Convective Initiation Proxies for Nowcasting Precipitation Severity Using the MSG-SEVIRI Rapid Scan

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.

What Is a Cloud? Toward a More Precise Definition

“…Clouds are observed from space actively and passively in all suitable atmospheric windows from the visible to millimeter wavelength and also in absorption bands of atmospheric absorbers of nearly constant mixing ratio (CO 2 , O 2 ) (e.g., Jayadevan et al 2019;Pingfang et al 2019; T.-X. Zhang et al 2019;Chen et al 2020;Gallucci et al 2020;Painemal et al 2021;Tarrio et al 2020;Yuchechen et al 2020;Painemal et al 2021;Wang et al 2020;Weaver et al 2020;Dubovik et al 2021).…”

“…However, the spatial and temporal coverage of research satellites, including the A-Train spacecraft, is limited with only near-nadir-viewing capability when compared to operational satellites. Other synergisms using different data sources were applied by Yuchechen et al (2020), Chen et al (2020), Gallucci et al (2020), Painemal et al (2021), and others with promising results.…”

Convective Initiation Nowcasting in South China Using Physics‐Augmented Random Forest Models and Geostationary Satellites

“…The traditional methods (Mecikalski et al, 2016;Senf & Deneke, 2017), which focus on the satellite-based characterization of convective storms, together with machine learning methods in CI nowcasting usually suffer from high FARs (Cancelada et al, 2020;Lee et al, 2017), while some methods show a good performance in FAR exhibit lower POD (Gallucci et al, 2020). Li et al (2024) have achieved significant success in CI warning research (high POD and low FAR), but researchers have focused on a half-hour lead time, limiting the timeliness of satellite data in CI to 2 hr.…”

“…The traditional methods (Mecikalski et al., 2016; Senf & Deneke, 2017), which focus on the satellite‐based characterization of convective storms, together with machine learning methods in CI nowcasting usually suffer from high FARs (Cancelada et al., 2020; Lee et al., 2017), while some methods show a good performance in FAR exhibit lower POD (Gallucci et al., 2020). Li et al.…”