Abstract
Abstract. 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 the 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 thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.
Highlights
Clouds and precipitation play a very important role in the global water and energy cycle
The purpose of this study is to describe a new algorithm based on a neural network (NN) approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation applied to AMSU/Microwave Humidity Sounder (MHS) observations, and to examine its performance for specific case studies over the European/Mediterranean area (25◦ N to 75◦ N latitude, 25◦ W to 45◦ E longitude)
The design, the characteristics and the performance of a new algorithm (PNPR) for surface precipitation estimation from cross-track passive microwave radiometers based on a single neural network for all types of surface background have been presented
Summary
Clouds and precipitation play a very important role in the global water and energy cycle. The third approach is based on the use of NNs (Hall et al, 1999; Staelin et al, 1999; Sorooshian et al, 2000; Chen and Staelin, 2003; Hong et al, 2004; Blackwell and Chen, 2005; Sussuravadee and Staelin, 2007, 2008a, b, 2009, 2010; Krasnopolsky et al, 2008; Leslie et al, 2008) This approach originates from the consideration that an exact relation between surface rain rate and observed brightness temperatures is nonlinear and difficult to evaluate, as precipitation is one of the most difficult of all atmospheric variables to retrieve. The purpose of this study is to describe a new algorithm based on a NN approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation applied to AMSU/MHS observations, and to examine its performance for specific case studies over the European/Mediterranean area (25◦ N to 75◦ N latitude, 25◦ W to 45◦ E longitude).
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