Forskare/Researcher
Forskare/Researcher
Air, Water and Landscape Science (LUVAL),
Department of Earth Science,
Uppsala University, Sweden-75236.
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About me
As an enthusiast in understanding the laws of nature, I focus on studying the earth and planetary system science and understanding climate, ocean and atmospheric dynamics and thermodynamic phenomena using machine learning techniques and advanced statistical, dynamic, and numerical models. I aimed to contribute to better forecasting and predicting weather and climate by improving algorithms, parameterisation and deploying new techniques supported by good data management, computing, and accessibility.
Research subjects
Earth, space and planetary sciences.
Greenhouse gases and climate feedback.
Atmospheric rivers, moisture transports and the global water cycle.
Drivers, predictability and impacts of climate change and hydrometeorological extremes.
Upper ocean variability and the global monsoon system.
Global radiation changes.
In-situ observations, GPSRO, remote sensing, reanalysis, hybrid, blended and climate model datasets.
Climate, atmospheric, and ocean dynamics and thermodynamic processes.
Bayesian models, stochasticity, chaos, uncertainty, heuristics, and complexities.
Earth system modelling and machine learning models.
Scientific programming, numerical modelling, statistical and dynamical models, ML and DL models.
Computational and applied physics.
Research interests
Understanding carbon cycle dynamics and quantifying impacts on climate feedbacks using Earth system models.
Quantifying the importance of air-sea interaction on atmospheric rivers to better predict extreme events in coastal zones under the changing climate.
Studying the role of meridional heat, momentum and moisture transport on extreme hydro-meteorological events over mid-latitudes and polar regions.
Equilibrium climate sensitivity, planetary boundaries and tipping elements in the Earth's climate system.
Understanding the role of ocean-atmospheric dynamics and thermodynamics on the concurrent and compound extremes
Role of upper ocean variability and climate modes on monsoon dynamics and precipitation variability.
Quantifying the uncertainties in data, heuristics, scaling and impacts to understand Earth's climate better.
Evaluating the heuristics, uncertainties, and challenges in understanding the climate system's chaotic, complex, and stochastic physical phenomena.
Role of the variability of radiative heat fluxes on the global upper ocean and climate system.
Understanding the functioning of our atmosphere and its interactions with other climate system components.
Exploring various climate and weather extremes - from severe windstorms to urban pollution using different techniques, methods, models and tools.
Geoscientific model and software development for finding practical, applicable solutions for state estimation of complex dynamic systems.
NWP and climate modelling, statistical, dynamical and ML techniques for parameterising and forecasting weather and climate variables.
Climate model emulators, digital twins, coupling ESMs with AI, LAM, and AI for climate emulators (ACE).
Physics-informed climate indicators, XAI, surrogate modelling, Large ENSembles (LENS), Mega/Large networks, PINNs, PIML, ClimODE, and AI4ESP.
AI-ML tools, packages and libraries; NLP, Generative AI, Foundation models, Diffusion models, AI Agents and Agentic AI.
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