Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Daily gridded lake surface temperature (LST) data (1979-2020) for each Great Lake - Superior (GLS), Michigan (GLM), Huron (GLH), Erie (GLE) and Ontario (GLO) - derived from LSTM detailed in Kayastha et al. (2023) paper: "Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach". Each matfile contains longitude (lon), latitude (lat), as well as the LST for each grid point. The files also contain the variable 'art1' (Area of Node-Base Control volume) required to calculate lake-wide average LST. The depth at each location (dep) is also provided.

本数据集涵盖苏必利尔湖（Superior, GLS）、密歇根湖（Michigan, GLM）、休伦湖（Huron, GLH）、伊利湖（Erie, GLE）与安大略湖（Ontario, GLO）这五大湖1979年至2020年的逐日网格化湖面温度（Lake Surface Temperature, LST）数据，其生成依据Kayastha等人2023年发表的论文"Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach"中详述的长短期记忆网络（Long Short-Term Memory, LSTM）方法。每个MATLAB格式数据文件均包含经度（lon）、纬度（lat）以及各网格点对应的湖面温度数据；文件中还包含用于计算全湖平均LST所需的变量'art1'（节点基控制体积面积），同时提供了各点位的水深（dep）数据。