The validation service of the hydrological SAF geostationary and polar satellite precipitation products

Abstract

Abstract. The development phase (DP) of the EUMETSAT Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF) led to the design and implementation of several precipitation products, after 5 yr (2005)(2006)(2007)(2008)(2009)(2010) of activity. Presently, five precipitation estimation algorithms based on data from passive microwave and infrared sensors, on board geostationary and sun-synchronous platforms, function in operational mode at the H-SAF hosting institute to provide near real-time precipitation products at different spatial and temporal resolutions.In order to evaluate the precipitation product accuracy, a validation activity has been established since the beginning of the project. A Precipitation Product Validation Group (PPVG) works in parallel with the development of the estimation algorithms with two aims: to provide the algorithm developers with indications to refine algorithms and products, and to evaluate the error structure to be associated with the operational products.In this paper, the framework of the PPVG is presented: (a) the characteristics of the ground reference data available to H-SAF (i.e. radar and rain gauge networks), (b) the agreed upon validation strategy settled among the eight European countries participating in the PPVG, and (c) the steps of the validation procedures. The quality of the reference data is discussed, and the efforts for its improvement are outlined, with special emphasis on the definition of a ground radar Published by Copernicus Publications on behalf of the European Geosciences Union. S. Puca et al.:The validation service of the hydrological SAF geostationary products quality map and on the implementation of a suitable rain gauge interpolation algorithm. The work done during the H-SAF development phase has led the PPVG to converge into a common validation procedure among the members, taking advantage of the experience acquired by each one of them in the validation of H-SAF products. The methodology is presented here, indicating the main steps of the validation procedure (ground data quality control, spatial interpolation, upscaling of radar data vs. satellite grid, statistical score evaluation, case study analysis).Finally, an overview of the results is presented, focusing on the monthly statistical indicators, referred to the satellite product performances over different seasons and areas.

HOAPS and ERA-Interim precipitation over the sea: validation against shipboard in situ measurements

“…Decorrelation lengths had been derived from in situ precipitation data over the Baltic Sea: 17 km based on disdrometer 8 min time series (Bumke et al, 2012), 25 km for convective precipitation and 46-68 km for stratiform/frontal precipitation, based on 8 min time series of SRG measurements (Clemens and Bumke, 2002) on board merchant ships. Using 46 km as the decorrelation length for stratiform/frontal precipitation, these three numbers give an average decorrelation length of about 30 km which agrees well with a study of Puca et al (2014). A decorrelation length of 30 km corresponds to a temporal correlation length of 45 min, assuming a merchant ship's speed to be of the order of 20 Kn.…”

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

“…The SRI product is derived applying a reflectivity-rainfall (Z-R) relationship to the Lowest Beam Map (LBM), in other words the reflectivity values at the lowest level of the corrected radar volumes. The SRI product used here represents the best estimate from the radar network available for the period under analysis, and it has already been used to validate satellite rainfall estimates (Cimini et al, 2013), including EUMETSAT H-SAF products (Puca et al, 2014). Procedures to improve the quality of the SRI product, including attenuation compensation, polarimetric rainfall inversion techniques, and adaptive algorithms to retrieve the mean vertical profiles of reflectivity have recently been developed at DPC (Vulpiani et al, 2012;Rinollo et al, 2013).…”

“…The cloud Mask Coupling of Statistical and Physical methods algorithm (MACSP; Ricciardelli et al, 2008;) is used for distinguishing cloudy from non-cloudy pixels. The version used for RainCEIV purposes is called C_MACSP, which stands for cloud Classification Mask Coupling of Statistical and Physical methods.…”

“…3.2.1 (Sect. 3.4) and Table 4 (Table 7), respectively, of Ricciardelli et al (2008), but the training data set has been updated in order to build the training samples for the convective cloud class. The training samples were collected in the Mediterranean Basin, where RainCEIV operates.…”

“…The SEVIRI images listed in Table 5 of Ricciardelli et al (2008), and in particular the ones used for the training of the Mediterranean Basin (enclosed in the areas B, C, and G of Tables 5 and 6). …”