Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloThe Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer
Anno di pubblicazione2018
Autore/iSanò, Paolo; Panegrossi, Giulia; Casella, Daniele; Marra, Anna C.; D'Adderio, Leo P.; Rysman, Jean F.; Dietrich, Stefano
Affiliazioni autoriCasella Daniele, SERCO SpA Tutti gli altri, CNR - ISAC Roma
Autori CNR e affiliazioni
  • inglese
AbstractThis 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.
Lingua abstractinglese
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Pagine totali21
RivistaRemote sensing (Basel)
Attiva dal 2009
Editore: Molecular Diversity Preservation International - Basel
Lingua: inglese
ISSN: 2072-4292
Titolo chiave: Remote sensing (Basel)
Titolo proprio: Remote sensing. (Basel)
Titolo abbreviato: Remote sens. (Basel)
Numero volume della rivista10
Fascicolo della rivista7
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000440332500145)
Parole chiavesatellite precipitation retrieval, neural networks, GPM, GMI, remote sensing
Link (URL, URI)
Titolo parallelo-
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Data di accettazione-
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Strutture CNR
  • ISAC — Istituto di scienze dell'atmosfera e del clima
Moduli/Attività/Sottoprogetti CNR
  • DTA.AD001.193.001 : H-SAF CDOP3
Progetti Europei-
Descrizione: pdf
Tipo documento: application/pdf