Preprints
https://doi.org/10.5194/amt-2022-15
https://doi.org/10.5194/amt-2022-15
 
06 Apr 2022
06 Apr 2022
Status: this preprint is currently under review for the journal AMT.

GPROF-NN: A neural network based implementation of the Goddard Profiling Algorithm

Simon Pfreundschuh1,2, Paula J. Brown2, Christian D. Kummerow2, Patrick Eriksson1, and Teodor Norrestad3 Simon Pfreundschuh et al.
  • 1Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden
  • 2Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523
  • 3Previously Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden

Abstract. The Global Precipitation Measurement (GPM) mission aims to provide global measurements of precipitation with a temporal resolution of three hours in order to allow close monitoring of the global hydrological cycle. To achieve global coverage at such high temporal resolution, GPM combines observations from a constellation of passive microwave (PMW) sensors. The Goddard Profiling Algorithm (GPROF) is the operational retrieval for precipitation and hydrometeor profiles from these sensors.

Aiming to investigate the effect of the retrieval algorithm on the accuracy of the PMW precipitation retrieval, we present two neural network based, probabilistic implementations of GPROF: GPROF-NN 1D, which processes individual pixels just as the current GPROF algorithm, and GPROF-NN 3D, which employs a convolutional neural network to incorporate structural information into the retrieval. To isolate the impact of the retrieval method from any issues in the training data, the GPROF and GPROF-NN algorithms are evaluated on a separate test set that has the same statistical properties as the data used for the training of the GPROF-NN algorithms and the development of the current GPROF algorithm. Comparison of GPROF and the GPROF-NN 1D algorithm shows that by replacing GPROF with an identical neural network based retrieval the accuracy of the retrieved surface precipitation from the GPM Microwave Imager (GMI) can be improved by 10 to 25 % in terms of absolute error, root mean squared error and symmetric mean absolute percentage error. Comparable improvements are observed for the retrieved hydrometeor profiles and their column integrals. The improvements are consistent spatially as well as with respect to different surface types. The effective resolution in along track direction of the retrieved surface precipitation fields is increased from 23 km to 14 km. Similar, additional improvements are found for the GPROF-NN 3D retrieval over the performance of the GPROF-NN 1D retrieval, showing the added benefits of incorporating structural information into the retrieval. The effective resolution in along-track direction of the GPROF-NN 3D algorithm is reduced to 13.5 km, which is the upper limit imposed by the along track separation of consecutive scan lines. Comparable improvements are found also when the algorithms are applied to synthetic observations from the cross track scanning Microwave Humidity Sounder (MHS) sensor. Application of the retrieval algorithm to real observations from the GMI and MHS sensors of Hurricane Harvey suggest that these improvements can be expect to carry over to operational application.

The novel GPROF-NN algorithms presented here were designed to be functionally equivalent to the current implementation, which enables the neural network approach to replace the current Bayesian scheme in a future update. Despite their superior retrieval accuracy, the single CPU core runtime required for the operational processing of an orbit of observations is lower than that of GPROF. The GPROF-NN algorithms thus promise to be a simple and cost efficient way to improve the accuracy of the PMW precipitation retrievals of the GPM constellation and thus help improve the monitoring of the global hydrological cycle.

Simon Pfreundschuh et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Simon Pfreundschuh et al.

Model code and software

GPROF-NNGPROF-NN: A neural network based implementation of the Goddard Profiling Algorithm Simon Pfreundschuh https://doi.org/10.5281/zenodo.5819297

Simon Pfreundschuh et al.

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Short summary
The Global Precipitation Measurement is an international satellite mission providing global measurements of rain at high temporal resolution by combining observations from a constellation of satellites. We present two machine learning based implementations of the algorithm turning these observations into precipitation estimates. We show the algorithm can be improved by replacing it with a special neural network and that even larger improvements are achieved by incorporating spatial information.