Abstract
In this study, we investigate the ability of several convective initiation predictors based on satellite infrared observations to distinguish convective weak precipitation events from those leading to intense rainfall. The two types of precipitation are identified according to hourly rainfall, respectively less than 10 mm and greater than 30 mm. The analysis is conducted on a representative dataset containing 92 severe and weak precipitation events collected over the Italian peninsula in the period 2016–2019 over June-September. The events are selected to be short-lived (i.e., less than 12 h) and localized (i.e., less than 50×50km2). Italian National Radar Network products, namely the Vertical Maximum Intensity (VMI) and the Surface Rain Total (SRT) variables (from Dewetra Platform by CIMA, Italian Civil Protection Department), are used as indicators of convection (i.e., VMI greater than 35 dBZ echo intensity) and cumulated rainfall, respectively. The considered predictors are linear combinations of spectral infrared channels measured with the Rapid Scan Service (RSS) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) geostationary satellites. We select a 5×5 SEVIRI pixel-box centered on the storm core and perform a statistical analysis of the predictors up to 2.5 h around the event occurrence. We demonstrate that some of the proxies—describing growth and glaciation storm properties—show few degrees contrast between severe and nonsevere precipitation cases, hence carrying significant information to help discriminate the two types. We design a threshold scheme based on the three most informative predictors to distinguish weak and strong precipitation events. This analysis yields accuracy higher than 0.6 and the probability of false detection lower than 0.26; in terms of reducing false alarms, this method shows slight better performances compared to related works, at the expense of a lower probability of detection. The overall results, however, show limited capability for these infrared proxies as stand-alone predictors to distinguish severe from nonsevere precipitation events. Nonetheless, these may serve as additional tools to reduce the false alarm ratio in nowcasting algorithms for convective orographic storms. This study also provides further insight into the correlation between early infrared fields signatures prior to convection and subsequent evolution of the storms, extending previous works in this field.
Highlights
The large number of convective rainfall events causing intense precipitation over the past few years represents a threat to society, as high-impact storms are generally accompanied by hail, strong winds and lightning, and can lead to flash-floods, in complex orography areas, with dramatic socio-economical consequences
We perform a study on convective initiation infrared field indicators, in order to evaluate their skill to discriminate convective events evolving in severe precipitation from those causing weak rainfall only
The analyzed proxies consist of linear combinations of passive remote sensing infrared observations with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) Rapid Scan aboard Meteosat Second Generation (MSG) geostationary satellites
Summary
The large number of convective rainfall events causing intense precipitation over the past few years represents a threat to society, as high-impact storms are generally accompanied by hail, strong winds and lightning, and can lead to flash-floods, in complex orography areas, with dramatic socio-economical consequences. A crucial aspect in nowcasting convective precipitation events is the assessment of their potential in terms of rainfall severity, aimed at providing means to prevent false alarms, which have socio-economic impacts e.g., on the aviation industry as well as agriculture, transport and public safety. This area of study is very attractive for the scientific community and to the present there is still need for further investigation. Blending techniques are widespread [33,34,35,36,37], exploiting the advantages of each of the above methods, depending on the time horizon of the forecast as well as the availability of satellite or radar observational data
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