Datasets and Models
Trying-Early Adaptive Multilevel Splitting Algorithm
Justin Finkel, Paul A. O’Gorman
Rare-event algorithms can be used to sample extreme events at a much-reduced cost compared to standard direct simulations by using ensembles of simulations that are selectively cloned or killed. Initial investigation showed that a promising rare-event algorithm known as adaptive multilevel splitting (AMS) did not perform well on daily precipitation extremes because the timescale of the extreme event was comparable to the timescale over which perturbed simulations diverge. Authors then introduced a modified algorithm, Trying-Early AMS (TEAMS), that split trajectories well in advance of the extreme event's onset. In this reporting period, we continued research on TEAMS and demonstrated improved sampling of extreme local events in the Lorenz 96 model by a factor of order 10 relative to direct sampling.
February, 2024
JPoNG
Rahman Khorramfar,Dharik Mallapragada, Saurabh Amin
JPoNG is an optimization model for joint planning of power and NG infrastructure with a resolved representation of spatial, temporal, and technological system operation. The model is implemented as open-source software in Python with Gurobi solver. The associated code and data is available in the following link
December, 2022
Statistical-Physical Adversarial Learning from Data and Models for Extreme Rainfall Downscaling
Anamitra Saha, Sai Ravela
Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands detail that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution (0.25∘×0.25∘) climate model outputs into high-resolution (0.01∘×0.01∘) rainfall fields while efficaciously quantifying uncertainty. Results indicate that the downscaled rainfall fields closely match observed spatial fields and their risk distributions.
December, 2022