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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 (NWP) 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 WRF model. 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 that occurred over the Chennai city, India, on 1 December 2015, by assimilating Doppler Weather Radar data using different control variable options in Weather Research and Forecast—three‐dimensional (3DVAR)‐ and four‐dimensional variational data assimilation (4DVAR)‐based nowcasting system. Results show that control variables choices have a positive impact on 4DVAR analysis, particularly on radial velocity. Our results also indicate that assimilation of Doppler Weather Radar data with zonal and meridional momentum control variable in a 4DVAR system shows more than 30% improvement in precipitation forecast skill compared to the 3DVAR system. Key Points: The 4DVAR assimilation of DWR data with zonal and momentum control variables improves skill of short term precipitation forecastsThe 4DVAR shows better skill compared to 3DVAR‐based nowcasting systemChoice of Control variables plays a major role in improving the skill of NWP nowcasting system [ABSTRACT FROM AUTHOR]

Monsoonal convective systems are examined using four years (2014-2017) of radar reflectivity data from the Precipitation Features (PFs) database of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual Precipitation Radar (DPR). The study classifies the cumulonimbus tower (PFCbT) at 12 km, and intense convective clouds at both 3 km (PF-ICC3) and 8 km (PF-ICC8) based on PFs' reflectivity threshold at a reference height over the Indo-Gangetic Plains (IGP) and Indian Western coastal (WG) region, including the Arabian Sea and the Western Ghats. Results show that the regional variations are more enhanced for the PF-ICC3 clouds with high occurrence over the IGP region. In the mixed-phase regime, the median maximum reflectivity is greater for all cloud types over the IGP region. The occurrences of 20 and 40 dBZ echo the top height > 5 km is higher in the IGP region, indicating the deep and intense convection. The aerosol-cloud interaction is examined for warm and mixed-phase clouds. The vertical structure of aerosols shows the suppression of warm rainfall over the IGP region. However, rainfall intensity increases in mixed-phase clouds because of the dominancy of ice processes. The significant positive (negative) correlation is observed between the echo top height and aerosol concentrations over the IGP (WG) region. The value of the novel findings clearly states the region-specific demand for a closer examination of the radar reflectivity-aerosol interaction on regime-dependent clouds over the IGP region as well as contrasts against other regions for similar and contrasting cloud-aerosol-radiation interactions. [ABSTRACT FROM AUTHOR]

A regionalized rain/no-rain classification (RNC) based on scattering index methodology is developed for detecting rainfall signatures over the land regions of the Mahanadi basin (India), using data products from the passive and active sensors onboard the Tropical Rainfall Measuring Mission (TRMM), namely the TRMM Microwave Imager (TMI) and Precipitation Radar (PR). The proposed model, developed using data for two years from the orbital database, was validated using PR andin-situdata for selected case study events in 2011 and 2012. Performance evaluation of the model is discussed using 10 metrics derived from the contingency table. Overall, the results show superior performance, with an average probability of detection of 0.83, bias of 1.10 and odds ratio skill score greater than 0.93. Accurate rainfall detection is obtained for 95% of case study events. The relative performance of the proposed model is dependent on rainfall type, but it should be useful in rainfall retrieval algorithms for current missions such as the Global Precipitation Measurement Mission.Editor M.C. Acreman; Associate editor Y. Gyasi-Agyei [ABSTRACT FROM PUBLISHER]

Abstract The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications. [ABSTRACT FROM AUTHOR]

• Evaluated performance of precipitation forcing data (TMPA, GDAS, CHIRPS, MSWEP) to simulated soil moisture. • Validation data was used from ESA-CCI soil moisture and CTP-SMTMS dataset for years 2010–2012. • Evaluated graphical and statistical comparison of results. • Simulated soil moisture forced with GDAS precipitation showed overall superior performance. Land surface processes considerably influence the global weather and climate patterns which makes its quantification significant to scientists, hydrologists as well as policymakers alike. Considering the lack of available in-situ measurement, retrieval of the land surface fluxes mostly relies on remotely sensed satellite retrieval and through simulations from land surface models (LSMs). Hence, it is essential to quantify the uncertainties present in the output of these land surface models which are mainly due to errors in forcing data, model parameters and model structure. Precipitation is one of the key input forcing data used in LSMs. With the advancement of remote sensing techniques, multiple sources of precipitation products are made available to the user community. This study examines the effect of precipitation uncertainty in LSM simulated soil moisture. For this study, four precipitation products are used namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT v7, Global Data Assimilation System (GDAS), Climate Hazards Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). These data products are used as meteorological forcing in Noah 3.6 LSM for the simulation of soil moisture. The uncertainty inherent in the precipitation products are examined using two approaches a) By evaluating the precipitation products against the gridded India Meteorological Department (IMD) precipitation dataset and b) By using the precipitation products for simulating soil moisture outputs. These were validated over the Indian subcontinent using validation data from the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture and Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) dataset for the years 2010 to 2012. The study utilizes various graphical as well as quantitative evaluation methods to determine the best performing precipitation product. Our study indicates that the simulated soil moisture forced with GDAS and MSWEP precipitation product performed consistently superior among all the other simulation outputs over India. [ABSTRACT FROM AUTHOR]