remote sensing Article The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification Leonardo Bagaglini 1 , Paolo Sanò 1, * , Daniele Casella 1 , Elsa Cattani 2 and Giulia Panegrossi 1   Citation: Baagaglini, L.; Sanò, P.; Casella, D.; Cattani, E.; Panegrossi, G. The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification. Remote Sens. 2021, 13, 1701. https://doi.org/10.3390/ rs13091701 Academic Editor: Francisco J. Tapiador Received: 30 March 2021 Accepted: 24 April 2021 Published: 28 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Roma, Italy; Leonardo.Bagaglini@artov.isac.cnr.it (L.B.); d.casella@isac.cnr.it (D.C.); g.panegrossi@isac.cnr.it (G.P.) 2 National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 40129 Bologna, Italy; e.cattani@isac.cnr.it * Correspondence: paolo.sano@cnr.it Abstract: This paper describes the Passive microwave Neural network Precipitation Retrieval algo- rithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions. Keywords: precipitation; satellite retrieval; microwave; neural network; climate data record; essential climate variables; Copernicus 1. Introduction In 2016, the European Centre for Medium-Range Weather Forecasts (ECMWF) im- plemented the Copernicus Climate Change Service (C3S), on behalf of the European Union, aimed at producing a new set of Essential Climate Variables (ECVs, variables that critically contribute to the characterization of the Earth’s climate) from observa- tions (https://climate.copernicus.eu/c3s312b-essential-climate-variable-products-derived- observations, accessed on 12 February 2021). The project focuses on five different variable Remote Sens. 2021, 13, 1701. https://doi.org/10.3390/rs13091701 https://www.mdpi.com/journal/remotesensing