01 Aug 2023
 | 01 Aug 2023
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

GPROF V7 and beyond: Assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson

Abstract. The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN.

This study validates GPROF V7 and multiple configurations of the GPROF-NN retrieval against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated.

The validation of GPROF V7 indicates that the retrieval produces reliable precipitation estimates over CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reproduces the principal precipitation characteristics of each region. The effective resolution of GPROF V7 is found to be 51 km over CONUS and 18 km over the tropical Pacific. GPROF V7 produces robust precipitation estimates also for the other sensors of the GPM constellation.

The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPROF precipi- tation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error by about twenty percent for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar/radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements comparged to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used in the training of the retrievals for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.

Simon Pfreundschuh et al.

Status: open (extended)

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  • RC1: 'Comment on egusphere-2023-1310', Anonymous Referee #1, 27 Sep 2023 reply

Simon Pfreundschuh et al.

Simon Pfreundschuh et al.


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Short summary
The latest version of the GPROF retrieval algorithm that produces global precipitation estimates from the Global Precipitation Measurement mission observations is validated against ground-based radars. The validation shows that the algorithm accurately estimates from continental to regional scales. In addition, we validate candidates for the next version of the algorithm and identify principal challenges for further improving space-borne rain measurements.