Global warming is causing more extreme precipitation events and variability in rainfall patterns, which are affecting the timing of peak rainfall occurrence and impacting the hydrological cycle in evolving climates. This study examines spatial rainfall trends across India during the Indian Summer Monsoon (ISM) period from 2001 to 2020, utilizing GSMaP_ISRO data. Analysis reveals a significant rise of 2 mm/day in rainfall over west central India from 2011 to 2020. Additionally, an investigation into the timing of peak rainfall demonstrates an early (late) peak over the Indo-Gangetic Plain (central India) in recent years, likely influenced by changes in aerosol loading. The present results suggest a significant shift in rainfall peak time with potential implications for agriculture,hydrology and disaster management.

This study aims to create a 21-year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland. The targeted resolutions of the GSMaP are hourly and 0.1°× 0.1°. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) daily gauge analysis (0.5° × 0.5°) and Indian Meteorological Department (IMD) daily gridded rainfall product (0.25° × 0.25°) were utilized to generate two long-term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon (ISM) periods of 2000–2020 with comparisons of other merged rainfall products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results suggest GSMaP_IMD has a smaller root-mean-square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the three-hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense rain gauge network in Karnataka suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation over India. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

The well recognized constraint of non-linear and non-Gaussian distribution of rainfall observation limits its assimilation in the high-dimensional numerical weather prediction (NWP) model. In this study, rain-gauges’ observed rainfall from Indian Meteorological Department (IMD) over Indian landmass is assimilated in the Weather Research and Forecasting (WRF) model using particle filter. In the framework of imperfect weather model, particles (or ensembles) for rainfall predictions are created with various combinations of model physics (viz. cumulus parameterization, micro-physics, planetary boundary layer schemes). With the help of IMD observed rainfall, weights are provided to various particles using multiple hypotheses, and this is the step in which IMD rainfall observations are used for assimilation. Further, a resampling step is performed to generate new particle from high weight particle using stochastic kinetic-energy backscatter scheme (SKEBS) method in which dynamical variables are perturbed into the model physics. Results based on rainfall verification scores suggest that assimilation of the rain-gauges observed rainfall using particle filter improved prediction of rainfall over CNT runs (unweighted particle; without assimilation). Moreover, surface and vertical profile of temperature, water vapour mixing ratio (WVMR) and wind speed are also improved in 24 h forecasts.

The all-sky Infrared (IR) radiance assimilation from geostationary satellites has been a prime research area in the numerical weather prediction (NWP) modeling. In this study, the variational data assimilation system of the weather research and forecasting (WRF) model has been customized to assimilate all-sky assimilation of water vapour (WV) radiance from Imager onboard two geostationary Indian National Satellites (INSAT-3D and INSAT-3DR). This study also integrated different hydrometeors (like cloud, rain, ice, snow and graupel) as control variables in the WRF variation assimilation system. To do this, parallel experiments were performed by carrying out model simulations with and without INSAT WV radiance assimilation during July 2018. Results of these simulations suggested that the WRF model analyses for all-sky assimilation are closer to the brightness temperature (T­B) of channel-1 (183.31 ± 0.2 GHz) of SAPHIR (Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie) sensor onboard Megha-Tropiques satellite and channel-3 (183.31 ± 1.0 GHz) of MHS (Microwave Humidity Sounder) sensor onboard National Oceanic and Atmospheric Administration (NOAA-18/19) and Meteorological Operational Satellite (MetOp-A/B/C) satellites. Furthermore, noteworthy changes are noticed in hydrometeors analyses with all-sky assimilation and the number of assimilated observations are increased significantly (around 2.5 times). The short-range predictions from all-sky assimilation runs revealed notable positive impact as compared to clear-sky assimilation runs when verified with SAPHIR and MHS T­B, and NCEP (National Centers for Environmental Prediction) final analysis.