Wang Min, Wei Jing, Wang Xiaodong, Luan Qingzu, Xu Xinliang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2024 Oct 15;11(1):1133. doi: 10.1038/s41597-024-03980-z.
A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (T), minimum (T), and mean (T) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily T, T, and T. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations.
一个具有高时空分辨率的高精度连续气温（Ta）数据集对于人类健康、疾病预测和能源管理至关重要。现有的数据集考虑了海拔、纬度和地表温度等因素，但对气象和时空因素的处理不够充分，影响了准确性。此外，目前没有高分辨率数据集包含使用统一方法生成的日最高温度（T）、最低温度（T）和平均温度（T）。在此，我们引入了四维时空深度森林（4D-STDF）模型，该模型整合了12个多源因素，包括静态和动态参数，以及6个精细的时空因素来生成Ta数据集。这种方法生成了三个空间分辨率为1公里、覆盖2003年至2022年中国大陆的高精度Ta数据集。这些数据集采用WGS84投影的GeoTIFF格式，包含日最高温度、最低温度和平均温度。估计值的总体均方根误差分别为1.49°C、1.53°C和1.18°C。4D-STDF模型也可应用于气象站稀少的其他地区。
