Review of the Kalman-type hydrological data assimilation

ABSTRACT

There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalman-type DA but also provide an important reference to other hydrological DA types.

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

Sensitivity Analysis of the Super Heavy Rainfall Event in Henan on 20 July (2021) Using ECMWF Ensemble Forecasts

An unprecedented heavy rainfall event occurred in Henan Province, China, during the period of 1200 UTC 19 -1200 UTC 20 July 2021 with a record of 522 mm accumulated rainfall. Zhengzhou, the capital city of Henan, received 201.9 mm of rainfall in just one hour on the day. In the present study, the sensitivity of this event to atmospheric variables is investigated using the ECMWF ensemble forecasts. The sensitivity analysis first indicates that a local YellowHuai River low vortex (YHV) in the southern part of Henan played a crucial role in this extreme event. Meanwhile, the western Pacific subtropical high (WPSH) was stronger than the long-term average and to the west of its climatological position. Moreover, the existence of a tropical cyclone (TC) In-Fa pushed into the peripheral of the WPSH and brought an enhanced easterly flow between the TC and WPSH channeling abundant moisture to inland China and feeding into the YHV. Members of the ECMWF ensemble are selected and grouped into the GOOD and the POOR groups based on their predicted maximum rainfall accumulations during the event. Some good members of ECMWF ensemble Prediction System (ECMWF-EPS) are able to capture good spatial distribution of the heavy rainfall, but still underpredict its extremity. The better prediction ability of these members comes from the better prediction of the evolution characteristics (i.e., intensity and location) of the YHV and TC In-Fa. When the YHV was moving westward to the south of Henan, a relatively strong southerly wind in the southwestern part of Henan converged with the easterly flow from the channel wind between In-Fa and WPSH. The convergence and accompanying ascending motion induced heavy precipitation.     

Impacts of New Implementing Strategies for Surface and Model Physics Perturbations in TREPS on Forecasts of Landfalling Tropical Cyclones

To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea (TREPS) in predicting landfalling tropical cyclones (TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–16. For sea surface temperature (SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones. The multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency (SPPT) scheme, was inflated in regions with evident convective activity (f-inflated SPPT). Lastly, the Stochastically Perturbed Parameterization (SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT, the spatially correlated SST perturbations generally showed comparable performance but were more (less) skillful for intensifying (non-intensifying) TCs. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both TC track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest winds and in probabilistic forecasts of rainfall; while for non-intensifying TCs, adding SPP frequently led to positive impacts on the deterministic guidance for track, intensity, strong winds, and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.     

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.     

用统计集合方法制作全国汛期降水滚动预测试验

以全国160站汛期(6-8月)降水量为预测量,以最新得到的74项环流特征量指数为因子,尝试制作全国160站汛期降水滚动预测.建立模型时考虑了预测量与环流特征量因子序列的显著线性变化趋势,以及预测量与环流因子之间的相关不稳定性,用"滑动相关-逐步回归-集合分析"预测方法,分别建立了2009年全国160站汛期降水量的物理统计集合分析预测模型,并进行了近10年独立样本预测试验分析.结果显示:(1)用物理统计集合分析预测方法,以最新得到的74项环流特征量指数为因子,实现了全国160站汛期降水逐月滚动预测,2009年以在5月份制作的滚动预测效果最好.(2)近10年预测试验的空间距平相关系数Acc、业务评分PS和异常级评分TS均高于国家气候中心近年汛期预测业务平均水平.经过不断改进思路和优化具体建模方案,该方法具有较高的业务应用潜力.     

以用户为导向的交互式预报系统及应用研究

基于近年来涌现的有关新一代气象预报系统发展的认识,本文提出用户导向的交互式预报系统的概念模型。新系统强调在用户信息分析的基础上,发展从用户出发再回到用户不断自我改善的预报流程。系统组成的关键模块包括：用户端风险决策动态需求分析模块、物理预测模块、用户目标量的降尺度模块,用户端专业耦合模块及用户风险决策模块。文中阐述了系统中各模块间的联系、用户端信息在系统中的反馈作用以及具体的“交互式”方式。以临沂地区水文用户为例,以引发洪涝的降水事件为预报对象,利用TIGGE全球超集合预报,初步构建了一个临沂水文用户导向的可能致洪降水交互式预报系统。这个具备迭代式自我完善功能的新型预报系统包含了汛期随时变化的用户决策信息、由前期影响雨量和当前水文条件决定的可变致洪降水阈值以及一个动态的用户端预报水平和不确定性评估模块。初步结果表明,结合用户端信息的预报优于未考虑用户信息的预报结果,从而更直接地帮助用户进行防汛决策。个例研究也为发展更完整的用户导向预报系统提供了参考。     

初始场的同化对短期气候预测的影响

基于已建立的三维变分资料同化系统(3D-VAR),利用大气环流模式(IAP9L2°×2.5°-AGCM),对同化和未同化2种初始场分别进行了17a(1988—2004年)的集合回报试验,并对试验结果进行了相关分析.结果表明:在热带地区,2组初始场下的集合回报结果差别很小,除热带外的中高纬地区差别较大,尤其是东亚地区;另外,大部分物理量场的17a异常空间相关系数的均值在同化后的初始场下也得到了提高,可能是因为同化的初始场包含了一段时间的大气信息,动力模式更加协调.     

广东“18.8”季风槽极端降水事件对流尺度集合预报分析

受季风槽影响,2018年8月30—31日华南地区出现一次极端暴雨过程,单日站点累计降水量达1?056.7 mm,刷新了广东有历史纪录以来新的极值。对于此次极端降水事件,常用的业务模式包括欧洲中期天气预报中心全球模式（ECMWF）、日本气象厅谱模式（JMA）和中国气象局广东快速更新同化数值预报系统（CMA-GD）,都低估了降水强度。利用深圳市气象局业务对流尺度集合预报系统分析了此次特大暴雨过程,结果表明：对流尺度集合预报系统对本次特大暴雨过程具有比较好的预报能力,概率匹配平均最大雨量达348.7 mm·(24 h)-1,集合平均的强降水中心和观测基本一致,观测极值附近区域发生大暴雨（≥150 mm）概率最大值达到80%。选取了较“好”和较“差”集合成员预报进行对比分析,发现较“好”成员预报的强降水中心位置和观测基本一致,而较“差”成员预报的降水中心位置则偏向福建地区。较 “好”成员预报出莲花山南侧地面中尺度辐合线较长时间的维持和缓慢移动,导致强降水雨团在莲花山脉附近不断地触发和维持,同时地形的阻挡作用使得对流系统在地形附近区域持续维持,造成了罕见的特大暴雨;而较“差”成员辐合区位于莲花山以北,对流形成后向东、向北移动,最终导致强降水预报位置偏向福建地区。     

基于优化样本组合的集合-变分混合同化方案研究

为有效引入“流依赖”的背景场误差协方差,同时降低集合预报带来的计算量,尝试通过优选与同化时刻天气形势更相似的历史预报样本,并结合预报过程中的时间滞后样本,将两种样本引入集合-变分混合同化系统中,构建基于优选历史预报样本和时间滞后样本的集合-变分混合同化方案。单点观测理想试验表明,优选历史预报样本结合时间滞后样本,既能够缓解样本不足所导致的采样误差,又能够为同化系统提供“流依赖”的背景场误差协方差。连续一周的循环同化及预报试验结果显示,相较于ERA5资料和探空资料,三维变分方案整体表现稍差,样本组合混合同化方案分析场和预报场的均方根误差最小,且比仅用时间滞后样本的混合同化方案有所改进;降水评分整体也表现最优,尤其对中雨和暴雨的模拟改进较明显,较好地模拟出了强降水中心的强度和位置,且改善了降水过报的问题。      

不同季节海温与中国夏季降水的时空特征分析

利用季降水异常的典型集合相关降水模式，分析了不同时段和不同区域的海温场与我国夏季（6～8月）降水之间的时空分布特征。结果表明：各大洋区海温存在着明显的季节、年际和年代际变化。与中国夏季降水相联系的印度洋海温的分布特征与季节有关，存在明显的偶极和单极分布形式，这种海温的异常变化对我国夏季纬向或经向雨带有一定的影响，全球特定的海温分布可以作为中国夏季旱涝预报的信号因子。不同季节海温和中国夏季降水在1970年代末都经历了一次突变，说明海温的季节差异对中国夏季降水的影响明显地受到海洋年代际基本态的制约。