Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data
<p<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 sho...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
J. Smith [verfasserIn] C. Birch [verfasserIn] J. Marsham [verfasserIn] S. Peatman [verfasserIn] M. Bollasina [verfasserIn] G. Pankiewicz [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Übergeordnetes Werk: |
In: Natural Hazards and Earth System Sciences - Copernicus Publications, 2004, 24(2024), Seite 567-582 |
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| Übergeordnetes Werk: |
volume:24 ; year:2024 ; pages:567-582 |
| Links: |
Link aufrufen |
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| DOI / URN: |
10.5194/nhess-24-567-2024 |
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DOAJ10161571X |
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| 520 | |a <p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< | ||
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10.5194/nhess-24-567-2024 doi (DE-627)DOAJ10161571X (DE-599)DOAJbf7f4b15cfe8462bb796f980e01384ce DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 QE1-996.5 J. Smith verfasserin aut Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< Environmental technology. Sanitary engineering Geography. Anthropology. Recreation G Environmental sciences Geology C. Birch verfasserin aut J. Marsham verfasserin aut S. Peatman verfasserin aut M. Bollasina verfasserin aut G. Pankiewicz verfasserin aut In Natural Hazards and Earth System Sciences Copernicus Publications, 2004 24(2024), Seite 567-582 (DE-627)338766928 (DE-600)2064587-9 16849981 nnns volume:24 year:2024 pages:567-582 https://doi.org/10.5194/nhess-24-567-2024 kostenfrei https://doaj.org/article/bf7f4b15cfe8462bb796f980e01384ce kostenfrei https://nhess.copernicus.org/articles/24/567/2024/nhess-24-567-2024.pdf kostenfrei https://doaj.org/toc/1561-8633 Journal toc kostenfrei https://doaj.org/toc/1684-9981 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 567-582 |
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10.5194/nhess-24-567-2024 doi (DE-627)DOAJ10161571X (DE-599)DOAJbf7f4b15cfe8462bb796f980e01384ce DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 QE1-996.5 J. Smith verfasserin aut Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< Environmental technology. Sanitary engineering Geography. Anthropology. Recreation G Environmental sciences Geology C. Birch verfasserin aut J. Marsham verfasserin aut S. Peatman verfasserin aut M. Bollasina verfasserin aut G. Pankiewicz verfasserin aut In Natural Hazards and Earth System Sciences Copernicus Publications, 2004 24(2024), Seite 567-582 (DE-627)338766928 (DE-600)2064587-9 16849981 nnns volume:24 year:2024 pages:567-582 https://doi.org/10.5194/nhess-24-567-2024 kostenfrei https://doaj.org/article/bf7f4b15cfe8462bb796f980e01384ce kostenfrei https://nhess.copernicus.org/articles/24/567/2024/nhess-24-567-2024.pdf kostenfrei https://doaj.org/toc/1561-8633 Journal toc kostenfrei https://doaj.org/toc/1684-9981 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 567-582 |
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10.5194/nhess-24-567-2024 doi (DE-627)DOAJ10161571X (DE-599)DOAJbf7f4b15cfe8462bb796f980e01384ce DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 QE1-996.5 J. Smith verfasserin aut Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< Environmental technology. Sanitary engineering Geography. Anthropology. Recreation G Environmental sciences Geology C. Birch verfasserin aut J. Marsham verfasserin aut S. Peatman verfasserin aut M. Bollasina verfasserin aut G. Pankiewicz verfasserin aut In Natural Hazards and Earth System Sciences Copernicus Publications, 2004 24(2024), Seite 567-582 (DE-627)338766928 (DE-600)2064587-9 16849981 nnns volume:24 year:2024 pages:567-582 https://doi.org/10.5194/nhess-24-567-2024 kostenfrei https://doaj.org/article/bf7f4b15cfe8462bb796f980e01384ce kostenfrei https://nhess.copernicus.org/articles/24/567/2024/nhess-24-567-2024.pdf kostenfrei https://doaj.org/toc/1561-8633 Journal toc kostenfrei https://doaj.org/toc/1684-9981 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 567-582 |
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10.5194/nhess-24-567-2024 doi (DE-627)DOAJ10161571X (DE-599)DOAJbf7f4b15cfe8462bb796f980e01384ce DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 QE1-996.5 J. Smith verfasserin aut Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< Environmental technology. Sanitary engineering Geography. Anthropology. Recreation G Environmental sciences Geology C. Birch verfasserin aut J. Marsham verfasserin aut S. Peatman verfasserin aut M. Bollasina verfasserin aut G. Pankiewicz verfasserin aut In Natural Hazards and Earth System Sciences Copernicus Publications, 2004 24(2024), Seite 567-582 (DE-627)338766928 (DE-600)2064587-9 16849981 nnns volume:24 year:2024 pages:567-582 https://doi.org/10.5194/nhess-24-567-2024 kostenfrei https://doaj.org/article/bf7f4b15cfe8462bb796f980e01384ce kostenfrei https://nhess.copernicus.org/articles/24/567/2024/nhess-24-567-2024.pdf kostenfrei https://doaj.org/toc/1561-8633 Journal toc kostenfrei https://doaj.org/toc/1684-9981 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2024 567-582 |
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verfasserin |
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evaluating pysteps optical flow algorithms for convection nowcasting over the maritime continent using satellite data |
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TD1-1066 |
| title_auth |
Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data |
| abstract |
<p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< |
| abstractGer |
<p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< |
| abstract_unstemmed |
<p<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 (<span class="inline-formula"<∼</span< 0–6 <span class="inline-formula"<h</span<). 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 <span class="inline-formula"<h</span< on spatial scales of 10 <span class="inline-formula"<km</span< and coarser and outperforms a persistence nowcast 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 <span class="inline-formula"<h</span< lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces 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 <span class="inline-formula"<≤</span< 60 <span class="inline-formula"<km</span< spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales <span class="inline-formula"<∼</span< 200 <span class="inline-formula"<km</span<. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.</p< |
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| title_short |
Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data |
| url |
https://doi.org/10.5194/nhess-24-567-2024 https://doaj.org/article/bf7f4b15cfe8462bb796f980e01384ce https://nhess.copernicus.org/articles/24/567/2024/nhess-24-567-2024.pdf https://doaj.org/toc/1561-8633 https://doaj.org/toc/1684-9981 |
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C. Birch J. Marsham S. Peatman M. Bollasina G. Pankiewicz |
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