Abstract
espanolVarios productos de precipitacion utilizan radiancias y reflectancias obtenidas del sensor SEVIRI (Spinning Enhanced Visible and Infrared Imager) para estimar la precipitacion de caracter convectivo. Es sabido que el uso directo de estos valores en algoritmos de precipitacion sobreestiman el area y subestiman la intensidad de precipitacion. Para atenuar estos efectos, la version mas reciente (2013) del paquete de software NWC SAF/MSG (Satellite Application Facility on Support to Nowcasting & Very Short Range Forecasting) incluye un nuevo algoritmo diurno que se aprovecha de los avances en la estimacion de los parametros de microfisica de nubes, en particular un mejor conocimiento del Radio Efectivo (Reff), el espesor optico de la nube (COT) y la fase del agua. El algoritmo mejorado, conocido como Intensidad de Precipitacion Convectiva a partir de las Propiedades Fisicas de la Nube (CRPh), utilitza estas propiedades de la Cima de la Nube (CPP) para estimar intensidades de precipitacion de nubes convectivas usando la informacion de un pixel de SEVIRI (alrededor de 3 km en el nadir). Este trabajo presenta las novedades de este algoritmo, hace una comparacion de los resultados con los de la version anterior en el paquete NWC SAF/MSG y una validacion con datos independientes provenientes de la red espanola de Radar operada por AEMET. Los resultados obtenidos para 46 tormentas sugieren que la CRPh proporciona estimaciones mas precisas que el algoritmo anterior, siendo por tanto mas util para un cierto numero de apliaciones cuantitativas. EnglishSeveral precipitation products use radiances and reflectances obtained from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to estimate convective precipitation. The direct use of these physical quantities in precipitation algorithms is known to generate an overestimation of the precipitation area and an underestimation of the rainfall rates. In order to extenuate these issues, the most recent Satellite Application Facility on Support to Nowcasting & Very Short Range Forecasting (NWC SAF/MSG) software package (version 2013) includes a new day-time algorithm that takes advantage of advances in cloud microphysics estimation, namely a better knowledge of Effective Radius (Ref f ), Cloud Optical Thickness (COT) and Water Phase. The improved algorithm, known as Convective Rainfall Rate from Cloud Physical Properties (CRPh), uses such Cloud Top Physical Properties (CPP) to estimate rainfall rates from convective clouds on a SEVIRI pixel basis (about 3 km at nadir). This paper presents the novelties of the new algorithm and provides both a comparison of the product with the previous versions in the NWC SAF/MSG software package, and a validation with independent ground radar data from the Spanish Radar Network operated by AEMET. Results obtained over 46 storms suggest that the CRPh provides more precise estimates than the previous algorithm, thus being more suitable for a number of quantitative applications.
Highlights
Heavy rain and floods can cause power cuts, economic losses, infectious diseases and even casualties, while droughts affect health and cause hunger in vast areas of the planet
Given the information provided in the previous section, it is easy to conclude that Nowcasting & Very Short Range Forecasting (NWC SAF) convective precipitation estimations have been improved by using cloud top microphysical information
The Convective Rainfall Rate (CRR) problem with respect to the high dependence on IR brightness temperature disappeared with the CRPh: warm top rainy clouds are detected and cold ring signals do not appear as precipitation patterns
Summary
Heavy rain and floods can cause power cuts, economic losses, infectious diseases and even casualties, while droughts affect health and cause hunger in vast areas of the planet. Precise measurements of precipitation may help to prevent some of these societal impacts by providing both weather prediction and climate models with good quality data. Rainfall and solid precipitation are the primary input to hydrological models predicting stream flow, and early warning systems for landslides benefit from a good knowledge of recent precipitation. Irrigation scheduling is contingent upon recent and expected rainfall in the near future, especially in semiarid environments. In the realm of weather, precipitation estimates are used for nowcasting and for assimilation into global and regional models, aiming to improve the forecasts. Precipitation science is at the crossroads of different scientific disciplines including hydrology, numerical modelling, climate change, remote sensing, and renewable energy research (Tapiador et al, 2012)
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