Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part I: Infrared Fields

Abstract

Infrared (IR) data from the Meteosat Second Generation (MSG) satellite are used to understand cloud-top signatures for growing cumulus clouds prior to known convective initiation (CI) events, or the first occurrence of a ≥35-dBZ echo from a new convective cloud. In the process, this study proposes how MSG IR fields may be used to infer three physical attributes of growing cumuli, cloud depth, cloud-top glaciation, and updraft strength, with limited information redundancy. These three aspects are observed as unique signatures within MSG IR data, for which this study seeks to relate to previous research, as well as develop a new understanding on which subset of IR information best identifies these attributes. Data from 123 subjectively identified CI events observed during the 2007 Convection and Orograpically Induced Precipitation Study (COPS) field experiment conducted over southern Germany and northeastern France are processed, per convective cell, to meet this study’s objectives. A total of 67 IR “interest fields” are initially assessed for growing cumulus clouds, with correlation and principal component analyses used to highlight the top 21 fields that are considered the best candidates for describing the three attributes. Using between 6 and 8 fields per category, a method is then proposed on how growing convective clouds may be quantified per 3-km2 pixel (or per cumulus cloud object) toward inferring each attribute. No independent CI-nowcasting analysis is performed, which instead is the subject of ongoing research.

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

“…Although such unstable weather systems can increase the potential risk of CI over a vast area, they actually trigger CI, occupying much smaller areas and making it difficult to predict the exact location. CI is characterized by the rapid variation of temperature and the increase of cloud tops, which can be effectively mea-sured by brightness temperature (T b ) changes at multispectral channels including visible and infrared (IR; Mecikalski and Bedka, 2006;Mecikalski et al, 2009Mecikalski et al, , 2010. Geostationary satellites carry optical sensors that scan over a few thousand square kilometres with high temporal resolution (∼ min) in the multispectral channels.…”

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

“…These satellites are equipped with optical sensors that provide visible and infrared imagery at several spectral wavelengths with a spatial resolution of a few kilometers. Many studies have been performed to detect CI using GOES [3,4,[19][20][21][22][23][24] and SEVIRI [25][26][27][28][29] data. Based on the fact that TB at spectral channels should vary in time during the course of atmospheric convection [3,24,30], most of the previous studies focused on developing nowcasting algorithms of CI using interest fields of specific spectral channels such as TB, difference of TB at different channels, and the patterns of their temporal variations.…”

“…SATellite Convection Analysis and Tracking (SATCAST) developed by University of Alabama [3,24] and Rapid Development Thunderstorms (RDT) developed by the Meteo-France and EUMETSAT nowcasting Satellite Application Facility (SAF) [31] are used to detect CI from GOES and SEVIRI data, respectively. The previously developed algorithms were mostly validated for a few regions in North America [3,24] and Europe [26,29], and showed good performances with a probability of detection up to 80% and a false alarm rate of ~60%. However, the performances of such algorithms have not been reported so far over Northeast Asia where convective clouds are prevalent in the East Asian summer monsoon period (Changma in Korea, Mei-Yu in China and Baiu in Japan) and typhoons [32,33].…”

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

“…IF (7) investigates the time trend of the channel difference in IF (5). IF (6) and IF (8) are used to highlight cloud pixels that are likely to develop into a precipitating cloud (see Table 1, Bedka and Mecikalski, 2005;Mecikalski et al, 2008Mecikalski et al, , 2010Roberts and Rutledge, 2003;Mueller et al, 2003). As in Mecikalski and Bedka (2006), in order to have confidence that CI will occur, 7 out of 8 criteria per pixel have to be met.…”

“…Although additional methods exist for the detection of earlier development using radar, like the detection of convergence lines using Bragg scattering effects due to thermodynamical gradients or Rayleigh scattering due to small insects (Weckwerth and Parsons, 2006;Wilson and Mueller, 1993), satellite data are better suited for this task. Mecikalski et al (2010) found that lead times of up to 75 min for thunderstorms are possible when a set of different channel criteria for geostationary satellite data is applied. An advantage of the geostationary perspective is the continuous spatial and temporal coverage of wide regions.…”