Abstract
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.
Highlights
Accurate precipitation measurements are essential for the validation of global climate models and for understanding the natural variability of the earth’s climate
In this paper, precipitation estimates derived from the Italian ground radar network (IT ground radars (GRs)) 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
This paper describes an algorithm, named RAINBOW (RAdar INfrared Blending algorithm for Operational Weather monitoring) combining the data collected by SEVIRI and by the Italian ground-based radars network, coordinated by the Italian Department of Civil Protection (IT GR) to provide precipitation estimation over Italy
Summary
Accurate precipitation measurements are essential for the validation of global climate models and for understanding the natural variability of the earth’s climate. The National Oceanic and Atmospheric Administration (NOAA) Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm estimates rainfall at a fine temporal resolution using PMW (SSM/I—-Special Sensor Microwave/Imager) and GEO (GOES) satellites It uses SSM/I data for rain/no-rain pixels classification, and GOES data to calibrate the relationship between brightness temperature and rain rate via linear regression for the precipitating pixels [39,40]. The “morphing” technique is based on the evidence that IR data, locally updated using PMW-based rainfall measurements, can be employed to measure cloud movement, propagating forward in time the rain field, between the consecutive LEO PMW satellite overpasses [37,51,52,53,54] This technique derives estimates of precipitation from infrared data when passive microwave information is unavailable.
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