25 Jul 2023
 | 25 Jul 2023
Status: this preprint is open for discussion.

Applying pySTEPS optical flow algorithms to improve convection nowcasting over the Maritime Continent

Joseph Albert Smith, Cathryn Birch, John Marsham, Simon Peatman, Massimo Bollasina, and George Pankiewicz

Abstract. The Maritime Continent (MC) regularly experiences powerful convective storms that produce intense rainfall, flooding and landslides, which numerical weather prediction models struggle to forecast. Nowcasting uses observations to make more accurate predictions of convective activity over short timescales (~0–6 hours). Optical flow algorithms are effective nowcasting methods as they are able to accurately track clouds across observed image series and predict forward trajectories. Optical flow is generally applied to weather radar observations, however, the radar coverage network over the MC is not complete and the signal cannot penetrate the high mountainous regions. In this research, we apply optical flow algorithms from the pySTEPS nowcasting library to satellite imagery to generate both deterministic and probabilistic nowcasts over the MC. The deterministic algorithm shows skill up to 4 hours on spatial scales of 10 km and coarser, and outperforms a persistence forecast for all lead times. Lowest skill is observed over the mountainous regions during the early afternoon and highest skill is seen during the night over the sea. A key feature of the probabilistic algorithm is its attempt to reduce uncertainty in the lifetime of small scale convection. Composite analysis of 3-hour lead time nowcasts, initialised in the morning and afternoon, show it produces reliable ensembles but with an under-dispersive distribution, and produced area under the curve scores (i.e. ratio of hit rate to false alarm rate across all probability thresholds) of 0.80 and 0.71 over the sea and land, respectively. When directly comparing the two approaches, the probabilistic nowcast shows greater skill at ≤ 60 km spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales ~200 km. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.

Joseph Albert Smith et al.

Status: open (until 30 Oct 2023)

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  • RC1: 'Comment on egusphere-2023-1404', Anonymous Referee #1, 02 Aug 2023 reply

Joseph Albert Smith et al.

Joseph Albert Smith et al.


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Short summary
Nowcasting uses observations to make predictions of the atmosphere on short time scales and is particularly applicable to the Maritime Continent where storms rapidly develop and cause natural disasters. This paper evaluates probabilistic and deterministic satellite nowcasting algorithms over the Maritime Continent. We show that the probabilistic approach is most skilful at small scales (~60 km) whereas the deterministic approach is most skilful at larger scales (~200 km).