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

• Published in: Atmospheric measurement techniques 2015-02, Vol.8 (2), p.837-857
• Main Authors: Sanò, P, Panegrossi, G, Casella, D, Di Paola, F, Milani, L, Mugnai, A, Petracca, M, Dietrich, S
• Format: Article
• Language: English
• Subjects: Advanced Microwave Sounding Unit; Algorithms; Artificial neural networks; Atmospheric precipitations; Brightness temperature; Case studies; Channels; Climatology; Cloud computing; Computer simulation; Data acquisition; Data processing; Evaluation; Gauges; Heavy rainfall; Humidity; Hydrology; Identification; Mathematical models; Measuring instruments; Meteorological conditions; Meteorological satellites; Microwave communications; Microwave imagery; Microwave sounding; Neural networks; Precipitation; Precipitation rate; Radar; Radiation; Radiative transfer; Radiometers; Rain; Rain gauges; Rainfall; Regions; Reliability analysis; Reliability aspects; Retrieval; Satellites; Scanning; Snow cover; Surface radiation temperature; Training; Tropical climate; Tropical rainfall; Weather; Weather conditions
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-8-837-2015

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 Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (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, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. 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; and (3) identification of precipitation over a cold/iced background, with increased 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 National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is t