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

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Cited articles

Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., and Inman, D. J.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks, J. Sound Vib., 388, 154–170,, 2017. a
Adhikari, A., Ehsani, M. R., Song, Y., and Behrangi, A.: Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods, Earth and Space Science, 7, e2020EA001357,, 2020. a
Bennartz, R., Fell, F., Pettersen, C., Shupe, M. D., and Schuettemeyer, D.: Spatial and temporal variability of snowfall over Greenland from CloudSat observations, Atmos. Chem. Phys., 19, 8101–8121,, 2019. a
Boudala, F. S., Gultepe, I., and Milbrandt, J. A.: The Performance of Commonly Used Surface-Based Instruments for Measuring Visibility, Cloud Ceiling, and Humidity at Cold Lake, Alberta, Remote Sens., 13, 5058,, 2021. a
Buttle, J. M., Allen, D. M., Caissie, D., Davison, B., Hayashi, M., Peters, D. L., Pomeroy, J. W., Simonovic, S., St-Hilaire, A., and Whitfield, P. H.: Flood processes in Canada: Regional and special aspects, Can. Water Resour. J., 41, 7–30,, 2016. a
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.