Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height

Two groups of retrieval algorithms, physics based and machine learning (ML) based, each consisting of two independent approaches, have been developed to retrieve cloud base height (CBH) and its diurnal cycle from Himawari-8 geostationary satellite observations. Validations have been conducted using...

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Vydané v:	Atmospheric chemistry and physics Ročník 24; číslo 24; s. 14239 - 14256
Hlavní autori:	Wang, Mengyuan, Min, Min, Li, Jun, Lin, Han, Liang, Yongen, Chen, Binlong, Yao, Zhigang, Xu, Na, Zhang, Miao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Katlenburg-Lindau Copernicus GmbH 20.12.2024 Copernicus Publications
Predmet:	Algorithms Artificial satellites Aviation CALIPSO (Pathfinder satellite) Climate change Clouds Comparative analysis Correlation coefficient Correlation coefficients Datasets Diurnal cycle Diurnal variations Geostationary satellites Ground-based observation Height Learning algorithms Lidar Machine learning Meteorological satellites Physics Radar Remote sensing Satellite observation Satellites Synchronous satellites Training Weather forecasting
ISSN:	1680-7324, 1680-7316, 1680-7324
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Abstract	Two groups of retrieval algorithms, physics based and machine learning (ML) based, each consisting of two independent approaches, have been developed to retrieve cloud base height (CBH) and its diurnal cycle from Himawari-8 geostationary satellite observations. Validations have been conducted using the joint CloudSat/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CBH products in 2017, ensuring independent assessments. Results show that the two ML-based algorithms exhibit markedly superior performance (the optimal method is with a correlation coefficient of R > 0.91 and an absolute bias of approximately 0.8 km) compared to the two physics-based algorithms. However, validations based on CBH data from the ground-based lidar at the Lijiang station in Yunnan Province and the cloud radar at the Nanjiao station in Beijing, China, explicitly present contradictory outcomes (R < 0.60). An identifiable issue arises with significant underestimations in the retrieved CBH by both ML-based algorithms, leading to an inability to capture the diurnal cycle characteristics of CBH. The strong consistence observed between CBH derived from ML-based algorithms and the spaceborne active sensors of CloudSat/CALIOP may be attributed to utilizing the same dataset for training and validation, sourced from the CloudSat/CALIOP products. In contrast, the CBH derived from the optimal physics-based algorithm demonstrates good agreement in diurnal variations in CBH with ground-based lidar/cloud radar observations during the daytime (with an R value of approximately 0.7). Therefore, the findings in this investigation from ground-based observations advocate for the more reliable and adaptable nature of physics-based algorithms in retrieving CBH from geostationary satellite measurements. Nevertheless, under ideal conditions, with an ample dataset of spaceborne cloud profiling radar observations encompassing the entire day for training purposes, the ML-based algorithms may hold promise for still delivering accurate CBH outputs.
AbstractList	Two groups of retrieval algorithms, physics based and machine learning (ML) based, each consisting of two independent approaches, have been developed to retrieve cloud base height (CBH) and its diurnal cycle from Himawari-8 geostationary satellite observations. Validations have been conducted using the joint CloudSat/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CBH products in 2017, ensuring independent assessments. Results show that the two ML-based algorithms exhibit markedly superior performance (the optimal method is with a correlation coefficient of R > 0.91 and an absolute bias of approximately 0.8 km) compared to the two physics-based algorithms. However, validations based on CBH data from the ground-based lidar at the Lijiang station in Yunnan Province and the cloud radar at the Nanjiao station in Beijing, China, explicitly present contradictory outcomes (R < 0.60). An identifiable issue arises with significant underestimations in the retrieved CBH by both ML-based algorithms, leading to an inability to capture the diurnal cycle characteristics of CBH. The strong consistence observed between CBH derived from ML-based algorithms and the spaceborne active sensors of CloudSat/CALIOP may be attributed to utilizing the same dataset for training and validation, sourced from the CloudSat/CALIOP products. In contrast, the CBH derived from the optimal physics-based algorithm demonstrates good agreement in diurnal variations in CBH with ground-based lidar/cloud radar observations during the daytime (with an R value of approximately 0.7). Therefore, the findings in this investigation from ground-based observations advocate for the more reliable and adaptable nature of physics-based algorithms in retrieving CBH from geostationary satellite measurements. Nevertheless, under ideal conditions, with an ample dataset of spaceborne cloud profiling radar observations encompassing the entire day for training purposes, the ML-based algorithms may hold promise for still delivering accurate CBH outputs.
Audience	Academic
Author	Li, Jun Zhang, Miao Chen, Binlong Liang, Yongen Wang, Mengyuan Min, Min Lin, Han Yao, Zhigang Xu, Na
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Cites_doi	10.1016/j.atmosenv.2022.119065 10.1109/TGRS.2023.3256365 10.1175/JTECH-D-16-0110.1 10.1016/j.atmosenv.2019.01.012 10.1016/j.solener.2017.11.049 10.1175/2009JTECHA1223.1 10.1175/JCLI-D-13-00352.1 10.1109/TGRS.2018.2882803 10.1109/TGRS.2019.2923247 10.1175/2007WAF2006017.1 10.5194/acp-23-8959-2023 10.5194/acp-22-1453-2022 10.1007/s11222-016-9646-1 10.1007/s00376-017-7096-0 10.1016/j.jqsrt.2014.03.014 10.5194/amt-11-3177-2018 10.1175/2008JTECHA1221.1 10.1175/JTECH-D-16-0109.1 10.1080/01431161.2020.1854891 10.1007/s13351-017-6161-z 10.5194/acp-9-1767-2009 10.1073/pnas.1514044113 10.1175/BAMS-D-12-00117.1 10.5194/amt-9-711-2016 10.1002/qj.3803 10.2151/jmsj.2016-009 10.1007/s00376-019-8272-1 10.1175/JTECH-D-18-0079.1 10.1175/BAMS-83-12-1771 10.1175/JAMC-D-11-0203.1 10.1175/JTECH1671.1 10.1175/JAMC-D-14-0082.1 10.1002/2015GL064809 10.1002/jgrd.50579 10.1109/TGRS.2022.3140348 10.1016/j.isprsjprs.2020.01.011 10.1080/01431160110117391 10.1016/j.rse.2021.112305 10.1029/2007GL031361 10.5194/amt-4-117-2011 10.1109/TGRS.2022.3160450 10.1029/2011JD015970 10.1175/2009JTECHA1281.1 10.1175/2009JTECHA1231.1 10.1175/2008JAMC1882.1 10.1080/01431160500296800 10.5194/acp-21-11979-2021 10.1175/1520-0450(2000)039<2336:CBHEUA>2.0.CO;2 10.2151/jmsj.2018-001 10.1002/2014JD022310 10.1029/2002GL015835 10.1002/2013GL058970 10.1023/A:1010933404324 10.1109/TGRS.2016.2610522 10.1016/j.rse.2019.111616 10.1175/JAS-D-19-0301.1 10.1029/2007GL032591 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 10.1016/j.rse.2022.112970
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Title	Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height
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