Error characterization is vital for the advancement of precipitation algorithms, the evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) has been a benchmark for successive Global Precipitation Measurement (GPM) based products. This has given way to the evolution of many multi-satellite precipitation products. This study evaluates the performance of the newly released multi-satellite Multi-Source Weighted-Ensemble Precipitation (MSWEP) product, whose temporal variability was determined based on several data products including TMPA 3B42 RT. The evaluation was conducted over India with respect to the IMD-gauge-based rainfall for pre-monsoon, monsoon, and post monsoon seasons at daily scale for a 35-year (1979–2013) period. The rainfall climatology is examined over India and over four geographical extents within India known to be subject to uniform rainfall. The performance evaluation of rainfall time series was carried out. In addition to this, the performance of the product over different rainfall classes was evaluated along with the contribution of each class to the total rainfall. Further, seasonal evaluation of the MSWEP products was based on the categorical and volumetric indices from the contingency table. Upon evaluation it was observed that the MSWEP products show large errors in detecting the higher quantiles of rainfall (>75th and > 95th quantiles). The MSWEP precipitation product available at a 0.25° × 0.25° spatial resolution and daily temporal resolution matched well with the daily IMD rainfall over India. Overall results suggest that a suitable region and season-dependent bias correction is essential before its integration in hydrological applications. While the MSWEP was observed to perform well for daily rainfall, it suffered from poor detection capabilities for higher quantiles, making it unsuitable for the study of extremes.

In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM) and the Soil Moisture Operational Products System (SMOPS) observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb) using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM) maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS) using the Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS) SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD) of 0.0303 m³/m³. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m³/m³. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb) at an incidence angle of 55° using the Community Microwave Emission Model (CMEM) Radiative transfer Model (RTM). This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA) brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

Accurate nowcasting of short‐lived extreme weather events is essential for saving millions of lives and property. Traditional methods of nowcasting are majorly focused on extrapolation of precipitation derived from radar reflectivity data, which often fail to capture the initiation and decay of weather systems. Earlier studies have shown the ability of high‐resolution Numerical Weather Prediction models to better capture the structure and lifecycle of storms compared to data‐driven methods. However, the initial value problem of NWP makes it more challenging to be implemented for nowcasting applications. To handle such uncertainty from initial conditions, we have designed an NWP nowcasting system based on variational approach using Weather Research and Forecast (WRF) model. Since one of the major challenges of the variational methods in the nowcasting system is the choice of control variables used for generating background error statistics. Thus we have investigated the impact of control variable options on improving the skill of variational based NWP nowcasting system. The proposed nowcasting system was tested for a heavy rainfall event occurred over the Chennai city, India on 1^st December 2015, by assimilating Doppler Weather Radar (DWR) data using different control variable options in WRF‐ 3DVAR and 4DVAR based nowcasting system. Results show that control variables (CV) choices have a positive impact on 4DVAR analysis, particularly on radial velocity. Our results also indicate that assimilation of DWR data with zonal and meridional momentum CV in a 4DVAR system shows more than 30% improvement in precipitation forecast skill compared to the 3DVAR system.

Groundwater (GW) storage plays a critical role in the sustainable development of the water-energy-food nexus of a country. Intensive exploitation of GW for irrigation has led to severe water deficit in many parts of India. Severe droughts (meteorological drought) further increases the rate of GW depletion. In the last decade, India witnessed severe droughts during 2002, 2009, and 2012. However, the critical change in the rate of GW depletion in India after frequent severe droughts remains unidentified. Here, we have devised a methodology to detect the point of maximum fluctuation in Gravity Recovery and Climate Experiment (GRACE) based GW time series using change point analysis. The occurrence of 2009 severe drought has significantly affected the GW balance over the majority of India. Our study indicates the transition in the GW trend observed in South India from positive to negative post-2009 drought. In the southern states, Karnataka indicates a maximum GW depletion rate of 0.25 cm per month for a time period from 2009 to 2016.

Lakes are an essential component of biogeochemical processes, and variations in lake level are regarded as indicators of climate change. For more than a decade, satellite altimetry has successfully monitored variation in water levels over inland seas, lakes, rivers, and wetlands. Through altimetry, the surface water levels are measured at varying temporal scales depending on the orbit cycle of the satellite. The futuristic mission of Surface Water and Ocean Topography (SWOT) scheduled to be launched in year 2022 shall offer the spatial coverage and resolution suitable for water level estimation and volume calculation in small water bodies like lakes worldwide. With a radar interferometer in Ka-band, SWOT proposes to provide two-dimensional maps of water heights 21 days repeat orbit configuration. Cycle average SWOT datasets for land will be developed with higher temporal resolution, with temporal resolution varying geographically. This work assesses the potential of SWOT for monitoring water volumes over a case study lake by analyzing SWOT like synthetic data produced using the SWOT simulator developed by the Centre National d'Etudes Spatiales (CNES). With SWOT relying on a novel technology, the initial 90 days of this mission after launch shall focus on an extensive calibration and validation. Firsthand results of SWOT-simulated water levels and volumes are presented over a case study region in the tropical band, namely, Pookode Lake, in the ecologically fragile district of Wayanad, Kerala, India. It is the second-largest freshwater lake in Kerala that is being affected by anthropogenic activities, causing huge depletion in lake water storage in the last four decades. Our analysis indicated that the lake region is subjected to a rise in temperature of 0.018°C per year. We further assess the potential of remote sensing and SWOT data to monitor water storage of Pookode Lake, which is undergoing a rapid change. Results show that the proxy water surface elevations have immense potential in scientific studies pertaining to lake monitoring across the world. Overall, the study shows the potential of SWOT for monitoring the variability of water levels and volumes in this region.