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The authors gratefully acknowledge the financial support (ECR/2016/001637) of SERB, Department of Science and Technology (DST), Govt. of India, Earth System Science Organization, Ministry of Earth Sciences (MoES/16/14/2014-RDEAS), Govt. of India. The authors also acknowledge the SERB-Purdue OVDF programme (SB/S9/Z-03/2017) for partial support. DN acknowledges the U.S. NSF grants: OAC-1835739, AGS-1522494, and USDA Hatch Project at Purdue University. The authors thank the INCOIS, Hyderabad for providing the GODAS product through the website.

This study focuses on improving the vortex of Tropical Cyclone (TC) using different initialization techniques and its subsequent impact on the performance of Advanced Research Weather Research and Forecasting (ARW) mesoscale model. The Initialization of TC vortex from global analyses (CNTL) is found to be poor due to coarse horizontal and vertical structure, and is improved by assimilating available observations using 3DVAR technique (3DV). In another experiment (VAR_VI), vortex is corrected using vortex initialization and relocation procedures to correspond with India Meteorological Department (IMD) ‘observed’ position and intensity estimates, and then assimilation of additional observations is undertaken. The initial vortex improvement in terms of horizontal and vertical structure is noted in both experiments with the VAR_VI results being better than for the 3DV experiment. Simulations following the VAR_VI experiment showed remarkable improvement in simulating track, intensity and structure of the two TCs it was evaluated for (TC Giri and TC Jal). The large scale dynamical and thermo-dynamical fields such as steering flow, vertical wind shear and warm core structure were also improved. For TC Giri, the VAR_VI could reproduce the observed intensification rate of 10 ms−1 within 12 h, while for TC Jal, the VAR_VI run could simulate the inland rainfall due to sheared convective clouds. Results suggested that the improvements would likely to be more for stronger TCs. This study highlights the continued need and value of improvement of TC vortex using initialization and in-situ and remote sensing observations over the Bay of Bengal region.

The Bay of Bengal (BoB) is a distinct oceanic region for mesoscale oceanic eddies. The sea level anomaly from the Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) help to identify an unusual anti-cyclonic eddy (ACE) over head BoB during May–July 2014. Two Indian moored buoys (BD08 and BD09) located over this region aided to study the subsurface thermohaline structures of the ACE. Compared to no-eddy environment, the temperature and salinity showed an increment of ~ 3–4 °C and ~ 1–2 PSU, respectively, during the ACE life period. The temperature and depth of the isothermal layer at genesis (peak) stages are increased to ~ 30 °C (~ 30.7 °C) and ~ 20 m (30 m) when compared with no-eddy conditions (28.2 °C and 10 m). The thermocline depth is deepened to 75 m at the peak stage, while it is 50 m in no-eddy condition. A temperature difference of 3 °C between no-eddy and peak stages of ACE is observed up to 50 m. The ocean heat content (OHC) at BD08 (BD09) during genesis and peak stages has increased by ~ 72% (~ 50%) and ~ 247% (~ 181%), respectively, when compared with no-eddy conditions. Moreover, the MOHC also shows a similar increment of ~ 125% (~ 123%) and ~ 258% (~ 284%), respectively. A noticeable influence is seen in turbulent fluxes and lower atmospheric variables during eddy life. This study highlights the capability of moored buoys in understanding the subsurface thermohaline features of the eddies over northern BoB.

The hydrological extremes due to the landfalling tropical cyclones (TCs) pose a severe threat to the coastal communities over the North Indian Ocean (NIO) region. Advanced Research Weather Research and Forecasting (ARW) model is obscure in rainfall prediction, while it is extensively evaluated for track and intensity prediction over the NIO. This study focuses on estimating the model rainfall errors based on a total of 280 TC forecast cases from 42 TCs from 2007 to 2018. The model rainfall errors are studied against rain gauge and Tropical Rainfall Measuring Mission (TRMM) data as a function of TC intensity stage and model forecast length. The short-range (24 h) rainfall guidance yields fewer errors than the long-range (48–96 h) forecast when the model is initialized at any TC intensity stage. The root mean square error (RMSE) and bias of ARW rainfall is higher when the model is initialized at weaker intensity (DD or CS) stages than initialized at stronger intensity (SCS and VSCS) stages. The inland rainfall errors increase with forecast lead. The model exhibited higher errors (~2 mm h−1) in the inner-core region (0–100 km) and lesser errors (~0.5 mm h−1) in the TC environment (200–400 km). The ARW model replicates the observed radial profiles of rainfall up to 400 km with 2–5 mm h−1 overestimation at different intensity stages. Rainfall error decomposition of contiguous rain area (CRA) analysis indicates that the pattern errors contribute the maximum (~50%) to the total error, followed by the displacement error (~35%). In comparison, the volume and rotational errors are less (10% and 2%, respectively). The mean CRA horizontal shift in rainfall decreases from weaker to stronger stage initialization. The radial-distance error of categorical rainfall distribution between the ARW model and TRMM is ~ 150–200 km. This error reduced to 20–50 km after correcting the model rainfall for CRA displacement errors. The RMSE of model-rainfall after CRA correction has reduced by 3–15% for any forecast length. This study helps to advance the regional hydrological model’s ability by improved model rainfall inputs at 72–96 h lead.

The impact of urban changes from 1989 to 2015 on urban rainstorms is assessed for the rapidly expanding Indian city of Hyderabad. A typical event that occurred during 12–14 June 2015 is simulated, and the possible association between altered urban-sprawl and shifts in the rainfall timing and patterns are analyzed. The simulation results revealed that the consideration of urban footprint changes was necessary to correctly simulate the heavy rainfall in the high-resolution Weather Research and Forecasting (WRF) model. With the urban expansion and associated anthropogenic heating (AH), the rainfall was more distributed, and its occurrence delayed by ~1–2 h. The results suggest that urbanization can affect rainfall both in terms of the timing as well as the location with respect to the urbanized area. The urban downwind region received an increased rainfall, and this spatial shift was more pronounced with increasing urbanization - from being over the urban area to over rural-urban boundary downwind of the city. The analysis of hydrometeors indicated that rain and cloud quantity decreased with an increasing AH in the urban area. Study results highlight the possibility that the change in the rainfall timing is another climatological signature of urban modification of rainfall.