Browsing by Author "Krushna Chandra Gouda"

Now showing 1 - 2 of 2

Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Clustering
(Springer Nature, 2025) Ujjwal Singh; Sadaf Nasreen; Gaurav Tripathi; Pragya Mehrishi; Rajani K. Pradhan; Poppová Bestakova; Vivek Vikram Singh; Krushna Chandra Gouda; Laxmi Kant Sharma; Kiran Jalem; Petr Maca; R. R. Nidamanuri; Akhilesh Singh Raghubanshi; Yannis Markonis; Rakovec Oldřich; Martin Hanel
Accurate precipitation estimation at high spatial and temporal resolutions is essential for hydrological and meteorological applications, especially in regions experiencing water resource degradation. This study presents a robust non-parametric framework for disaggregating coarse-resolution satellite precipitation data to finer scales, using a hybrid model that integrates Extreme Gradient Boosting (XGBoost) with multivariate spatio-temporal fuzzy clustering. Eight clusters were delineated based on Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation and Shuttle Radar Topography Mission (SRTM) elevation data, with one representative station per cluster used for training and validation, and an additional 19 stations employed solely for independent validation. We downscaled 255 months (June 2000–September 2021) of IMERG precipitation data from 11 to 1 km spatial resolution across the Czech Republic. The disaggregated precipitation demonstrated marked accuracy improvements when evaluated against observed station data, with R2 values ranging from 0.63 to 0.85, RMSE between 17.43 mm and 32.41 mm, NSE from 0.39 to 0.82, and KGE spanning 0.67 to 0.86-indicating a significant reduction in the bias inherent in the original IMERG data. The proposed methodology achieved (1) enhanced agreement between disaggregated and observed monthly precipitation, (2) significant improvement in IMERG data accuracy at finer scales, and (3) demonstrated operational potential in regions with sparse ground-based observations. This approach offers a promising solution for generating reliable, high-resolution precipitation datasets in data-scarce environments, with broad applicability in global hydrological and meteorological modelling. © The Author(s) 2025.
Impact of assimilation of microwave imager radiance data on simulation of tropical cyclones over the Bay of Bengal using the NCUM-R modelling system
(John Wiley and Sons Ltd, 2025) Ashish Routray; Shivaji Singh Patel; Devajyoti Dutta; Kottu B.R.R. Hari Prasad; Krushna Chandra Gouda; R. Bhatla; V. S. Prasad
An effort was made to investigate the impact of the assimilation of microwave imager (MI) radiance observations on the simulation of tropical cyclones (TCs) using the high-resolution NCUM-4DVAR assimilation method. Two numerical experiments were performed: a control experiment (CTL) (assimilated global telecommunication system [GTS] observations) and satellite observations (SAT) (assimilated GTS plus global microwave imager [GMI] and special sensor microwave imager and sounder [SSMIS] satellite MI radiance). Assimilation of MI radiance is observed to be capable of depicting the structure, track and intensity of storms. The analysis increments of temperature and geopotential height suggested that the SAT experiment effectively adjusted the core region of TCs and systematically corrected the position in the first guess of the model. The strength of large-scale moisture transport from the underlying oceanic surface and helicity around the storms are well simulated by the SAT with the axisymmetric eye of TCs. The premature intensification of TCs is simulated by CTL, whereas the progression of the intensity of storms is relatively well captured in the SAT experiment. Dry air invading the inner core of a TC can disrupt the energy cycle and reduce storm intensity, as seen in the CTL simulations. Similarly, the negative value of diabatic heating appears around the centre of the storm with an increase of altitudes in CTL simulations, which impedes the intensity of TCs. This feature suggested that CTL simulations do not adequately bring out the intensity and vertical structure of the TCs. The track forecast of storms is considerably improved in SAT simulations compared to CTL. The forecast skill of rainfall is also relatively improved in SAT simulations. Overall assimilation of MI radiances enhanced the model's forecast skill to simulate structure, movement, intensity and precipitation associated with the storms. © 2025 Royal Meteorological Society.