A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products
Solar radiation drives many geophysical and biological processes in Antarctica, such as sea ice melting, ice sheet mass balance, and photosynthetic processes of phytoplankton in the polar marine environment. Although reanalysis and satellite products can provide important insight into the global sca...
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crfrontiers:10.3389/feart.2022.961799 2024-02-11T09:58:55+01:00 A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products Zeng, Zhaoliang Wang, Xin Wang, Zemin Zhang, Wenqian Zhang, Dongqi Zhu, Kongju Mai, Xiaoping Cheng, Wei Ding, Minghu National Natural Science Foundation of China 2022 http://dx.doi.org/10.3389/feart.2022.961799 https://www.frontiersin.org/articles/10.3389/feart.2022.961799/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Earth Science volume 10 ISSN 2296-6463 General Earth and Planetary Sciences journal-article 2022 crfrontiers https://doi.org/10.3389/feart.2022.961799 2024-01-26T10:02:09Z Solar radiation drives many geophysical and biological processes in Antarctica, such as sea ice melting, ice sheet mass balance, and photosynthetic processes of phytoplankton in the polar marine environment. Although reanalysis and satellite products can provide important insight into the global scale of solar radiation in a seamless way, the ground-based radiation in the polar region remains poorly understood due to the harsh Antarctic environment. The present study attempted to evaluate the estimation performance of empirical models and machine learning models, and use the optimal model to establish a 35-year daily global solar radiation (DGSR) dataset at the Great Wall Station, Antarctica using meteorological observation data during 1986–2020. In addition, it then compared against the DGSR derived from ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products. For the DGSR historical estimation performance, the machine learning method outperforms the empirical formula method overall. Among them, the Mutli2 model (hindcast test R 2 , RMSE , and MAE are 0.911, 1.917 MJ/m 2 , and 1.237 MJ/m 2 , respectively) for the empirical formula model and XGBoost model (hindcast test R 2 , RMSE , and MAE are 0.938, 1.617 MJ/m 2 , and 1.030 MJ/m 2 , respectively) for the machine learning model were found with the highest accuracy. For the austral summer half-year, the estimated DGSR agrees very well with the observed DGSR, with a mean bias of only −0.47 MJ/m 2 . However, other monthly DGSR products differ significantly from observations, with mean bias of 1.05 MJ/m 2 , 3.27 MJ/m 2 , and 6.90 MJ/m 2 for ICDR (AVHRR) satellite, ERA5, and CRA40 reanalysis products, respectively. In addition, the DGSR of the Great Wall Station, Antarctica followed a statistically significant increasing trend at a rate of 0.14 MJ/m 2 /decade over the past 35 years. To our best knowledge, this study presents the first reconstruction of the Antarctica Great Wall Station DGSR spanning 1986–2020, which will contribute to the research of surface ... Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Sheet Sea ice Frontiers (Publisher) Antarctic Austral Great Wall Station ENVELOPE(-58.970,-58.970,-62.217,-62.217) Frontiers in Earth Science 10 |
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crfrontiers |
language |
unknown |
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General Earth and Planetary Sciences |
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General Earth and Planetary Sciences Zeng, Zhaoliang Wang, Xin Wang, Zemin Zhang, Wenqian Zhang, Dongqi Zhu, Kongju Mai, Xiaoping Cheng, Wei Ding, Minghu A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products |
topic_facet |
General Earth and Planetary Sciences |
description |
Solar radiation drives many geophysical and biological processes in Antarctica, such as sea ice melting, ice sheet mass balance, and photosynthetic processes of phytoplankton in the polar marine environment. Although reanalysis and satellite products can provide important insight into the global scale of solar radiation in a seamless way, the ground-based radiation in the polar region remains poorly understood due to the harsh Antarctic environment. The present study attempted to evaluate the estimation performance of empirical models and machine learning models, and use the optimal model to establish a 35-year daily global solar radiation (DGSR) dataset at the Great Wall Station, Antarctica using meteorological observation data during 1986–2020. In addition, it then compared against the DGSR derived from ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products. For the DGSR historical estimation performance, the machine learning method outperforms the empirical formula method overall. Among them, the Mutli2 model (hindcast test R 2 , RMSE , and MAE are 0.911, 1.917 MJ/m 2 , and 1.237 MJ/m 2 , respectively) for the empirical formula model and XGBoost model (hindcast test R 2 , RMSE , and MAE are 0.938, 1.617 MJ/m 2 , and 1.030 MJ/m 2 , respectively) for the machine learning model were found with the highest accuracy. For the austral summer half-year, the estimated DGSR agrees very well with the observed DGSR, with a mean bias of only −0.47 MJ/m 2 . However, other monthly DGSR products differ significantly from observations, with mean bias of 1.05 MJ/m 2 , 3.27 MJ/m 2 , and 6.90 MJ/m 2 for ICDR (AVHRR) satellite, ERA5, and CRA40 reanalysis products, respectively. In addition, the DGSR of the Great Wall Station, Antarctica followed a statistically significant increasing trend at a rate of 0.14 MJ/m 2 /decade over the past 35 years. To our best knowledge, this study presents the first reconstruction of the Antarctica Great Wall Station DGSR spanning 1986–2020, which will contribute to the research of surface ... |
author2 |
National Natural Science Foundation of China |
format |
Article in Journal/Newspaper |
author |
Zeng, Zhaoliang Wang, Xin Wang, Zemin Zhang, Wenqian Zhang, Dongqi Zhu, Kongju Mai, Xiaoping Cheng, Wei Ding, Minghu |
author_facet |
Zeng, Zhaoliang Wang, Xin Wang, Zemin Zhang, Wenqian Zhang, Dongqi Zhu, Kongju Mai, Xiaoping Cheng, Wei Ding, Minghu |
author_sort |
Zeng, Zhaoliang |
title |
A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products |
title_short |
A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products |
title_full |
A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products |
title_fullStr |
A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products |
title_full_unstemmed |
A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products |
title_sort |
35-year daily global solar radiation dataset reconstruction at the great wall station, antarctica: first results and comparison with era5, cra40 reanalysis, and icdr (avhrr) satellite products |
publisher |
Frontiers Media SA |
publishDate |
2022 |
url |
http://dx.doi.org/10.3389/feart.2022.961799 https://www.frontiersin.org/articles/10.3389/feart.2022.961799/full |
long_lat |
ENVELOPE(-58.970,-58.970,-62.217,-62.217) |
geographic |
Antarctic Austral Great Wall Station |
geographic_facet |
Antarctic Austral Great Wall Station |
genre |
Antarc* Antarctic Antarctica Ice Sheet Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet Sea ice |
op_source |
Frontiers in Earth Science volume 10 ISSN 2296-6463 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/feart.2022.961799 |
container_title |
Frontiers in Earth Science |
container_volume |
10 |
_version_ |
1790594754368503808 |