In large lakes, ice cover plays an important role in shipping and navigation, coastal erosion, regional weather and climate, and aquatic ecosystem function. In this study, a novel deep learning model for ice cover concentration prediction in Lake Michigan is introduced. The model uses hindcasted meteorological variables, water depth, and shoreline proximity as inputs, and NOAA ice charts for training, validation, and testing. The proposed framework leverages Convolution Long Short-Term Memory (ConvLSTM) and Convolution Neural Network (CNN) to capture both spatial and temporal dependencies between model input and output to simulate daily ice cover at 0.1° resolution. The model performance was assessed through lake-wide average metrics and local error metrics, with detailed evaluations conducted at six distinct locations in Lake Michigan. The results demonstrated a high degree of agreement between the model’s predictions and ice charts, with an average RMSE of 0.029 for the daily lake-wide average ice concentration. Local daily prediction errors were greater, with an average RMSE of 0.102. Lake-wide and local errors for weekly and monthly averaged ice concentrations were reduced by almost 50% from daily values. The accuracy of the proposed model surpasses currently available physics-based models in the lake-wide ice concentration prediction, offering a promising avenue for enhancing ice prediction and hindcasting in large lakes and coastal areas.

中文翻译：

---

一种用于对密歇根湖冰盖进行建模和后报的深度学习方法


在大型湖泊中，冰盖在航运和航行、海岸侵蚀、区域天气和气候以及水生生态系统功能方面发挥着重要作用。在本研究中，介绍了一种新的深度学习模型，用于密歇根湖冰盖浓度预测。该模型使用后报气象变量、水深和海岸线接近度作为输入，并使用 NOAA 冰图进行训练、验证和测试。所提出的框架利用卷积长短期记忆 （ConvLSTM） 和卷积神经网络 （CNN） 来捕获模型输入和输出之间的空间和时间依赖关系，以 0.1° 的分辨率模拟每日冰盖。模型性能通过全湖平均指标和局部误差指标进行评估，并在密歇根湖的六个不同位置进行了详细评估。结果表明，模型的预测与冰图高度一致，全湖每日平均冰浓度的平均 RMSE 为 0.029。局部每日预测误差更大，平均 RMSE 为 0.102。每周和每月平均冰浓度的全湖和局部误差比每日值减少了近 50%。所提出的模型的准确性超过了目前可用的基于物理的模型在全湖冰浓度预测方面，为增强大型湖泊和沿海地区的冰预测和后报提供了一条有前途的途径。