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Current Research
Current Research
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Previous research and projects
Previous research and projects
Understanding uncertainties in the downwelling radiative fluxes and their impact on the upper ocean variability in the global tropical oceans using in-situ observations and satellite data.
Objective: The work quantifies the uncertainties in the downwelling radiative fluxes and their impact on upper ocean variability in the global tropical oceans using GTMBA observations and CERES/MODIS satellite data. It evaluates the downwelling short-wave and long-wave observations from ocean observation networks of RAMA (Indian Ocean), TRITON (Pacific), and PIRATA (Atlantic) using CERES/MODIS downwelling radiation data from multiple versions during 2000-2017. Further, the study explores the factors that led to the variability of these fluxes across the global tropical oceans spanning 30S-30N and investigates how these radiative fluxes can cause the upper ocean thermodynamics to change. This framework enables the identification of the role of downwelling radiative fluxes on upper ocean variability, which can further enhance our ability to validate air-sea interactions in the climate, ocean and weather forecasting models.
This work was initiated as part of the larger framework of the ocean mixing and monsoon (OMM) project alongside improving ocean modelling to enhance the INCOIS-GODAS using the Modular Ocean Model (MOM) of GFDL.
Upper Ocean Heat Variability, Climate Modes and Indian Summer Monsoon
Objective: To evaluate the role of upper ocean thermal parameters, particularly Tropical Cyclone Heat Potential (TCHP), Ocean Mean Temperature (OMT), Sea Surface Temperature (SST) anomalies, and Mean Sea Level Anomaly (MSLA), in improving the prediction of tropical cyclone intensity and Indian Summer Monsoon Rainfall (ISMR). The study aims to identify more reliable predictors for ISMR beyond traditional SST-based approaches by analysing the relationship between these oceanographic variables and monsoon variability. Additionally, it seeks to assess the influence of large-scale climate phenomena such as El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on ocean heat capacitance and its subsequent impact on regional rainfall distribution. Through a combination of statistical analysis and long-term trend evaluation, this study aims to enhance the understanding of ocean-atmosphere interactions and contribute to more accurate weather and climate forecasting in the Indian Ocean region.
A Neural Network Approach to Improve the Vertical Resolution of Atmospheric Relative Humidity Profiles from Geostationary Satellites using GPSRO data.
Objective: To develop a machine-learning / artificial neural network approach using GPSRO data to improve the vertical resolution of the atmospheric soundings from geostationary satellites in the Indian Ocean and landmass.
This work was carried out as a master's thesis at NRSC, ISRO, Hyderabad
Other projects
Other projects
Contributing developer for EC-Earth 4 consortium.
Worked on CLIVAR, WCRP, and Ocean Mixing and Monsoon (OMM) projects.
Developed techniques to study the life cycle of tropical cyclones and associated precipitation using lightning data.
Studied the impact of large-scale circulation and coupled ocean-atmospheric processes (ENSO, IOD) on the variability of upper ocean processes on evaporation, winds, clouds, and water vapour transport in the Indian Ocean region and on the Indian summer monsoon rainfall.
Machine learning techniques to improve the vertical resolution of atmospheric relative humidity profiles from geostationary satellites and using GPSRO data.
Developed super ensemble machine learning models to downscale, bias correct and generalise the North American Multimodal Ensemble (NMME) global seasonal forecasting for precipitation and temperature on a regional scale.
Processing and analysing ocean-atmospheric parameters data from satellite altimeters and in situ instruments.
Validating, statically analysing and evaluating high-resolution, hybrid and blended atmospheric and ocean flux data sets (required to force GCMs) from multiple reanalysis and satellite products like MODIS-CERES.
Processing and using in situ data from the Global Tropical Moored Buoy Array (GTMBA) and Ocean Moored Buoy Network for northern Indian Ocean (OMNI) buoys in the global tropical oceans for weather and climate forecasting.
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