The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This …
Atmospheric rivers (ARs) play a key role in California's water supply and are responsible for most of the extreme precipitation and major flooding along the west coast of North …
Using a high-resolution atmospheric general circulation model simulation of unprecedented ensemble size, we examine potential predictability of monthly anomalies under El …
Initialized climate predictions offer distinct benefits for multiple stakeholders. This Review discusses initialized prediction on subseasonal to seasonal (S2S), seasonal to …
This study investigates the filtering problem with one-way coupled (OWC) state-space systems, for which the joint ensemble Kalman filter (EnKF) is the standard solution. In this …
A new prototype coupled ocean-atmosphere Ensemble Kalman Filter reanalysis product, the Community Earth System Model using the Data Assimilation Research Testbed (CESM-DART), is …
Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some …
The latest generation of coupled models, the sixth Coupled Models Intercomparison Project (CMIP6), is used to study the changes in the El Nino-Southern Oscillation (ENSO) in a …
Precipitation in California is highly variable and not well forecasted on subseasonal-to-seasonal (S2S) timescales. Understanding relationships between synoptic-scale …
A key aim of observational campaigns is to sample atmosphere-ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early …