ABSTRACT

Storm surges are coastal sea-level variations caused by meteorological conditions. It is vital that they are forecasted accurately to reduce the potential for financial damage and loss of life. In this study, we investigate how effectively the variational assimilation of sparse sea level observations from tide gauges can be used for operational forecasting in the North Sea. Novel data assimilation ideas are considered and evaluated: a new shortest-path method for generating improved distance-based correlations in the presence of coastal boundaries and an adaptive error covariance model. An assimilation setup is validated by removing selections of tide gauges from the assimilation procedure for a North Sea case study. These experiments show widespread improvements in RMSE and correlations, reaching up to 16 cm and 0.7 (respectively) at some locations. Simulated forecast experiments show RMSE improvements of up to 5 cm for the first 24 h of forecasting, which is useful operationally. Beyond 24 h, improvements quickly diminish however. Using the setup based on the shortest path algorithm shows little difference when compared to a simpler Euclidean method at most locations. Analysis of this event shows that improvements due to data assimilation are bounded and relatively short lived.

摘要

风暴潮是由气象条件引起的沿海海平面变化。准确预测它们以减少经济损失和生命损失的可能性至关重要。在这项研究中，我们研究了潮汐测量仪对稀疏海平面观测值的变分同化如何有效地用于北海的业务预报。考虑并评估了新颖的数据同化想法：一种新的最短路径方法，用于在存在沿海边界和自适应误差协方差模型的情况下生成改进的基于距离的相关性。通过从北海案例研究的同化过程中删除测潮仪的选择来验证同化设置。这些实验显示 RMSE 和相关性的广泛改进，达到 16 厘米和 0。7（分别）在某些位置。模拟预报实验表明，预报的前 24 小时 RMSE 提高了 5 厘米，这在操作上很有用。然而，超过 24 小时后，改善会迅速减弱。在大多数位置，与更简单的欧几里德方法相比，使用基于最短路径算法的设置几乎没有区别。对该事件的分析表明，由于数据同化而带来的改进是有限的，而且持续时间相对较短。