论文标题
用于冰川建模的集合卡尔曼过滤
Ensemble Kalman Filtering for Glacier Modeling
论文作者
论文摘要
我们使用两个阶段的冰盖模型，探索如何使用统计数据同化方法来改善冰川融化的预测以及相关的海平面上升。我们发现，ENKF改进了使用不正确的初始条件或参数初始运行的模型，从而为我们提供了更好的未来冰川融化模型。我们探索生成准确的模型运行所需的必要观测值。此外，我们确定可以通过现代观察数据来纠正及卫星前时代的数据点很少的数据点的偏差。最后，使用从改进的模型得出的数据，我们计算海平面上升和模型风暴潮，以了解海平面上升造成的影响。
Working with a two-stage ice sheet model, we explore how statistical data assimilation methods can be used to improve predictions of glacier melt and relatedly, sea level rise. We find that the EnKF improves model runs initialized using incorrect initial conditions or parameters, providing us with better models of future glacier melt. We explore the necessary number of observations needed to produce an accurate model run. Further, we determine that the deviations from the truth in output that stem from having few data points in the pre-satellite era can be corrected with modern observation data. Finally, using data derived from our improved model we calculate sea level rise and model storm surges to understand the affect caused by sea level rise.
