Explore >> Select a destination
|
You are here 
|
esd.copernicus.org | ||
| | | | |
www.hydrology-and-earth-system-sciences.net
|
|
| | | | | ||
| | | | |
www.atmospheric-measurement-techniques.net
|
|
| | | | | ||
| | | | |
gmd.copernicus.org
|
|
| | | | | Abstract. Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-based models. To address this, we present a novel DL architecture called multi-scale feature fusion (MFF) that can forecast precipitation with a lead time of up to 3?h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the... | |
| | | | |
gmd.copernicus.org
|
|
| | | |||
