The Passive Microwave Neural Network Precipitation Retrieval (PNPR) for the Cross-track Scanning ATMS Radiometer

The passive microwave (PMW) cross-track scanning radiometers, originally developed for temperature and humidity soundings, have shown great capabilities to provide a significant contribution in the precipitation monitoring (in terms of measurement quality and spatial/temporal coverage). The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for cross-track scanning radiometers, originally developed for the Advanced Microwave Sounding Unit/Microwave Humidity Sounder (AMSUA/ MHS) radiometers (on board the European MetOp and U.S. NOAA satellites), was recently newly designed to exploit the Advanced Technology Microwave Sounder (ATMS) on board the Suomi-NPP satellite and the future JPSS satellites. The PNPR-ATMS algorithm, based on the Artificial Neural Network (ANN) approach, is intended to be also easily tailored to the future Microwave Sounder (MWS) onboard the MetOp-Second Generation (MetOp-SG) satellites. The main PNPR-ATMS algorithm improvements are the design and implementation of a new ANN able to manage the information derived from the additional ATMS channels (respect to the AMSUA/ MHS radiometer) and a new screening procedure for not-precipitating pixels. One of the main goals of the research is to achieve maximum consistency of the retrieved surface precipitation from the different cross-track radiometers orbiting around the globe. To this purpose, both the PNPR algorithms are based on the same physical foundation. The PNPR is optimized for the European and the African area. The neural network was trained using a cloud-radiation database built upon 94 cloud-resolving simulations over Europe and the Mediterranean and over the African area. A Radiative Transfer Modeling System has been used to compute simulated satellite TB vectors consistent with the ATMS channel frequencies, viewing angles, and view-angle dependent IFOV sizes along the scan projections. As opposed to other ANN precipitation retrieval algorithms, PNPR uses a unique ANN that retrieves the surface precipitation rate for all types of surface backgrounds represented in the training database, i.e., land (vegetated or arid), ocean, snow/ice or coast. This approach prevents different precipitation estimates from being inconsistent with one another when an observed precipitation system extends over two or more types of surfaces. As input data, the PNPR algorithm incorporates the TBs from selected ATMS channels, and various additional TBs-derived variables. Ancillary geographical/geophysical inputs (i.e., latitude, terrain height, surface type, season) are also considered during the training phase. The PNPR 64 algorithm outputs consist of both the surface precipitation rate (along with the information on precipitation phase: liquid, mixed, solid) and a pixelbased quality index. We will illustrate the main features of the PNPR algorithm and will show some results of a verification study over Europe and Africa. The verification is based on the available ground-based radar and/or rain gauge network observations (over the European area), and on the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM-PR) (over the African area). The NASA/JAXA Global Precipitation Measurement (GPM) Dual frequency Precipitation Radar (DPR) products are used as further reference. Moreover, the precipitation retrievals obtained from different PMW cross-track radiometers will be shown to evaluate the consistency among the different products. The PNPR algorithm aims at contributing towards the full exploitation of all cross-track and conically scanning PMW radiometers available in the NASA/JAXA Global Precipitation Measurement (GPM) mission era for global monitoring of precipitation.