基于EEMD和ARIMA的海温预测模型研究          下载免费PDF全文

张莹  谭艳春  彭发定  廖杏杰  余昱昕 《海洋学研究》2019,37(1):9-14

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。  相似文献   

Groundwater contaminant source identification based on iterative local update ensemble smoother          下载免费PDF全文

YANG Ai-lin  JIANG Si-min  LIU Jin-bing  JIANG Qian-yun  ZHOU Ting  ZHANG Wen 《地下水科学与工程》2020,8(1):1-9

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation, and is called groundwater contaminant source identifcation. This is a kind of typical groundwater inverse problem, and the solution is usually ill-posed. Especially considering the spatial variability of hydraulic conductivity field, the identification process is more challenging. In this paper, the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model (MT3DMS) and a data assimilation method (Iterative local update ensemble smoother, ILUES). In addition, Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction. In practical problems, the geostatistical method is usually used to characterize the hydraulic conductivity feld, and only the contaminant source information is inversely calculated in the identifcation process. In this study, the identification of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field. The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site, and ILUES has good performance in solving high-dimensional parameter inversion problems.  相似文献   

A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System     

Zhizhen XU  Jing CHEN  Mu MU  Guokun DAI  Yanan MA 《大气科学进展》2022,39(9):1432-1450

How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts. In this study, a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System (GRAPES) Convection-Allowing Ensemble Prediction System (CAEPS). The nonlinear forcing singular vector (NFSV) approach, that is, conditional nonlinear optimal perturbation-forcing (CNOP-F), is applied in this study, to construct a nonlinear model perturbation method for GRAPES-CAEPS. Three experiments are performed: One of them is the CTL experiment, without adding any model perturbation; the other two are NFSV-perturbed experiments, which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint. Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment, which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts. Additionally, the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables. But for precipitation verification, the NFSV-S experiment performs better in forecasts for light precipitation, and the NFSV-L experiment performs better in forecasts for heavier precipitation, indicating that for different precipitation events, the perturbation magnitude constraint must be carefully selected. All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.  相似文献   

基于CWRF的中国近海热带气旋集合模拟及其对物理参数化方案的敏感性研究          下载免费PDF全文

Wenru Shi  Haishan Chen  Xin-Zhong Liang 《大气和海洋科学快报》2021,14(1):22-26

为评估CWRF模式的降尺度能力和其热带气旋模拟对物理参数化方案的敏感性,本文利用ERI再分析资料驱动CWRF在30km网格上对1982-2016年中国近海热带气旋开展了一次集合模拟.结果表明:CWRF与ERI均能模拟出热带气旋的季节变化和年际变化形势且均存在低估,但相较ERI,CWRF的降尺度技术和集合模拟可以再现更多的热带气旋,显著减少低估.年际变化结果提升最为明显,它对积云方案最为敏感,其次是边界层,陆面和辐射方案,对云和微物理方案较弱.该研究为应用CWRF理解和预报热带气旋提供了参考.  相似文献   

一次沿海局地特大暴雨的集合敏感性分析     

陈伟斌  翟舒楠  韩慎友  陈见 《气象研究与应用》2019,40(1):11-15

利用常规观测资料和EC集合预报资料,基于集合敏感性方法 ,首先分析了2018年10月21日广西沿海局地特大暴雨过程的降水影响天气系统及关键区,并进一步分析了集合预报效果,得到不同层次气压场及风场的降水敏感特征以及集合预报降水预报偏差的可能主要原因,所得结论可供预报参考。  相似文献   

中国地区集合预报产品自适应递减平均偏差订正法的改进研究     

肖瑶  史一丛  王耸  王新伟 《气象与环境学报》2019,(2):9-14

基于中国地区T213集合预报产品2 m温度预报数据,采用卡尔曼滤波类型的自适应递减平均法进行偏差订正处理,原方案在剧烈降温天气订正效果表现不理想。通过对递减平均参数w的重新构建得到改进的订正方案w(i,p)(i为站点信息,p为天气过程信息),在此基础上进一步优化对历史信息的有效提取,得到改进的方案w(i,p)相似法和w(i,p)统计法,并进行效果检验。结果表明:改进为包含空间和天气过程信息的函数w(i,p)后方案的订正效果得到不同程度的提高,其中24 h剧烈降温预报各成员预报均方根误差平均减小了0. 15℃;而进一步改进的w(i,p)统计法在当前几种剧烈降温预报中订正效果最优,其集合平均偏差与w(i,p)方案相比减小2. 54℃。  相似文献   

集合预报误差在GRAPES全球四维变分同化中的应用研究Ⅱ：参数选取与数值试验     

龚建东  张林  王瑞春 《气象学报》2021,79(1):48-64

研究的第一部分讨论了如何有效应用集合预报误差的科学方案,确定了集合预报误差在GRAPES（Global Regional Assimilation and PrEdiction System）全球4DVar（four dimensional variational data assimilation）中应用的分析框架。在此基础上研究了针对集合预报误差实际应用于GRAPES全球4DVar,解决接近或超过100个集合样本数时高效生成的计算效率问题,以及与GRAPES全球4DVar匹配的同化关键参数确定问题。选择基于4DVar的集合资料同化方法生成集合样本,通过将第1个样本极小化迭代过程中产生的预调节信息用于其他样本极小化做预调节,将计算效率提高了2倍。通过时间错位扰动方法增加集合样本数,实现集合样本增加到3倍。对集合方差进行膨胀,并选择水平局地化相关尺度为流函数背景误差水平相关的1.4倍。通过批量数值试验方法确定背景误差与集合预报误差的权重系数,对60个集合样本当集合预报误差权重为0.7时预报效果最好。对北半球夏、冬两季各52 d的批量试验表明,对于南、北半球En4DVar （ensemble 4DVar）较4DVar的改进在冬季主要集中在700—30 hPa,而在夏季主要集中在400—150 hPa。赤道地区受季节影响较小,En4DVar对位势高度、风场与温度的改进都较为明显,且经向风场的改进最为显著。文中研发的集合预报误差在GRAPES全球4DVar中应用的方法合理可行。   相似文献   

数值天气预报和气候预测的集合预报方法:思考与展望          下载免费PDF全文

段晚锁  汪叶  霍振华  周菲凡 《气候与环境研究》2019,24(3):396-406

从初始误差、模式误差以及两者综合影响的角度,综述了天气、气候集合预报方法的研究进展,指出了传统方法的优势,同时也评论了这些方法的局限性,提出了对未来先进集合预报方法的一些思考,以及需要解决的挑战性问题和可能的应用。  相似文献   

基于热层电离层耦合数据同化的热层参量估计   总被引：1，自引：0，他引：1       下载免费PDF全文

何建辉  乐新安 《地球物理学报》2020,63(7):2497-2505

本文采用高效集合卡尔曼滤波(EnKF)算法和背景场热层电离层理论模式NCAR-TIEGCM,开发了热层电离层数据同化系统.基于全球空地基GNSS电离层斜TEC观测、CHAMP和TIMED/GUVI热层参量观测构型设计了系列观测系统模拟实验,对热层参量进行估计.实验结果表明,(1)通过集合卡尔曼滤波算法同化电离层TEC观测能够较好地优化热层参量.(2)中性质量密度优化效果在整个同化阶段均有提升,提升百分比能达到40%.(3)积分氧氮比在同化阶段也能得到较好的优化,但在电子密度水平梯度变化剧烈区域效果较差.最后本文对中性质量密度进行了预报评估,结果表明,由于中性成分优化,在地磁平静条件下其预报时间尺度可长达24h.  相似文献   

State estimation of tidal hydrodynamics using ensemble Kalman filter     

《Advances in water resources》2014

This paper presents a coupling of an ensemble Kalman filter (EnKF) with a discontinuous Galerkin-based, two-dimensional circulation model (DG ADCIRC-2DDI) to improve the state estimation of tidal hydrodynamics including water surface elevations and depth-integrated velocities. The methodology in this paper using EnKF perturbs the modeled hydrodynamics and bottom friction parameterization in the model while assimilating data with inherent error, and demonstrates a capability to apply EnKF within DG ADCIRC-2DDI for data assimilation. Parallel code development presents a unique aspect of the approach taken and is briefly described in the paper, followed by an application to a real estuarine system, the lower St. Johns River in north Florida, for the state estimation of tidal hydrodynamics. To test the value of gauge observations for improving state estimation, a tide modeling case study is performed for the lower St. Johns River successively using one of the four available tide gauging stations in model-data comparison. The results are improved simulations of water surface elevations and depth-integrated velocities using DG ADCIRC-2DDI with EnKF, both locally where data are available and non-locally where data are not available. The methodology, in general, is extensible to other modeling and data applications, for example, the use of remote sensing data, and specifically, can be readily applied as is to study other tidal systems.  相似文献