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Highlights • Variational data assimilation used for assimilating Doppler weather data is sensitive to the selection of control variables. • Doppler Weather Radar assimilation using the unconventional control variables of horizontal wind components significantly improve the skill of high intensity precipitation forecast in the first 12 h of the forecast model. • The smaller length scale and larger variance from horizontal wind components product analysis increments that tend to effectively capture small-scale features. Abstract Short-term precipitation forecasts from numerical weather prediction models are a vital source of information for real-time flood forecasting systems. Previous studies show that assimilation of Doppler Weather Radar (DWR) observations significantly improves the forecast skill of short-term precipitation. However, the variational assimilation methods used for DWR assimilation are sensitive to the selection of control variable options in background error statistics. In this study, the impact of control variable choices in assimilating DWR observations for improving the forecast of heavy rainfall event is analysed. For this purpose radar reflectivity and radial velocity, observations are assimilated using stream function velocity potential (ψχ) and horizontal wind components (uv) control variable options in Weather Research and Forecast model – 3DVAR (three-dimensional variational assimilation system). The results show that DWR assimilation using uv control variable option has improved the skill of first 12 h of high intensity precipitation forecasts. [ABSTRACT FROM AUTHOR]

This work introduces Sentinel-1 based Inland water dynamics Mapping System (SIMS), an open-source web application developed to enable automated mapping of inland water dynamics using Sentinel-1 radar imagery. SIMS relies on a novel framework built using Python and Google Earth Engine. The underlying algorithm involves a simple binary thresholding technique and an outlier removal method tailored to perform efficiently across complicated flow regimes. Results can be downloaded as numerical data or as time-series of shapefiles representing the variation of inland water extents. Exported geospatial datasets aid the pre-launch study of future Surface Water and Ocean Topography (SWOT) mission which is expected to deliver hydrological measurements at unprecedented spatial resolutions. Classification metrics are evaluated at 20 validation sites across the globe using Sentinel-2 based Modified Normalized Difference Water Index (MNDWI) images as reference. Results indicated high overall accuracy ranging from 84.16% to 99.47% for lakes and 87.23%–98.96% for rivers. • A new open-source web app for mapping dynamic inland water extents is presented. • Application is programmed in Python using Sentinel-1 data from Google Earth Engine. • Backend algorithm involves a novel framework configurable for rivers and lakes. • Derived outputs can be exported as time series of surface water extent shapefiles. • Results have huge potential to improve the pre-launch study of future SWOT mission. [ABSTRACT FROM AUTHOR]

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]