Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms
There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, exis...
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2023
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Online Access: | https://doi.org/10.3390/atmos14020228 https://doaj.org/article/2c4c77867fc54b15b1eb58f6f103f207 |
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ftdoajarticles:oai:doaj.org/article:2c4c77867fc54b15b1eb58f6f103f207 2023-05-15T16:09:41+02:00 Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms Fenghua Zhao Jiawei Xia Lin Zhu Hongfu Sun Dexin Zhao 2023-01-01T00:00:00Z https://doi.org/10.3390/atmos14020228 https://doaj.org/article/2c4c77867fc54b15b1eb58f6f103f207 EN eng MDPI AG https://www.mdpi.com/2073-4433/14/2/228 https://doaj.org/toc/2073-4433 doi:10.3390/atmos14020228 2073-4433 https://doaj.org/article/2c4c77867fc54b15b1eb58f6f103f207 Atmosphere, Vol 14, Iss 228, p 228 (2023) volcanic ash cloud base height machine learning CALIOP lidar data passive satellite measurement Meteorology. Climatology QC851-999 article 2023 ftdoajarticles https://doi.org/10.3390/atmos14020228 2023-02-26T01:31:23Z There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm ... Article in Journal/Newspaper Eyjafjallajökull Iceland Directory of Open Access Journals: DOAJ Articles Tonga ENVELOPE(7.990,7.990,63.065,63.065) Atmosphere 14 2 228 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
volcanic ash cloud base height machine learning CALIOP lidar data passive satellite measurement Meteorology. Climatology QC851-999 |
spellingShingle |
volcanic ash cloud base height machine learning CALIOP lidar data passive satellite measurement Meteorology. Climatology QC851-999 Fenghua Zhao Jiawei Xia Lin Zhu Hongfu Sun Dexin Zhao Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms |
topic_facet |
volcanic ash cloud base height machine learning CALIOP lidar data passive satellite measurement Meteorology. Climatology QC851-999 |
description |
There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm ... |
format |
Article in Journal/Newspaper |
author |
Fenghua Zhao Jiawei Xia Lin Zhu Hongfu Sun Dexin Zhao |
author_facet |
Fenghua Zhao Jiawei Xia Lin Zhu Hongfu Sun Dexin Zhao |
author_sort |
Fenghua Zhao |
title |
Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms |
title_short |
Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms |
title_full |
Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms |
title_fullStr |
Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms |
title_full_unstemmed |
Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms |
title_sort |
retrieval of volcanic ash cloud base height using machine learning algorithms |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/atmos14020228 https://doaj.org/article/2c4c77867fc54b15b1eb58f6f103f207 |
long_lat |
ENVELOPE(7.990,7.990,63.065,63.065) |
geographic |
Tonga |
geographic_facet |
Tonga |
genre |
Eyjafjallajökull Iceland |
genre_facet |
Eyjafjallajökull Iceland |
op_source |
Atmosphere, Vol 14, Iss 228, p 228 (2023) |
op_relation |
https://www.mdpi.com/2073-4433/14/2/228 https://doaj.org/toc/2073-4433 doi:10.3390/atmos14020228 2073-4433 https://doaj.org/article/2c4c77867fc54b15b1eb58f6f103f207 |
op_doi |
https://doi.org/10.3390/atmos14020228 |
container_title |
Atmosphere |
container_volume |
14 |
container_issue |
2 |
container_start_page |
228 |
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1766405526630957056 |