SEVIRI rainfall retrieval and validation using weather radar observations

This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The Precipitation Properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates. An independent dataset of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it for SEVIRI.

For a two-month period the ability of SEVIRI to discriminate precipitating from non-precipitating is evaluated using weather radar over the Netherlands. In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period. During the observation period the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (corr. ≈ 0.90), while weaker correlations (corr. ≈ 0.63) are found between the spatially mean rain rate retrievals from these instruments. The combined use of information on CWP, cloud thermodynamic phase and particle size for the detection of precipitation results in an increase in explained variance (~10%) and decrease in false alarms (~15%), as compared to detection methods that are solely based on a threshold CWP. At a pixel level the SEVIRI-retrievals have an acceptable accuracy of about 0.1 mm h-1 and a precision of about 0.8 mm h-1. It is argued that parts of the differences are caused by parallax shifts in the SEVIRI data, and irregularities in the weather radar data due to residual clutter. In future studies we intend to exploit the observations of the European weather radar network (OPERA) and extend this study to entire Europe.