An Enhanced Geostationary Satellite–Based Convective Initiation Algorithm for 0–2-h Nowcasting with Object Tracking

Abstract

This paper describes an enhanced 0–2-h convective initiation (CI) nowcasting algorithm known as Satellite Convection Analysis and Tracking, version 2 (SATCASTv2). Tracking of developing cumulus cloud “objects” in advance of CI was developed as a means of reducing errors caused by tracking single satellite pixels of cumulus clouds, as identified in Geostationary Operational Environmental Satellite (GOES) output. The method rests on the idea that cloud objects at one time, when extrapolated forward in space and time using mesoscale atmospheric motion vectors, will overlap with the same actual cloud objects at a later time. Significant overlapping confirms that a coherent cumulus cloud is present and trackable in GOES data and that it is persistent enough that various infrared threshold–based tests may be performed to assess cloud growth. Validation of the new object-tracking approach to nowcasting CI was performed over four regions in the United States: 1) Melbourne, Florida; 2) Memphis, Tennessee; 3) the central United States/Great Plains; and 4) the northeastern United States as a means of evaluating algorithm performance in various convective environments. In this study, 9943 CI nowcasts and 804 CI events were analyzed. Optimal results occurred in the central U.S./Great Plains domain, where the probability of detection (POD) and false-alarm ratio (FAR) reached 85% and 55%, respectively, for tracked cloud objects. The FARs were partially attributed to difficulties inherent to the CI nowcasting problem. PODs were seen to decrease for CI events in Florida. Discussion is provided on how SATCASTv2 performed, as well as on how certain problems may be mitigated, especially in light of enhanced geostationary-satellite systems.

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

“…Mecikalski et al (2015) developed CI nowcasting algorithms by combining the interest fields derived from GOES and numerical weather prediction (NWP) model data based on probabilistic approaches. They validated the performance of the probabilistic algorithms for CI cases in the United States, resulting in a FAR of 10-18 %, which is much lower than the existing deterministic CI detection algorithms for GOES (FAR ∼ 48-60 %;Walker et al, 2012). However, Mecikalski et al (2015) used fewer satellite-based interest fields than the GOES-R CI algorithm due to the limited number of spectral channels of GOES, which implies that it is unknown whether such probabilistic approaches would work for Himawari-8 AHI as well.…”

“…Therefore, the threshold of ≥35 dBZ precipitation intensity has been widely used as the definition of convective events (Mecikalski and Bedka, 2006;Mecikalski et al, 2008Mecikalski et al, , 2010Roberts and Rutledge, 2003;Walker et al, 2012). In this study, the first occurrence of rainfall with ≥ 35 dBZ precipitation intensity was defined as CI.…”

“…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.…”

“…These two satellites have forecasted CI using their operational algorithms, i.e. SATellite Convection AnalySis and Tracking (SATCAST) and Rapidly Developing Thunderstorms (RDT) which are basically based on the empirical determination of the thresholds of interest fields in terms of CI development (Mecikalski and Bedka, 2006;Walker et al, 2012;Morel and Senesi, 2002). These algorithms have been assessed for CI cases in North America and showed a probability of detection (POD) over 0.8 (80 %) and a false alarm rate (FAR) around 0.6 (60 %).…”

“…The spectral characteristics of Himawari-8 AHI are comparable to the Advanced Baseline Imager (ABI) integrated into the Geostationary Operational Environmental Satellite-R series (GOES-R) satellites (Schmit et al, 2005), which are a series of geostationary satellites, the first of which will be launched in November 2016 and operated by the National Oceanic and Atmospheric Administration (NOAA). The University of Alabama in Huntsville with NOAA has developed a CI detection algorithm for GOES-R using 12 interest fields designed for the spectral bands of ABI (Walker and Mecikalski, 2011;Walker et al, 2012;Mecikalski et al, 2015). The interest fields of the GOES-R CI algorithm can be directly adopted for Himawari-8 AHI.…”