Research and Projects

Current Research

Atmospheric rivers-hydrometeorological extreme events

Goal: To quantify the importance of air-sea interaction on atmospheric rivers for better prediction of extreme events in the coastal zone.

1. Purpose and aims: Atmospheric rivers are long, narrow bands of large integrated water vapour transport in the troposphere. At landfall, they are typically associated with extreme wind and precipitation. In recent years, there has been a growing interest in atmospheric rivers, including their regional impacts on water availability, their modulation by climate variability, and their representation in weather and forecast models. Air-sea processes are major modulators of two aspects of describing and understanding atmospheric rivers: (i) in the source area over the large oceans and (ii) coastal processes at landfall. When atmospheric rivers make landfall, they interact with coastal processes, the uplift of moisture-rich air within the rivers over the land (mountains), and the dynamics of the large-scale flow. These features frequently act to generate high precipitation totals and extreme wind conditions. The coastal zone is a complex and important area within the climate system, where there is a dynamic interaction among land, sea, and atmosphere. Gradients in surface friction and temperature also result in mesoscale circulation systems interacting with dynamics at larger scales.

The scientific objectives of this project are:

To understand air-sea interaction features in source regions across large oceans that lead to the development of atmospheric rivers.
Describe the feedback of the coastal interaction processes and the dynamics of the atmosphere, with particular focus on the interaction with atmospheric rivers.
Understand and describe the complex interaction between land/sea features and atmospheric rivers in the coastal zone, leading to intensified extreme events at landfall.

The expected outcomes of the project are several, including:

Physical understanding of the relation between air-sea interaction, coastal processes and atmospheric rivers.
Improved coupled Earth-System models.
Climatological analysis of extreme hydrometeorological events caused by atmospheric rivers for present and future climate conditions.

See 1 and 2

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.

See 1, 2, and 3.

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.

See 1, 2, 3, and 4 .

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

See 1, and 2.

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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