activity
20202026
most citedRotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems

13 citations · 13 across the 4 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2026

Video Diffusion for Satellite-based High-Dynamical-Fidelity Precipitation (HiDFiP) Field Generation

Runze Li, Yan Xia, Yongquan Qu +6

High spatiotemporal fidelity precipitation products that accurately capture storm spatial organization, propagation, and lifecycle evolution, are essential for advancing hydrometeo…

cs.LG2025

A Diffusion-Based Framework for High-Resolution Precipitation Forecasting over CONUS

Marina Vicens-Miquel, Amy McGovern, Aaron J. Hill +3

Accurate precipitation forecasting is essential for hydrometeorological risk management, especially for anticipating extreme rainfall that can lead to flash flooding and infrastruc…

physics.ao-ph2025

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain

Simon Pfreundschuh, Malarvizhi Arulraj, Ali Behrangi +17

Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means…

physics.ao-ph2024

A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations

Clement Guilloteau, Gavin Kerrigan, Kai Nelson +4

A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and…

eess.SY2020★ 13 cited

Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems

Clément Guilloteau, Antonios Mamalakis, Lawrence Vulis +2

Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatio-temporal patterns within specific frequency bands and extracting dynamical m…