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The purpose of this study is to describe a new algorithmbased on a neural network approach (Passive microwave Neural networkPrecipitation Retrieval – PNPR) for precipitation rate estimation fromAMSU/MHS observations, and to provide examples of its performance forspecific case studies over the European/Mediterranean area. The algorithmoptimally exploits the different characteristics of Advanced MicrowaveSounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels,and their combinations, including the brightnesstemperature (TB) differences of the 183.31 channels,with the goal of having a single neural network for different types ofbackground surfaces (vegetated land, snow-covered surface, coast and ocean).The training of the neural network is based on the use of a cloud-radiationdatabase, built from cloud-resolving model simulations coupled to aradiative transfer model, representative of the European and MediterraneanBasin precipitation climatology. The algorithm provides also the phase ofthe precipitation and a pixel-based confidence index for the evaluation ofthe reliability of the retrieval.  Applied to different weather conditions in Europe, the algorithm shows goodperformance both in the identification of precipitation areas and in theretrieval of precipitation, which is particularly valuable over the extremelyvariable environmental and meteorological conditions of the region.  The PNPR is particularly efficient in (1) screening andretrieval of precipitation over different background surfaces; (2)identification and retrieval of heavy rain for convective events; and (3)identification of precipitation over a cold/iced background, with increaseduncertainties affecting light precipitation. In this paper, examples of goodagreement of precipitation pattern and intensity with ground-based data(radar and rain gauges) are provided for four different case studies. Thealgorithm has been developed in order to be easily tailored to newradiometers as they become available (such as the cross-track scanning SuomiNational Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationallyvery efficient. PNPR has been recently extended for applications to the regions of Africaand the South Atlantic, and an extended validation over these regions(using 2 yr of data acquired by the Tropical Rainfall Measuring Missionprecipitation radar for comparison) is the subject of a paper in preparation.The PNPR is currently used operationally within the EUMETSAT HydrologySatellite Application Facility (H-SAF) to provide instantaneousprecipitation from passive microwave cross-track scanning radiometers. Itundergoes routinely thorough extensive validation over Europe carried out bythe H-SAF Precipitation Products Validation Team

atmospheric measurement techniques

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