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The Passive Microwave Neural Network Precipitation Retrieval (Pnpr) Algorithm for Amsu/Mhs Observations: Description and Application to European Case Studies : Volume 7, Issue 9 (15/09/2014)

By Sanò, P.

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Book Id: WPLBN0004000336
Format Type: PDF Article :
File Size: Pages 61
Reproduction Date: 2015

Title: The Passive Microwave Neural Network Precipitation Retrieval (Pnpr) Algorithm for Amsu/Mhs Observations: Description and Application to European Case Studies : Volume 7, Issue 9 (15/09/2014)  
Author: Sanò, P.
Volume: Vol. 7, Issue 9
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Paola, F. D., Panegrossi, G., Mugnai, A., Petracca, M., Milani, L., Sanò, P.,...Dietrich, S. (2014). The Passive Microwave Neural Network Precipitation Retrieval (Pnpr) Algorithm for Amsu/Mhs Observations: Description and Application to European Case Studies : Volume 7, Issue 9 (15/09/2014). Retrieved from http://www.ebooklibrary.org/


Description
Description: Institute of Atmospheric Sciences and Climate (ISAC), Italian National Research Council (CNR), 00133 Rome, Italy. The purpose of this study is to describe a new algorithm based on a Neural Network approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of AMSU-A and MHS channels, and their combinations, including the TB differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval.

Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, particularly valuable over the extremely variable environmental and meteorological conditions of the region.

In particular, the PNPR is particularly efficient in: (1) screening and retrieval of precipitation over different background surfaces, (2) identification and retrieval of heavy rain for convective events, (3) identification of precipitation over cold/iced background, with some uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi NPP ATMS) and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to Africa and Southern Atlantic regions, and an extended validation over these regions (using two years of data acquired by the Tropical Rainfall Measuring Mission Precipitation Radar for comparison) is subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely through extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Group.


Summary
The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

Excerpt
Anagnostou, E. N. and Krajewski, W. F.: Real-time radar rainfall estimation. Part I: Algorithm formulation, J. Atmos. Ocean. Tech., 16, 189–197, 1999.; Anders, U. and Korn, O.: Model selection in neural networks, Neural Netw., 12, 309–323, 1999.; Bauer, P., Moreau, E., Di Michele, S.: Hydrometeor retrieval accuracy using microwave window and sounding channel observations, J. Appl. Meteorol., 44, 1016–1032, doi:10.1175/JAM2257.1, 2005.; Bellerby, T. J.: Satellite rainfall uncertainty estimation using an artificial neural network, J. Hydrometeorol., 8, 1397–1412, 2007.; Bennartz, R. and Bauer, P.: Sensitivity of microwave radiances at 85–183 GHz to precipitating ice particles, Radio Sci., 38, 8075, doi:10.1029/2002RS002626, 2003.; Bennartz, R. and Petty, G. W.: The sensitivity of microwave remote sensing observations of precipitation to ice particle size distributions, J. Appl. Meteorol., 40, 345–364, 2001.; Blackwell, W. J. and Chen, F. W.: Neural network applications in high-resolution atmospheric remote sensing, Lincoln Lab. J., 15, 299–322, 2005.; Burns, B. A., Wu, X., and Diak, G. R.: Effects of precipitation and cloud ice on brightness temperatures in AMSU moisture channels, IEEE T. Geosci. Remote, 35, 6, 1429–1437, 1997.; Casella, D., Dietrich, S., Formenton, M., Mugnai, A., Panegrossi, G., Sanò, P., Smith, E. A., and Tripoli, G. J.: Verification of Cloud Dynamics and Radiation Database (CDRD) passive microwave precipitation retrieval algorithm using TRMM satellite radar and radiometer measurements over Southern Mediterranean Basin, Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 2012 12th Specialist Meeting, 1–4, 2012.; Casella, D., Panegrossi, G., Sanò, P., Mugnai, A., Smith, E. A., Tripoli, G. J., Dietrich, S., Formenton, M., Leung, W. Y., and Mehta, A.: Transitioning from CRD to CDRD in bayesian retrieval of rainfall from satellite passive microwave measurements: Part 2. Overcoming database profile selection ambiguity by consideration of meteorological control on microphysics, IEEE T. Geosci. Remote, 51, 4650–4671, 2013.; Casella, D., Sanò, P., Panegrossi, G., Milani, L., Petracca, M., Dietrich, S.: A novel algorithm for detection of precipitation in tropical regions using PMW radiometers, Atmos. Meas. Tech. Discuss., submitted, 2014.; Chen, F. W. and Staelin, D. H.: AIRS/AMSU/HSB precipitation estimates, IEEE T. Geosci. Remote, 41, 410–417, 2003.; Deeter, M. N. and Vivekanandan, J.: AMSU-B observations of mixed-phase clouds over land, J. Appl. Meteorol., 44, 72–85, 2005.; Chen, Y., Aires, F., Francis, J. A., and Miller, J. R.: Observed relationships between artic longwave cloud forcing and cloud parameters using a neural network, J. Climate, 4087–4104, 2006.; Di Tommaso, E., Romano, F., and Cuomo, V.: Rainfall estimation from satellite passive microwave observations in the range 89 GHz to 190 GHz, J. Geophys. Res., 114, D18203, 2009.; Ferraro, R. R.: The Status of the NOAA/NESDIS Operational AMSU Precipitation Algorithm, 2nd Workshop of the International Precipitation Working Group, Monterey, 9 pp., 2004.; Ferraro, R. R. and Marks, G. F.: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements, J. Atmos. Ocean. Tech., 12, 755–770, 1995.; Ferraro, R. R., Weng, F., Grody, N. C., and Zhao, L.: Precipitation characteristics over land from the NOAA-15 AMSU sensor, Geophys. Res. Lett., 27, 2669–2672, 2000.; Ferraro, R. R., Weng, F., Grody, N. C., Zhao, L., Meng, H., Kongoli, C., Pellegrino, P., Qiu, S., and Dean, C.: NOAA operational hydrological products derived from the advanced microwave sounding unit, IEEE T. Geosci. Remote, 43, 5, 1036–1049, 2005.; Funatsu, B. M., Claud, C., and Chaboureau, J.-P.: Potential of Advanced Microwave Sounding Unit to identify precipit

 

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