Improving Atmospheric River Forecasts With Machine Learning
w.-e.-chapman
A new regional coupled ocean-atmosphere model is developed and its implementation is presented in this paper. The coupled model is based on two open-source community model …
A suite of statistical linear inverse models (LIMs) are used to understand the remote and local SST variability that influences SST predictions over the North Pacific region. …
The causes of the extreme and persistent warming in the Northeast Pacific from the winter of 2013/14 to that of 2014/15 are still not fully understood. While global warming may …
Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the …
Many coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological …
Ocean surface winds, currents, and waves play a crucial role in exchanges of momentum, energy, heat, freshwater, gases, and other tracers between the ocean, atmosphere, and ice. …
A multimodel evaluation of subseasonal-to-seasonal (S2S) hindcast skill of atmospheric rivers (ARs) out to 4-week lead over the western United States is presented for three …
Tropical sea surface temperature (SST) and winds vary on a wide range of timescales and have a substantial impact on weather and climate across the globe. Here we study the …