Search

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring. [ABSTRACT FROM AUTHOR]

Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances. [ABSTRACT FROM AUTHOR]

H-SAF is the Satellite Application Facility on support to operational Hydrology and water management supervised and coordinated by EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) since 2005. Currently, the program entered in its Third Continuous Development and Operation Phase (CDOP-3), which will end on February 2022. During this phase, H-SAF contributes to the EUMETSAT MTG (Meteosat Third Generation) program by providing operational precipitation products based on combined microwave/infrared (MW/IR) techniques for the high spatial/temporal/spectral resolution Flexible Combined Imager (FCI) and for the MTG Lightning Imager (LI). Moreover, in view of the EUMETSAT Polar System Second Generation (EPS-SG) satellite mission, day-1 precipitation products for the Microwave Sounder (MWS), and Microwave Imager (MWI) will be delivered to provide precipitation estimates on a global scale. The collaboration with the NASA Precipitation Measurement Mission (PMM) Research Program promotes fruitful interactions on several critical aspects in precipitation retrieval algorithm development between H SAF and GPM (Global Precipitation Measurement). The nearly-global observational datasets built from coincident active and passive spaceborne MW observations are used as reference for the development of the EPS-SG precipitation products, and for the optimization of the existing PMW (Passive Microwave) products. The current work fits in this context. The H-SAF validation activities are performed by the Product Validation Group (PVG) composed by experts from the National Meteorological and Hydrological European Institutes under the coordination of the Italian Civil Protection Department (DPC). The product validation methodology is based on comparisons with radar and rain gauge data to produce yearly statistics (multi-categorical and continuous scores) through the use of a Unique Common Code (UCC) developed by the PVG. Recently, the validation over the MSG full disk area was performed with respect to the 2A DPR products. Comparison results obtained by the two H-SAF neural network algorithms retrieved by MW cross-track scanners (AMSU/MHS for H02B and ATMS for H18) over the European and full disk area are shown. [ABSTRACT FROM AUTHOR]