Developing a Stabilized Ensemble Kalman Filter for integrating daily GRACE/GRACE-FO data into process models (S-ENKF)

最近的研究建议，使用Enkleman卡尔曼滤波方法，将重力恢复和气候实验（GRACE）使命任务获得的全球质量再分布数据与水文模型相结合。这将为洪水（即排放）和干旱预报应用提供巨大的潜力，特别是当应用于GRACE解决方案时，在次月分辨率。然而，结果到目前为止还没有达到理论预期，其原因很可能是发现在固有的病态的GRACE横向和垂直解聚问题，结合数值特性的有限大小的合奏方法进行最佳估计。在这里，我们建议开发一个稳定的EnKF;通过实施/定制正则化和最佳加权技术，从气象学和大地测量学合成方法。稳定的方法将被应用于整合每日GRACE数据与水文模型在0.5度的空间分辨率。该方法将应用于恒河-雅鲁藏布江-梅克纳地区的真实的数据，并通过模拟以及使用独立的数据进行评估。

Recent studies have suggested integrating global mass redistribution data obtained from the Gravity Recovery and Climate Experiment (GRACE) mission with hydrological models, using the Ensemble Kalman Filter (EnKF) approach. This would offer great potential for flood (i.e. discharge) and drought forecasting applications, in particular when applied to GRACE solutions at sub-monthly resolution. However, results so far have not met theoretical expectations and the reason for this is likely to be found in the inherent ill-conditioning of the GRACE lateral and vertical disaggregation problem, combined with the numerical peculiarities of limited-size ensemble approaches for optimal estimation. Here, we suggest developing a stabilized EnKF; through implementing/tailoring regularization and optimal weighting techniques, synthesizing methods from meteorology and from geodesy. The stabilized method will be applied to integrate daily GRACE data with a hydrological model at 0.5-degree spatial resolution. The approach will be applied to real data over the Ganges-Brahmaputra-Meghna region, and evaluated though simulations as well as using independent data.