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Found 3 papers, 2 papers with code
Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture
This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time.
A Deep Learning Architecture for Passive Microwave Precipitation Retrievals using CloudSat and GPM Data
This paper presents an algorithm that relies on a series of dense and deep neural networks for passive microwave retrieval of precipitation.
Ensemble Riemannian Data Assimilation over the Wasserstein Space
Unlike the Eulerian penalization of error in the Euclidean space, the Wasserstein metric can capture translation and difference between the shapes of square-integrable probability distributions of the background state and observations -- enabling to formally penalize geophysical biases in state-space with non-Gaussian distributions.
Methodology
