Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial–temporal analyses of rainfall have been studied by using 107 (1901–2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.

We compare coincident thermospheric neutral wind observations made by the Michelson Interferometer for Global High‐Resolution Thermospheric Imaging (MIGHTI) on the Ionospheric Connection Explorer (ICON) spacecraft, and four ground‐based specular meteor radars (SMRs). Using the green‐line MIGHTI channel, we analyze 1158 coincidences between Dec 2019 and May 2020 in the altitude range from 94 to 104 km where the observations overlap. We find that the two datasets are strongly correlated (r = 0.82) with a small mean difference (4.5 m/s). Although this agreement is good, an analysis of known error sources (e.g., shot noise, calibration errors, and analysis assumptions) can only account for about a quarter of the disagreement variance. The unexplained variance is 27.8% of the total signal variance and could be caused by unknown errors. However, based on an analysis of the spatial and temporal averaging of the two measurement modalities, we suggest that some of the disagreement is likely caused by temporal variability of the wind on scales ≲70 min. The observed magnitudes agree well during the night, but during the day, MIGHTI observes 16%–25% faster winds than the SMRs. This remains unresolved but is similar in certain ways to previous SMR‐satellite comparisons.

The spatial and seasonal variability of the vertical structure of precipitation has been studied using 15 years of Tropical Rainfall Measuring Mission's Precipitation Radar (TRMM PR) version 7 data over India and adjoining oceans. Special emphasis has been put on six different climatic rain regimes and on different types of precipitation including the virga rain. The distribution of reflectivity factor (Z) above the freezing level height is broader in northwest India (NWI) and narrower over the Arabian Sea and west coast of India (ASWC) than in other selected regions, due to dominance of deep and shallow convective rain, respectively, in those regions. The height variation of contours in normalized distributions for Z indicates that evaporation of raindrops (low-level hydrometeor growth) could be significant in NWI (ASWC and Bay of Bengal). All the above features show clear seasonal variation and are observed predominantly during the southwest monsoon. The occurrence of virga rain clearly shows land-ocean contrast (less over the oceans) and seasonal variation (preponderant during premonsoon). Among different rain categories, the stratiform (convective) rain had highest (lowest) fraction of virga rain of >15–30% (<10%) over land regions. 1. The storm height (SH) vertical distributions show a peak in the vicinity of bright band (BB) in all regions, except for those regions and seasons, where convective precipitation is dominant. The well-defined BB feature and SH exhibit significant seasonal and regional variations, which are linked to variations in the occurrence of stratiform rain and height of BB. The spatial and seasonal variations of mean SH and the occurrence of deep and overshooting convective rain show good correspondence with the spatial variation of convective available potential energy.