ABSTRACT

Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology.

摘要

数据同化 (DA) 为不确定性识别提供了巨​​大的潜力，改进了水文和大气建模的初始估计。本文回顾了使用卡尔曼滤波器在水文 DA 中的研究。总结了卡尔曼滤波器在水文和大气 DA 中的最新应用。简要描述了 DA 研究的现有挑战。此外，还介绍了三个案例研究示例，强调了以下因素的影响：(a) 诺亚陆地表面模型中的土壤水分 DA；(b) 改进 WRF（天气研究预报）模型降水预报的变分同化；(c) 模拟 AMSR-2（高级微波扫描辐射计-2）对 DA 的辐射。尽管 DA 中有许多未解决的问题值得进一步研究，