Digital information on sea ice extent,thickness,volume,and distribution is crucial for understanding Earth's climate system.The Snow and Ice Mass Balance Apparatus(SIMBA)is used to determine snow and ice temperatu...Digital information on sea ice extent,thickness,volume,and distribution is crucial for understanding Earth's climate system.The Snow and Ice Mass Balance Apparatus(SIMBA)is used to determine snow and ice temperatures in Arctic,Antarctic,ice-covered seas,and boreal lakes.Snow depth and ice thickness are derived from SIMBA temperature regimes(SIMBA_ET and SIMBA_HT).In warm conditions,SiMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile.SIMBA_HT provides a visible ice-bottom interface for manual quantification.We propose an unmanned approach,combining neural networks,wavelet analysis,and Kalman filtering(NWK),to mathematically establish NwK and retrieve ice bottoms from various SIMBA_HT datasets.In the Arctic,NWK-derived total thickness showed a bias range of-5.64 cm to 4.01 cm and a correlation coefficient of 95%-99%.For Baltic Sea ice,values ranged from 1.31 cm to 2.41 cm(88%-98%correlation),and for boreal lake ice,-0.7 cm to 2.6 cm(75%-83%correlation).During ice growth,thermal equilibrium,and melting,the bias varied from-3.93 cm to 2.37 cm,-1.92 cm to 0.04 cm,and-4.90 cm to 3.96 cm,with correlation coefficients of 76%-99%.These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments.展开更多
基金supported by the Key-Area Research and Development Program of Guangdong Province,China(No.2021B0101190003)the Natural Science Foundation of Guangdong Province,China(No.2022A1515010831)BC was partly supported by the European Union’s Horizon 2020 research and innovation program(727890-INTAROS)in the early phase of SIMBA data analyzes and partly by Polar Regions in the Earth System project(PolarRES,grant 101003590)during the finalization stage of this work.
文摘Digital information on sea ice extent,thickness,volume,and distribution is crucial for understanding Earth's climate system.The Snow and Ice Mass Balance Apparatus(SIMBA)is used to determine snow and ice temperatures in Arctic,Antarctic,ice-covered seas,and boreal lakes.Snow depth and ice thickness are derived from SIMBA temperature regimes(SIMBA_ET and SIMBA_HT).In warm conditions,SiMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile.SIMBA_HT provides a visible ice-bottom interface for manual quantification.We propose an unmanned approach,combining neural networks,wavelet analysis,and Kalman filtering(NWK),to mathematically establish NwK and retrieve ice bottoms from various SIMBA_HT datasets.In the Arctic,NWK-derived total thickness showed a bias range of-5.64 cm to 4.01 cm and a correlation coefficient of 95%-99%.For Baltic Sea ice,values ranged from 1.31 cm to 2.41 cm(88%-98%correlation),and for boreal lake ice,-0.7 cm to 2.6 cm(75%-83%correlation).During ice growth,thermal equilibrium,and melting,the bias varied from-3.93 cm to 2.37 cm,-1.92 cm to 0.04 cm,and-4.90 cm to 3.96 cm,with correlation coefficients of 76%-99%.These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments.
