A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019
The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this pr...
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Wiley
2021-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2021/8278579 |
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| author | Miao Fang |
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| description | The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p=0.04) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p=0.05). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p=0.01∼0.27) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p=0.04∼0.42) with a peak over the higher latitudes. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2021-01-01 |
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| series | Advances in Meteorology |
| spelling | doaj-art-7098d693e2e54900bf11323772df703c2025-02-03T01:25:15ZengWileyAdvances in Meteorology1687-93091687-93172021-01-01202110.1155/2021/82785798278579A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019Miao Fang0Northwest Institute of Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou 730000, ChinaThe spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p=0.04) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p=0.05). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p=0.01∼0.27) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p=0.04∼0.42) with a peak over the higher latitudes.http://dx.doi.org/10.1155/2021/8278579 |
| spellingShingle | Miao Fang A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 Advances in Meteorology |
| title | A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 |
| title_full | A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 |
| title_fullStr | A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 |
| title_full_unstemmed | A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 |
| title_short | A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 |
| title_sort | ga based bp artificial neural network for estimating monthly surface air temperature of the antarctic during 1960 2019 |
| url | http://dx.doi.org/10.1155/2021/8278579 |
| work_keys_str_mv | AT miaofang agabasedbpartificialneuralnetworkforestimatingmonthlysurfaceairtemperatureoftheantarcticduring19602019 AT miaofang gabasedbpartificialneuralnetworkforestimatingmonthlysurfaceairtemperatureoftheantarcticduring19602019 |