Sparse Data Initialization for Machine Learning Weather Prediction Models
This grant opportunity focuses on developing a machine learning weather prediction system to create accurate forecasts from incomplete and irregular observational data.
Phase I involves understanding the technical challenges of initiating these models with sparse data and creating innovative solutions by studying current data assimilation and forecast analysis methods.…
Develop a machine learning weather prediction (MLWP) system to generate a complete model analysis, initialization, and produce skillful forecasts from an incomplete, non-gridded set of observations that may include sparse and irregular spatial and temporal sampling of in-situ, remote sensing, sa…
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