A new comprehensive surface temperature data set for India is used to document changes in Indian temperature over seven decades, in order to examine the patterns and possible effects of global warming. The data set is subdivided into pre-monsoon, monsoon, and post-monsoon categories in order to study the temperature patterns in each of these periods. When the decade means in maximum, minimum and daily mean temperature for the 2000s are compared to those of the 1950s, a consistent pattern of warming is found over northwestern and southern India, and a pattern of cooling is seen in a broad zone anchored over northeastern India and extending southwestward across central India. These patterns are explained by the presence of a large region of anthropogenic brown haze over India and adjacent ocean regions. These aerosols absorb solar radiation, leading to warming of the haze layer over northeastern and central India and to cooling of the surface air beneath. The heated air rises and then sinks to the north and south of the haze region over northwestern and southern India, warming the air by compression as it sinks in those regions. The possible impact of these temperature patterns on Indian agriculture is considered.

A series of numerical experiments are carried out to investigate the sensitivity of a landfalling monsoon depression to land surface conditions using the Weather Research and Forecasting (WRF) model. Results suggest that precipitation is largely modulated by moisture influx and precipitation efficiency. Three cloud microphysical schemes (WSM6, WDM6, and Morrison) are examined, and Morrison is chosen for assessing the land surface‐precipitation feedback analysis, owing to better precipitation forecast skills. It is found that increased soil moisture facilitates Moisture Flux Convergence (MFC) with reduced moisture influx, whereas a reduced soil moisture condition facilitates moisture influx but not MFC. A higher Moist Static Energy (MSE) is noted due to increased evapotranspiration in an elevated moisture scenario which enhances moist convection. As opposed to moist surface, sensible heat dominates in a reduced moisture scenario, ensued by an overall reduction in MSE throughout the Planetary Boundary Layer (PBL). Stability analysis shows that Convective Available Potential Energy (CAPE) is comparable in magnitude for both increased and decreased moisture scenarios, whereas Convective Inhibition (CIN) shows increased values for the reduced moisture scenario as a consequence of drier atmosphere leading to suppression of convection. Simulations carried out with various fixed soil moisture levels indicate that the overall precipitation features of the storm are characterized by initial soil moisture condition, but precipitation intensity at any instant is modulated by soil moisture availability. Overall results based on this case study suggest that antecedent soil moisture plays a crucial role in modulating precipitation distribution and intensity of a monsoon depression.

The Advanced Weather Research and Forecasting (WRF‐ARW) model is used to carry out 3 simulations to examine the response of varying resolution of sea surface temperature (SST) on tropical cyclone (TC) characteristics up to 72 hours lead time. They are named as CNTL (Control, with default SST), SST1 (with SST data at 0.5^o×0.5^o), and SST2 (with SST data at 0.083^o×0.083^o) respectively. It is found that the cooler SST (~0.2−0.4 K) within the effective radius (i.e. 2.5 times the radius of maximum wind) of the storm leads to a weaker storm (about 7 hPa) for SST2 than CNTL. The storm size in SST2 is about 5 km larger than CNTL during 24 to 72 forecast hours. The reduced storm size and enhanced diabatic heating at the core region facilitating stronger storm for CNTL compare to others. As a result, the central pressure of TC deepens further with a stronger radial pressure gradient, leading to rapid intensification in CNTL than others. Daily update of SST has improved the storm track and intensity by 37% and 41% for SST2 and 9% and 20% for SST1 respectively in comparison to CNTL. The study demonstrates that SST with the higher horizontal resolution (0.083^o×0.083^o) serves as useful input parameter for the model to improve the prediction of cyclone's track and intensity.

Predicting heavy rainfall events (HREs) in real time poses a significant challenge in India, particularly in complex terrain regions like Assam, where these hydro-meteorological events frequently associated with flash floods with severe consequences over region. The devastating HREs in June 2022 led to numerous casualties, extensive damage, and economic losses exceeding 200 crore, necessitating the evacuation of over 4 million individuals. As we write this paper Assam again going through immense flooding situation in now i.e. June2023. Due to the limitations of deterministic numerical weather models in accurately forecasting these events, the study explores the incorporation of deep learning (DL) models, specifically U-Nets, using simulated daily accumulated rainfall outputs from various parametrization schemes. Over a four-day period in June 2022, the U-Net based model demonstrated superior skills in predicting rainfall at the district scale, achieving a Mean Absolute Error (MAE) of less than 12mm, outperforming individual and ensemble model outputs. Comparing the DL model’s performance to the Weather Research and Forecasting (WRF) forecasts, it exhibited a remarkable 64.78% reduction in MAE across Assam. Notably, the proposed model accurately predicted HREs in specific districts such as Barpeta, Kamrup, Kokrajhar, and Nalbari, showcasing improved spatial variation compared to the WRF model. The DL model’s predictions aligned with actual rainfall (> 150 mm) observations from the India Meteorological Department (IMD), while the WRF forecasts consistently underestimated rainfall intensity (< 100 mm). Furthermore, the proposed model achieved a high prediction accuracy of 77.9% in categorical rainfall prediction, significantly outperforming the WRF schemes by 38.1%.