The 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
The objective of this paper is to describe the development and evaluate the performance of a completely 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 precipitat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-11-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/9/5441/2016/amt-9-5441-2016.pdf |
| _version_ | 1819065737765978112 |
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| author | P. Sanò G. Panegrossi D. Casella A. C. Marra F. Di Paola S. Dietrich |
| author_facet | P. Sanò G. Panegrossi D. Casella A. C. Marra F. Di Paola S. Dietrich |
| author_sort | P. Sanò |
| collection | DOAJ |
| description | The objective of this paper is to describe the development and evaluate the
performance of a completely 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 Advanced Technology Microwave Sounder (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.
<br><br>
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 2-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<sup>−1</sup> over ocean and
1.11 mm h<sup>−1</sup> 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<sup>−1</sup>
over vegetated land and 0.71 mm h<sup>−1</sup> 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 <i>R</i>
over the different surfaces, with generally better estimation 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. |
| first_indexed | 2024-12-21T15:51:13Z |
| format | Article |
| id | doaj.art-d741aff8aef14947b47f283a24d66caf |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-21T15:51:13Z |
| publishDate | 2016-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-d741aff8aef14947b47f283a24d66caf2022-12-21T18:58:14ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482016-11-0195441546010.5194/amt-9-5441-2016The 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 radarsP. Sanò0G. Panegrossi1D. Casella2A. C. Marra3F. Di Paola4S. Dietrich5Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), 00133 Rome, ItalyInstitute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), 00133 Rome, ItalyInstitute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), 00133 Rome, ItalyInstitute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), 00133 Rome, ItalyInstitute of Methodologies for Environmental Analysis (IMAA), Italian National Research Council of Italy (CNR), C.da S.Loja, Tito Scalo, 85050 Potenza, ItalyInstitute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy (CNR), 00133 Rome, ItalyThe objective of this paper is to describe the development and evaluate the performance of a completely 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 Advanced Technology Microwave Sounder (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. <br><br> 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 2-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<sup>−1</sup> over ocean and 1.11 mm h<sup>−1</sup> 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<sup>−1</sup> over vegetated land and 0.71 mm h<sup>−1</sup> 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 <i>R</i> over the different surfaces, with generally better estimation 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.https://www.atmos-meas-tech.net/9/5441/2016/amt-9-5441-2016.pdf |
| spellingShingle | P. Sanò G. Panegrossi D. Casella A. C. Marra F. Di Paola S. Dietrich The 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 Atmospheric Measurement Techniques |
| title | The 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 |
| title_full | The 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 |
| title_fullStr | The 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 |
| title_full_unstemmed | The 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 |
| title_short | The 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 |
| title_sort | 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 |
| url | https://www.atmos-meas-tech.net/9/5441/2016/amt-9-5441-2016.pdf |
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