Next Article in Journal
Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks
Next Article in Special Issue
How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey
Previous Article in Journal
The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases
Previous Article in Special Issue
Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula
Open AccessArticle

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

1
Institute of Atmospheric Sciences and Climate (ISAC), Italian National Research Council (CNR), 00133 Rome, Italy
2
SERCO SpA, 00044 Frascati, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1122; https://doi.org/10.3390/rs10071122
Received: 22 June 2018 / Revised: 12 July 2018 / Accepted: 14 July 2018 / Published: 16 July 2018
(This article belongs to the Special Issue Remote Sensing of Precipitation)
This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications. View Full-Text
Keywords: satellite precipitation retrieval; neural networks; GPM; GMI; remote sensing satellite precipitation retrieval; neural networks; GPM; GMI; remote sensing
Show Figures

Graphical abstract

MDPI and ACS Style

Sanò, P.; Panegrossi, G.; Casella, D.; Marra, A.C.; D’Adderio, L.P.; Rysman, J.F.; Dietrich, S. The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer. Remote Sens. 2018, 10, 1122.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop