern regions and the southwest coast of India is mostly 0.5–2.5 h, producing heavy rainfall (>7 mm·h−1) due to more moisture content and stronger updrafts. The composite structure of thermodynamic indices exhibits significant spatial variations over India and can be used to differentiate the regions of high thunderstorm activity. The minimum (maximum) convective available potential energy (convective inhibition) value required for thunderstorm development is not uniform throughout the country. However, the composites of K index and total totals index during thunderstorms are mostly uniform. This study highlights the benefits of IMERG rainfall in thunderstorm detection over India and helps to understand the local forcings and the effect of thunderstorm activity on different sectors like aviation, agriculture and so forth.

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  2. Abstract

    This study aimed to understand the microphysical processes that affect rapid intensity changes of tropical cyclones (TCs) over the Bay of Bengal (BoB). Four representative TCs were simulated using the Weather Research and Forecasting model with storm tracking nested configuration (at 9‐km and 3‐km resolution). Results indicate that the inner‐core heating strongly correlated (r > 0.85) with the precipitated compared to non‐precipitated hydrometeors. Furthermore, the vertical distribution of hydrometeors and heating is dependent on inner‐core updrafts and relative humidity. A novel composite analysis of microphysical processes indicates that the warmer (2 K) inner core is close to saturation (>90%) with excess water vapor (>2–3 × 10−3 kg·kg−1), which enhances the latent heat release (LHR) through condensation below the freezing level during the rapid intensification (RI) onset. In addition, during RI, strong updrafts transport the water vapor (>2 × 10−3 kg·kg−1) and cloud liquid water (2.5 × 10−4 kg·kg−1) to above freezing level, and enhance the LHR because of deposition and freezing respectively. The increased precipitating particles in the saturated inner core also enhance LHR. The symmetric convection structured by the atmospheric moisture causes the formation of prolonged RI episodes, as seen in TCPhailin. During rapid weakening (RW), asymmetric and relatively fewer hydrometeors are evident, along with the presence of weak updrafts and strong shear. The dry‐air intrusion into the inner core also causes the cooling processes (evaporation and sublimation). The enhancement or reduction of moist static energy and potential vorticity is associated with increased or reduced LHR in the TC rapid intensity changes.

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  3. Abstract

    Taking the examples of Hurricane Florence (2018) over the Carolinas and Hurricane Harvey (2017) over the Texas Gulf Coast, the study attempts to understand the performance of slab, single‐layer Urban Canopy Model (UCM), and Building Environment Parameterization (BEP) in simulating hurricane rainfall using the Weather Research and Forecasting (WRF) model. The WRF model simulations showed that for an intense, large‐scale event such as a hurricane, the model quantitative precipitation forecast over the urban domain was sensitive to the model urban physics. The spatial and temporal verification using the modified Kling‐Gupta efficiency and Method for Object based Diagnostic and Evaluation in Time Domain suggests that UCM performance is superior to the BEP scheme. Additionally, using the BEP urban physics scheme over UCM for landfalling hurricane rainfall simulations has helped simulate heavy rainfall hotspots.

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  4. Abstract

    This study investigates the impact of direct versus indirect initialization of soil moisture (SM) and soil temperature (ST) on monsoon depressions (MDs) and heavy rainfall simulations over India. SM/ST products obtained from high‐resolution, land data assimilation system (LDAS) are used in the direct initialization of land surface conditions in the ARW modeling system. In the indirect method, the initial SM is sequentially adjusted through the flux‐adjusting surface data assimilation system (FASDAS). These two approaches are compared with a control experiment (CNTL) involving climatological SM/ST conditions for eight MDs at 4‐km horizontal resolution. The surface fields simulated by the LDAS run showed the highest agreement, followed by FASDAS for relatively dry June cases, but the error is high (~15–30%) for the relatively wet August cases. The moisture budget indicates that moisture convergence and local influence contributed more to rainfall. The surface‐rainfall feedback analysis reveals that surface conditions and evaporation have a dominant impact on the rainfall simulation and these couplings are notable in LDAS runs. The contiguous rain area (CRA) method indicates better performance of LDAS for very heavy rainfall distribution, and the location (ETS > 0.2), compared to FASDAS and CNTL. The pattern error contributes the maximum to the total rainfall error, and the displacement error is more in August cases' rainfall than that in June cases. Overall analyses indicated that the role of land conditions is significantly high in the drier month (June) than a wet month (August), and direct initialization of SM/ST fields yielded improved MD and heavy rain simulations.

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