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1.
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   
2.
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   
3.
Channel bank failure, and collapses of shoal margins and beaches due to flow slides, have been recorded in Dutch estuaries for the past 200 years but have hardly been recognized elsewhere. Current predictions lack forecasting capabilities, because they were validated and calibrated for historic data of cross‐sections in specific systems, allowing local hindcast rather than location and probability forecasting. The objectives of this study were to investigate where on shoal margins collapses typically occur and what shoal margin collapse geometries and volumes are, such that we can predict their occurrence. We identified shoal margin collapses, generally completely submerged, from bathymetry data by analyzing digital elevation models of difference of the Western Scheldt for the period 1959–2015. We used the bathymetry data to determine the conditions for occurrence, specifically to obtain slope height and angle, and applied these variables in a shoal margin collapse predictor. We found 299 collapses along 300 km of shoal margin boundaries over 56 years, meaning that more than five collapses occur on average per year. The average shoal margin collapse body is well approximated by a 1/3 ellipsoid shape, covers on average an area of 34 000 m2 and has an average volume of 100 000 m3. Shoal margin collapses occur mainly at locations where shoals take up a proportionally larger area than average in the cross‐section of the entire estuary, and occur most frequently where lateral shoal margin displacement is low. A receiver operating characteristic curve shows that the forecasting method predicts the shoal margin collapse location well. We conclude that the locations of the shoal margin collapses are well predicted by the variation in conditions of the relative slope height and angle within the Western Scheldt, and likely locations are at laterally relatively stable shoal margins. This provides hypotheses aiding the recognition of these features in sandy estuaries worldwide. Copyright © 2018 John Wiley & Sons, Ltd.  相似文献   
4.
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   
5.
Xubin ZHANG 《大气科学进展》2022,39(11):1833-1858
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.  相似文献   
6.
Surface solar irradiance (SSI) nowcasting (0–3 h) is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants. In this study, an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A (FY-4A) geostationary satellite. The system is composed of two key components: The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model. The second component is the SSI nowcasting model, the core of which is the derivation of the cloud motion vector (CMV) using the block-matching method. The goal of simultaneous estimation and nowcasting of global horizontal irradiance (GHI) and direct normal irradiance (DNI) is fulfilled. The system was evaluated under different sky conditions using SSI measurements at Xianghe, a radiation station in the North China Plain. The results show that the accuracy of GHI estimation is higher than that of DNI estimation, with a normalized root-mean-square error (nRMSE) of 22.4% relative to 45.4%. The nRMSE of forecasting GHI and DNI at 30–180 min ahead varied within 25.1%–30.8% and 48.1%–53.4%, respectively. The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern, being lower in spring–summer and higher in autumn–winter. The FY-4A has great potential in supporting SSI nowcasting, which promotes the development of photovoltaic energy and the reduction of carbon emissions in China. The system can be improved further if calibration of the empirical method is improved.  相似文献   
7.
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   
8.
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   
9.
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   
10.
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   
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