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Abstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities. 

Abstract This study investigates the influence of land surface processes on short-spell monsoonal heavy rainfall events under varying soil wetness conditions in India, using the Weather Research and Forecasting Model coupled with two land surface schemes: Noah and SLAB. To represent contrasting soil conditions, four rainfall events are chosen, two in onset (June) and two in active (August) months, during the Indian summer monsoon season. The results indicate that rainfall sensitivity differs notably between onset and active cases. Specifically, in onset, the SLAB overpredicts rainfall to the north of the storm compared to the Noah. The northward displacement of rainfall is attributed to the sensitivity of evapotranspiration to the preferential soil moisture regime in onset. Furthermore, the higher surface air saturation deficit in onset favors plant transpiration, resulting in increased boundary layer moisture. This contributes to enhanced moist static energy, thereby affecting potential vorticity and precipitation. In contrast, evapotranspiration sensitivity is modest during active months, under wet soil and lower surface air saturation deficit conditions. The study reveals the distinct soil moisture feedback mechanisms during the onset and active phases, through variations in evapotranspiration sensitivity. Variations in soil moisture and surface air saturation deficit in these phases play a crucial role in modulating evapotranspiration, which in turn affects precipitation. These findings underscore the importance of land surface initialization and land data assimilation in land–atmosphere interaction studies. 

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. 

This study assessed two different vortex initialization (VI) and relocation methods for improved prediction of tropical cyclones (TCs) over the Bay of Bengal (BoB) using the triply nested (27/9/3 km) state-of-the-art Hurricane Weather Research and Forecasting (HWRF) model. The first VI method, “cold-start,” obtained the initial TC vortex from the global analysis. The second one, “cyclic-start,” received the initial vortex from the 6-h forecast of the previous forecast cycle of the same model. In both the strategies, the vortex was corrected to the position, strength, and structure defined by the India Meteorological Department. A total of 32 forecast cases (from five cyclones) over the BoB were considered. The cyclic-start experiments yielded better initial structure and asymmetry as compared to the cold-start experiments. The average statistics indicated that the cyclic initialization improved the 24-h track prediction (by 29%), while the cold initialization was better for the 72-h prediction (by ~ 28%). The intensity was consistently improved in the cyclic-start experiment by up to 68%. The number of cyclic initializations depended on the TC duration. On average, the cyclic initialization improved the representation (strength and size) of the initial vortex up to nine cycles after the first cold start and exhibited an improved skill of 25%; beyond nine cycles, the skill improvement was only 12%. Diagnostic analyses of very severe cyclonic storm (VSCS) Phailin (rapidly intensified) and VSCS Lehar (rapidly weakening) revealed that the cyclic initialization realistically represented equivalent potential temperature, upper-level cloud condensate, and moisture intrusion, which improved the model performance. This study brought out the benefit of the (cyclic) VI for improved TC prediction capabilities in the BoB basin. 

Abstract Extreme flooding over southern Louisiana in mid-August of 2016 resulted from an unusual tropical low that formed and intensified over land. We used numerical experiments to highlight the role of the ‘Brown Ocean’ effect (where saturated soils function similar to a warm ocean surface) on intensification and it’s modulation by land cover change. A numerical modeling experiment that successfully captured the flood event (control) was modified to alter moisture availability by converting wetlands to open water, wet croplands, and dry croplands. Storm evolution in the control experiment with wet antecedent soils most resembles tropical lows that form and intensify over oceans. Irrespective of soil moisture conditions, conversion of wetlands to croplands reduced storm intensity, and also, non-saturated soils reduced rain by 20% and caused shorter durations of high intensity wind conditions. Developing agricultural croplands and more so restoring wetlands and not converting them into open water can impede intensification of tropical systems that affect the area.