2026-10-14T00:32:34Z https://www.scidb.cn/oai

10.57760/sciencedb.07670 2024-04-25T10:29:15Z

2024-04-25 DORS0.25°: A High-Resolution Global Ocean Subsurface Temperature Dataset Using Remote Sensing Data and In Situ Observations doi:10.57760/sciencedb.07670 en This product utilized Deep Forest algorithm to reconstruct a high spatial resolution (0.25° × 0.25°) global ocean subsurface (30–2000 m) temperature dataset for the period 1993–2023 (named DORS0.25°). It combined multisource surface remote sensing data (sea surface temperature [SST], absolute dynamic altitude [ADT], and the sea surface wind field, which can be decomposed into northward and eastward components [USSW and VSSW]) with georeferenced information and in situ temperature profile observations. The DORS0.25º dataset shows high correlation with EN4-profile and ARMOR3D datasets, achieving an average R2 of 0.980 and an RMSE of 0.567°C relative to the EN4-profile. In addition, the geographical pattern of DORS0.25º is consistent with Argo gridded dataset, but captures more detailed signals. The results indicate that the reconstruction of DOR0.25º dataset in time-series and geographical pattern is reliable, and can provide data support for the study of mesoscale ocean interior dynamics and global climate change. Time Range: 1993.01-2023.12Region: GlobalLongitude: 180°W~180°ELatitude: 78.375°S~78.375°NParameters:TemperatureHorizontal Resolution: 0.25° × 0.25°Vertical Resolution: 23 levels (30-2000 m)Temporal Resolution: monthlyStorage Format: netcdf remote sensing; high resolution; ocean warming ; subsurface temperature; global climate change Hua Su Feiyan Zhang An Wang Jianchen Teng Jinwen Jiang PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank