A Semi-Lagrangian Method for Detecting and Tracking Deep Convective Clouds in Geostationary Satellite Observations

Abstract

Abstract. Automated methods for the detection and tracking of deep convective storms in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds, however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows for severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting deep convective clouds (including detection of growing cores through cloud top cooling and detection of anvil using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire lifecycle of deep convective clouds and their associated anvils. Furthermore, we expect this framework to applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts

“…Geostationary observations are also used to track mesoscale convective systems (MCSs), organized storms whose outflow is the source of tropical anvil cirrus (e.g., Fiolleau and Roca, 2013;Roca et al, 2017). From visible and infrared channels, a transition can be inferred from isolated convective cores, to optically thick anvil outflow, to optically thin residual outflow in the growing, mature, and dissipating stages of an MCS life cycle (Jones et al, 2023). This inference is generally based upon brightness temperature thresholds or changes in cloud-top radiative cooling rates.…”

“…Storm tracking. Storm tracking is valuable for understanding the evolution of anvil cirrus because it helps track the development and movement of the parent storm that is the source of anvil cirrus (Menzel, 2001;Jones et al, 2023). This is a relatively simple approach, often relying on a single 2D variable such as brightness temperature or outgoing longwave radiation obtained from geostationary satellites or model output (Fiolleau and Roca, 2013).…”

Object Tracking Based on Satellite Videos: A Literature Review

“…Nevertheless, some other satellite applications are not discussed in the following sections but are only listed here because of little published data on the specific applications. These applications include but are not limited to wild animal tracking [39], cloud tracking, tree defoliation tracking [40], low-salinity pool tracking [41], deep convective cloud tracking [42], crop phenology tracking [43], etc. Furthermore, traffic object tracking is one of most interest within the field of satellite video-based visual tracking due to its promising application potential and performance.…”