Articles | Volume 9, issue 11
https://doi.org/10.5194/amt-9-5441-2016
https://doi.org/10.5194/amt-9-5441-2016
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

Paolo Sanò, Giulia Panegrossi, Daniele Casella, Anna C. Marra, Francesco Di Paola, and Stefano Dietrich

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Cited articles

Aires, F., Aznay, O., Prigent, C., Paul, M., and Bernardo F.: Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys. Res., 117, D18304, https://doi.org/10.1029/2011JD017188, 2012.
Anders, U. and Korn, O.: Model selection in neural networks, Neural Networks, 12, 309–323, 1999.
Bellerby, T., Todd, M., Kniveton, D., and Kidd, C.,: Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network, J. Appl. Meteorol., 39, 2115–2128, https://doi.org/10.1175/1520-0450(2001)040<2115:REFACO>2.0.CO;2, 2000.
Bellerby, T. J.: Satellite rainfall uncertainty estimation using an artificial neural network, J. Hydrometeorol., 8, 1397–1412, https://doi.org/10.1175/2007JHM846.1, 2007.
Bennartz, R. and Bauer, P.: Sensitivity of microwave radiances at 85-183 GHz to precipitating ice particles, Radio Sci., 38, 8075, https://doi.org/10.1029/2002RS002626, 2003.
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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.