DeepPrecip: a deep neural network for precipitation retrievals

King, Fraser; Duffy, George; Milani, Lisa; Fletcher, Christopher G.; Pettersen, Claire; Ebell, Kerstin

doi:https://doi.org/10.5194/amt-15-6035-2022

Articles | Volume 15, issue 20

https://doi.org/10.5194/amt-15-6035-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-15-6035-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 20

Research article

|

21 Oct 2022

Research article |

| 21 Oct 2022

DeepPrecip: a deep neural network for precipitation retrievals

Fraser King, George Duffy, Lisa Milani, Christopher G. Fletcher, Claire Pettersen, and Kerstin Ebell

Data sets

DeepPrecip Training Data Fraser King https://doi.org/10.5281/zenodo.5976046

ERA5 hourly data on single levels from 1959 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.adbb2d47

Model code and software

frasertheking/DeepPrecip: Full Release (v1.0.0) Fraser King https://doi.org/10.5281/zenodo.7221133

Short summary

Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.