Abstract
Abstract. Precipitation retrievals based on measurements from microwave (MW) radiometers onboard low-Earth-orbit (LEO) satellites can reach high level of accuracy – especially regarding convective precipitation. At the present stage though, these observations cannot provide satisfactory coverage of the evolution of intense and rapid precipitating systems. As a result, the obtained precipitation retrievals are often of limited use for many important applications – especially in supporting authorities for flood alerts and weather warnings. To tackle this problem, over the past two decades several techniques have been developed combining accurate MW estimates with frequent infrared (IR) observations from geosynchronous (GEO) satellites, such as the European Meteosat Second Generation (MSG). In this framework, we have developed a new fast and simple precipitation retrieval technique which we call Passive Microwave – Global Convective Diagnostic, (PM-GCD). This method uses MW retrievals in conjunction with the Global Convective Diagnostic (GCD) technique which discriminates deep convective clouds based on the difference between the MSG water vapor (6.2 μm) and thermal-IR (10.8 μm) channels. Specifically, MSG observations and the GCD technique are used to identify deep convective areas. These areas are then calibrated using MW precipitation estimates based on observations from the Advanced Microwave Sounding Unit (AMSU) radiometers onboard operational NOAA and Eumetsat satellites, and then finally propagated in time with a simple tracking algorithm. In this paper, we describe the PM-GCD technique, analyzing its results for a case study that refers to a flood event that struck the island of Sicily in southern Italy on 1–2 October 2009.
Highlights
It is well known that precipitation retrievals based on measurements from space-borne microwave (MW) radiometers can reach a high level of accuracy for convective precipitation (Ebert et al, 1996; Smith et al, 1998; Kummerow et al, 2001)
The technique can be considered as an evolution of the High Precipitation NAW Areas (HPNA) method (Porcuet al., 1999; Kotroni et al, 2005), which is based on the NegriAdler-Wetzel (NAW) technique (Negri et al, 1984) to define low and high precipitation areas from thermal-IR GEO
The PM-Global Convective Diagnostic (GCD) technique calculates one couple of α and β parameters for each cloud and for each time a new MW observation is available. These two figures show that the rain intensity pattern within each PM-GCD precipitation area is very similar to that retrieved from the corresponding Advanced Microwave Sounding Unit (AMSU) observation; in particular, the most convective regions that are characterized by the largest precipitation values in the PM-GCD approach tend to match the AMSUbased high precipitation regions, with the significant exception of the convective cell over south-eastern Sicily in Fig. 5 – as a result, the highest PM-GCD rain intensities within this cell are significantly lower than those retrieved from AMSU
Summary
It is well known that precipitation retrievals based on measurements from space-borne microwave (MW) radiometers can reach a high level of accuracy for convective precipitation (Ebert et al, 1996; Smith et al, 1998; Kummerow et al, 2001) These observations are taken from low-Earth-orbit (LEO) satellites and do not provide satisfactory coverage of rapidly evolving precipitation systems. In spite of the considerable number of existing MWIR combined techniques, it is very important to develop simple, operator-oriented satellite monitoring tools in order to provide guidance and support to the authorities in raising flood alerts This becomes vital whenever there is a lack of an adequate radar network or in the presence of a complex orography, as in the Mediterranean area – see, for instance, the European Commission RISKMED (http://www.riskmed.net/results.asp) and FLASH (http://flash-eu.tau.ac.il/) projects. A preliminary evaluation of the technique has been performed by comparing the evolving PM-GCD rain maps with the corresponding AMSU-based retrievals
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