DeepPrecip: a deep neural network for precipitation retrievals - Kölner UniversitätsPublikationsServer

King, Fraser ORCID: 0000-0003-1698-8482, Duffy, George, Milani, Lisa ORCID: 0000-0003-0498-1021, Fletcher, Christopher G., Pettersen, Claire ORCID: 0000-0002-8685-6242 and Ebell, Kerstin ORCID: 0000-0002-0042-4968 (2022). DeepPrecip: a deep neural network for precipitation retrievals. Atmos. Meas. Tech., 15 (20). S. 6035 - 6051. GOTTINGEN: COPERNICUS GESELLSCHAFT MBH. ISSN 1867-8548

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DOI:10.5194/amt-15-6035-2022

Abstract

Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval algorithm from a deep convolutional neural network (DeepPrecip) to estimate 20 min average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays a high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 160 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5-2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.

Item Type:

Journal Article

Creators:

Creators	Email	ORCID	ORCID Put Code
King, Fraser	UNSPECIFIED	orcid.org/0000-0003-1698-8482	UNSPECIFIED
Duffy, George	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Milani, Lisa	UNSPECIFIED	orcid.org/0000-0003-0498-1021	UNSPECIFIED
Fletcher, Christopher G.	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Pettersen, Claire	UNSPECIFIED	orcid.org/0000-0002-8685-6242	UNSPECIFIED
Ebell, Kerstin	UNSPECIFIED	orcid.org/0000-0002-0042-4968	UNSPECIFIED