Adjusting thresholds of satellite‐based convective initiation interest fields based on the cloud environment

Abstract

[1] The Time-Space Exchangeability (TSE) concept states that similar characteristics of a given property are closely related statistically for objects or features within close proximity. In this exercise, the objects considered are growing cumulus clouds, and the data sets to be considered in a statistical sense are geostationary satellite infrared (IR) fields that help describe cloud growth rates, cloud top heights, and whether cloud tops contain significant amounts of frozen hydrometeors. In this exercise, the TSE concept is applied to alter otherwise static thresholds of IR fields of interest used within a satellite-based convective initiation (CI) nowcasting algorithm. The convective environment in which the clouds develop dictate growth rate and precipitation processes, and cumuli growing within similar mesoscale environments should have similar growth characteristics. Using environmental information provided by regional statistics of the interest fields, the thresholds are examined for adjustment toward improving the accuracy of 0-1 h CI nowcasts. Growing cumulus clouds are observed within a CI algorithm through IR fields for many 1000 s of cumulus cloud objects, from which statistics are generated on mesoscales. Initial results show a reduction in the number of false alarms of~50%, yet at the cost of eliminating approximatelỹ 20% of the correct CI forecasts. For comparison, static thresholds (i.e., with the same threshold values applied across the entire satellite domain) within the CI algorithm often produce a relatively high probability of detection, with false alarms being a significant problem. In addition to increased algorithm performance, a benefit of using a method like TSE is that a variety of unknown variables that influence cumulus cloud growth can be accounted for without need for explicit near-cloud observations that can be difficult to obtain.

Distribution and Variability of Satellite‐Derived Signals of Isolated Convection Initiation Events Over Central Eastern China

“…However, more negative LTs and smaller positive LTs are present in CEC in midsummer (Figures c and d). During the EASM period, more warm and moist air from the tropical and subtropical oceans prevails over CEC from May to August, providing more precipitable water (Figure ), especially more liquid water content, which is more favorable for convective storms to begin with a warm‐rain process (Jewett & Mecikalski, ). This partly accounts for the monthly increase in the percentage of −30 min and zero LTs and thus the monthly decrease in mean LT (Figure b).…”

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

“…Therefore, these geostationary satellites can be extremely useful in CI nowcasting. Previous studies developed CI nowcasting algorithms for geostationary satellites by determining a threshold or a range of values of T b at specific channels, and their spectral and/or temporal differences (Mecikalski and Bedka, 2006;Mecikalski et al, 2008;Walker et al, 2012;Morel and Senesi, 2002;Jewett and Mecikalski, 2013;Merk and Zinner, 2013;Siewert et al, 2010;Sobajima, 2012;Han et al, 2015). Geostationary Operational Environmental Satellite (GOES) systems and Meteorological Second Generation (MSG) are the representative geostationary satellites operated at the National Oceanic and Atmospheric Administration (NOAA) and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), respectively.…”

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.…”