Articles | Volume 9, issue 11
https://doi.org/10.5194/amt-9-5441-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-5441-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Nov 2016
Research article |
| 14 Nov 2016
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
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Giulia Panegrossi
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Daniele Casella
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Anna C. Marra
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
Francesco Di Paola
Institute of Methodologies for Environmental Analysis (IMAA), Italian
National Research Council of Italy (CNR), C.da S.Loja, Tito Scalo, 85050
Potenza, Italy
Stefano Dietrich
Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy (CNR), 00133 Rome, Italy
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Cited
19 citations as recorded by crossref.
- Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data V. Gorooh et al. 10.1175/JHM-D-21-0194.1
- A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements L. D’Adderio et al. 10.3390/rs15245662
- A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data M. Sehad & S. Ameur 10.1016/j.asr.2019.11.018
- Daily precipitation estimation through different microwave sensors: Verification study over Italy L. Ciabatta et al. 10.1016/j.jhydrol.2016.12.057
- A Machine Learning Snowfall Retrieval Algorithm for ATMS P. Sanò et al. 10.3390/rs14061467
- The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer P. Sanò et al. 10.3390/rs10071122
- Validation of HOAPS Rain Retrievals against OceanRAIN In-Situ Measurements over the Atlantic Ocean K. Bumke et al. 10.3390/atmos10010015
- The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification L. Bagaglini et al. 10.3390/rs13091701
- Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements E. Alerskans et al. 10.1016/j.rse.2022.113220
- Validation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products over Italy M. Petracca et al. 10.1175/JHM-D-17-0144.1
- 风云气象卫星光学遥感数据的智能处理与典型应用综述（特邀） 罗. Luo Chuyao et al. 10.3788/AOS241175
- Performances of GPM satellite precipitation over the two major Mediterranean islands D. Caracciolo et al. 10.1016/j.atmosres.2018.06.010
- Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications F. Paola et al. 10.1109/JSTARS.2024.3386629
- Passive Microwave Rainfall Error Analysis Using High-Resolution X-Band Dual-Polarization Radar Observations in Complex Terrain Y. Derin et al. 10.1109/TGRS.2017.2763622
- Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil L. Martins Costa do Amaral et al. 10.3390/rs10111743
- The Cloud Dynamics and Radiation Database Algorithm for AMSR2: Exploitation of the GPM Observational Dataset for Operational Applications D. Casella et al. 10.1109/JSTARS.2017.2713485
- GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm S. Pfreundschuh et al. 10.5194/amt-15-5033-2022
- Multiscale and Multilevel Feature Fusion Network for Quantitative Precipitation Estimation With Passive Microwave Z. Wang et al. 10.1109/TGRS.2024.3396379
- MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique F. Di Paola et al. 10.3390/rs10091398
19 citations as recorded by crossref.
- Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data V. Gorooh et al. 10.1175/JHM-D-21-0194.1
- A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements L. D’Adderio et al. 10.3390/rs15245662
- A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data M. Sehad & S. Ameur 10.1016/j.asr.2019.11.018
- Daily precipitation estimation through different microwave sensors: Verification study over Italy L. Ciabatta et al. 10.1016/j.jhydrol.2016.12.057
- A Machine Learning Snowfall Retrieval Algorithm for ATMS P. Sanò et al. 10.3390/rs14061467
- The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer P. Sanò et al. 10.3390/rs10071122
- Validation of HOAPS Rain Retrievals against OceanRAIN In-Situ Measurements over the Atlantic Ocean K. Bumke et al. 10.3390/atmos10010015
- The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification L. Bagaglini et al. 10.3390/rs13091701
- Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements E. Alerskans et al. 10.1016/j.rse.2022.113220
- Validation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products over Italy M. Petracca et al. 10.1175/JHM-D-17-0144.1
- 风云气象卫星光学遥感数据的智能处理与典型应用综述（特邀） 罗. Luo Chuyao et al. 10.3788/AOS241175
- Performances of GPM satellite precipitation over the two major Mediterranean islands D. Caracciolo et al. 10.1016/j.atmosres.2018.06.010
- Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications F. Paola et al. 10.1109/JSTARS.2024.3386629
- Passive Microwave Rainfall Error Analysis Using High-Resolution X-Band Dual-Polarization Radar Observations in Complex Terrain Y. Derin et al. 10.1109/TGRS.2017.2763622
- Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil L. Martins Costa do Amaral et al. 10.3390/rs10111743
- The Cloud Dynamics and Radiation Database Algorithm for AMSR2: Exploitation of the GPM Observational Dataset for Operational Applications D. Casella et al. 10.1109/JSTARS.2017.2713485
- GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm S. Pfreundschuh et al. 10.5194/amt-15-5033-2022
- Multiscale and Multilevel Feature Fusion Network for Quantitative Precipitation Estimation With Passive Microwave Z. Wang et al. 10.1109/TGRS.2024.3396379
- MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique F. Di Paola et al. 10.3390/rs10091398
Latest update: 20 Nov 2024
Short summary
The 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.
The objective of this paper is to describe the development and evaluate the performance of a...
