Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting edge image registration techniques. This work can also have application to other physical models which produce coherent structures.
翻译:受物理方程式制约的海冰模型已被用于模拟冰的状态,包括冰厚度、浓度和运动等特征。最近的一些模型还试图捕捉断裂或冰中铅等特征。这些模拟特征与观测数据相比可以部分错配或误差,无论是由于数字近似还是由于物理不全。为了作出现实的预测和增进对基本过程的了解,有必要将数字模型与实地数据加以校准。基于一般的最小度测量标准的传统校准方法对海洋冰裂等线性特征有缺陷。我们开发了一个统计模拟和校准框架,其中考虑到特征的不匹配和误差,这涉及将模型输出与使用切边缘图像登记技术观察到的特征进行最佳协调。这项工作还可以适用于产生一致结构的其他物理模型。