Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloThe new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: Description and verification study over Europe and Africa using GPM and TRMM spaceborne radars
Anno di pubblicazione2016
  • Elettronico
  • Cartaceo
Autore/iSanò P.; Panegrossi G.; Casella D.; Marra A.C.; Di Paola F.; Dietrich S.
Affiliazioni autoriInstitute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), Rome, 00133, Italy; Institute of Methodologies for Environmental Analysis (IMAA), Italian National Research Council of Italy (CNR), C.da S.Loja, Tito Scalo, Potenza, 85050, Italy
Autori CNR e affiliazioni
  • inglese
AbstractThe objective of this paper is to describe the development and evaluate the performance of a totally new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track ATMS radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered . The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area, the statistical analysis was carried out for a two-year (2013-2014) dataset of coincident observations, over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h-1 over ocean and 1.11 mm h-1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h-1 over vegetated land and 0.71 mm h-1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with general better estimate of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.
Lingua abstractinglese
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Pagine da5441
Pagine a5460
Pagine totali20
RivistaAtmospheric measurement techniques (Print)
Attiva dal 2008
Editore: Copernicus Publications - Göttingen
Paese di pubblicazione: Germania
Lingua: inglese
ISSN: 1867-1381
Titolo chiave: Atmospheric measurement techniques (Print)
Titolo proprio: Atmospheric measurement techniques. (Print)
Titolo abbreviato: Atmos. meas. tech. (Print)
Titolo alternativo: AMT (Print) (Print)
Numero volume della rivista9
Fascicolo della rivista11
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84995953651)
  • ISI Web of Science (WOS) (Codice:000387981100001)
Parole chiaveMicrowave radiometry, Rain, remote sensing, Artificial neural network, algorithms, atms
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Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IMAA — Istituto di metodologie per l'analisi ambientale
  • ISAC — Istituto di scienze dell'atmosfera e del clima
Moduli/Attività/Sottoprogetti CNR
  • TA.P06.017.005 : Meteorologia da satellite
  • TA.P06.021.001 : GdR: Telerilevamento Satellitare di Nubi e Precipitazioni
Progetti Europei-