9 citations · 11 across the 8 of their papers we have counts for
6 papers · 1 filter
Improving global precipitation forecasts with an AI weather model trained on satellite observations
Julian F. Schmitt, Bertrand Delorme, Robert C. King +4
Precipitation forecasts shape decision-making across the global economy, particularly in sectors such as agriculture. However, unlike variables such as temperature, precipitation i…
MAUSAM: An Observations-focused assessment of Global AI Weather Prediction Models During the South Asian Monsoon
Aman Gupta, Aditi Sheshadri, Dhruv Suri
Accurate weather forecasts are critical for societal planning and disaster preparedness. Yet these forecasts remain challenging to produce and evaluate, especially in regions with…
Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves
Aman Gupta, Aditi Sheshadri, Sujit Roy +5
Global climate models parameterize a range of atmospheric-oceanic processes like gravity waves, clouds, moist convection, and turbulence that cannot be sufficiently resolved. These…
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
Aman Gupta, Aditi Sheshadri, Sujit Roy +3
Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity…
Machine learning for climate physics and simulations
Ching-Yao Lai, Pedram Hassanzadeh, Aditi Sheshadri +3
We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, un…
Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM
Y. Qiang Sun, Hamid A. Pahlavan, Ashesh Chattopadhyay +6
Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinea…