Deep-learning (DL) postprocessing methods are examined to obtain reliable and accurate probabilistic forecasts from single-member numerical weather predictions of integrated …
Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties …
Waves generated by tropical cyclones can have devastating effects on coastal regions. However, the role of ocean currents in modifying wave amplitudes, wavelengths, and …
Projections of future climate are sensitive to the representation of upper-ocean diurnal variability, including the diurnal cycle of winds. Two different methods suitable for …
Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve …
Coupled processes and associated subsurface dynamics near the eastern edge of the Indo/western Pacific (WP) Warm Pool are important for air-sea interactions involved in tropical …
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 …