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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.

Accurate estimation of the planetary boundary layer (PBL) top is essential for air quality prediction, weather forecast, and assessment of regional and global climate models. In this article, the long-term climatology of seasonal, global distribution of PBL is presented by using global positioning system radio occultation (GPSRO) based payloads such as Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), Communication/Navigation Outage Forecast System (C/NOFS), TerraSAR-X, and The Gravity Recovery and Climate Experiment (GRACE) from the year 2006-2015. We used Wavelet Covariance Transform (WCT) technique for precise PBL top identification. The derived PBL top from GPSRO data is rigorously evaluated with GPS radiosonde data over Gadanki. Significant seasonal variation is noticed in both radiosonde and GPSRO observations. Further, we compared the PBL obtained GPS RO with global radiosonde network and observed very good correlation. The number of occultations reaching down to 500 m and retrieval rate of PBL top from WCT method is very high in mid-latitudes compared to tropical latitudes. The global distribution of PBL top shows significant seasonal variation with higher during summer followed by spring, fall, and minimum in winter. In the vicinity of Inter Tropical Convergence Zone (ITCZ), the PBL top is high over eastern Pacific compared to other regions. The ERA-Interim reanalysis data underestimate the PBL top compared to GPS RO observations due to different measurement techniques. The seasonal variation of global averaged PBL top over land and ocean shows contrasting features at different latitude bands. [ABSTRACT FROM AUTHOR]