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.
The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0-60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.
Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.
A study was undertaken to examine growing cumulus clouds using 1-min time resolution Super Rapid Scan Operations for Geostationary Operational Environmental Satellite-R (GOES-R) (SRSOR) imagery to diagnose in-cloud processes from cloud-top information. SRSOR data were collected using GOES-14 for events in 2012–14. Use of 1-min resolution SRSOR observations of rapidly changing scenes provides far more insights into cloud processes as compared to when present-day 5–15-min time resolution GOES data are used. For midday times on five days, cloud-top temperatures were cataloged for 71 cumulus clouds as they grew to possess anvils and often overshooting cloud tops, which occurred over 33–152-min time periods. Characteristics of the SRSOR-observed updrafts were examined individually, on a per day basis, and collectively, to reveal unique aspects of updraft behavior, strength, and acceleration as related to the ambient stability profile and cloud-top glaciation. A conclusion is that the 1-min observations capture two specific cumulus cloud growth periods, less rapid cloud growth between the level of free convection and the 0°C isotherm level, followed by more rapid growth shortly after the time of cloud-top glaciation. High correlation is found between estimated vertical motion (w) and the amount of convective available potential energy (CAPE) realized to the cloud-top level as clouds grew, which suggests that updrafts were responding to the local buoyancy quite strongly. Influences of the environmental buoyancy profile shape and evidence of entrainment on cloud growth are also found through these SRSOR data analyses.
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