A5016508930- Meteorological Phenomena and Simulations
- Tropical and Extratropical Cyclones Research
- Climate variability and models
- Seismic Waves and Analysis
- demographic modeling and climate adaptation
- Hydrological Forecasting Using AI
- Underwater Acoustics Research
- Insurance, Mortality, Demography, Risk Management
- Model Reduction and Neural Networks
University of California, Irvine
2022-2025
Abstract The aggregation of tropical convection greatly influences the mean‐state atmosphere, altering humidity distributions, total atmospheric radiative cooling, and cloud amounts. Although studies have demonstrated sensitivity convective to horizontal resolution domain size, few explored impact vertical on aggregation. Here, we investigate simulations deep using System for Atmospheric Modeling resolving model. We analyze radiative‐convective equilibrium with varying levels (32, 64, 128,...
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation higher fidelity simulators can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations ML emulators. However, this hybrid ML-physics simulation approach requires...
Vertical resolution is an often overlooked parameter in simulations of convection. We explore the sensitivity simulated deep convection to vertical System for Atmospheric Modeling (SAM) resolving model. analyze run tropical radiative convective equilibrium with 32, 64, 128, and 256 levels a small (100 km) large domain (1500 km). At high resolution, relative humidity anvil cloud fraction are reduced, which linked reduction both fractional volumetric detrainment. This increases total...