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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
Publication Date:
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

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.

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

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