VARIATIONAL DATA ASSIMILATION USING WAVELET BACKGROUND ERROR COVARIANCE: INITIALIZATION OF TYPHOON KAEMI (2006)

Background error covariance plays an important role in any variational data assimilation system, because it determines how information from observations is spread in model space and between different model variables. In this paper, the use of orthogonal wavelets in representation of background error covariance over a limited area is studied. Based on the WRF model and its 3D-VAR system, an algorithm using orthogonal wavelets to model background error covariance is developed. Because each wavelet function contains information on both position and scale, using a diagonal correlation matrix in wavelet space gives the possibility to represent some anisotropic and inhomogeneous characteristics of background error covariance. The experiments show that local correlation functions are better modeled than spectral methods. The formulation of wavelet background error covariance is tested with the typhoon Kaemi (2006). The results of experiments indicate that the subsequent forecasts of typhoon Kaemi’s track and intensity are significantly improved by the new method.     

A COMPARISON OF THE BLENDING AND CONSTRAINING METHODS TO INTRODUCE LARGE-SCALE INFORMATION INTO GRAPES MESOSCALE ANALYSIS

To solve the problem of mesoscale analysis error accumulation after a period of continuous cycle data assimilation (CCDA), a blending method and a constraining method are compared to introduce global analysis information into the Global/Regional Assimilation and Prediction Enhanced System mesoscale three-dimensional variational data assimilation system (GRAPES-Meso 3Dvar). Based on a spatial filter used to obtain a blended analysis, the blending method is weighted toward the T639 global analysis for scales larger than the cutoff wavelength of 1,200 km and toward the GRAPES mesoscale analysis for wavelengths below that. The constraining method considers the T639 global analysis data as an extra source of information to be added in the 3DVar cost function. The cloud-resolving GRAPES-Meso system (3 km resolution) with a 3 h analysis cycle update is chosen, and forecast experiments on an extreme precipitation event over the eastern part of China are presented. The comparison shows that the inclusion of large-scale information with both methods has a positive impact on the regional model, in which the 3 h background forecasts are slightly closer to the radiosonde observations. The results also show that both methods are effective in improving large-scale analysis while reserving the well-featured mesoscale information, leading to an enhancement in the balance and accuracy of the analysis. Subjective verification reveals that the introduction of large-scale information has a visible beneficial impact on the forecast of precipitation location and intensity. The methodologies and experiences presented in this paper could serve as a reference for ongoing efforts toward the development of multi-scale analysis in GRAPES-Meso.     

A technique for verification of weather forecast and climate simulation with fuzzy sets

In the light of the idea of fuzzy neartude, a fuzzy procedure of verifying weather forecast and climate simula-tion is demonstrated. Several verification indices used in weather forecast, such as hit ratio, Heidke score, Brier score, correlation coefficient and information entropy score, are unified under fuzzy neartude and a new verifica-tion index is proposed on the basis of Hamming distance neartude. Further, D2-statistics used in climate sim-ulation verification is derived from a weighted Euclidean distance neartude. The “January climate” generated by general circulation model (GCM) is also numerically verified.     

A TECHNIQUE FOR VERIFICATION OF WEATHER FORECAST AND CLIMATE SIMULATION WITH FUZZY SETS

In the light of the idea of fuzzy neartude, a fuzzy procedure of verifying weather forecast and climate simulation is demonstrated. Several verification indices used in weather forecast, such as hit ratio, Heidke score, Brier score, correlation coefficient and information entropy score, are unified under fuzzy neartude and a new verification index is proposed on the basis of Hamming distance neartude. Further, D2-statistics used in climate simulation verification is derived from a weighted Euclidean distance neartude. The "January climate" generated by general circulation model (GCM) is also numerically verified.     

CHARACTERISTIC DISTRIBUTION OF SLOPE SCATTERED RADIATION WITH ITS CALCULATION MODEL

Detailed analysis is made of anisotropy of slope scattered radiation(SSR)in terms of the data obtainedby a pyranometer mounted on a theodolite,indicating the change of SSR as a function of orientation and slope.And also reviewed are the models for SSR calculation developed by earlier researchers through the tests withthe data.On this basis a new model for SSR flux density is proposed which is of higher applicability and hasadvantage over the analogues abroad both in physical implication and accuracy of the calculations.     

Analogue Correction Method of Errors by Combining Statistical and Dynamical Methods

Based on the atmospheric analogy principle, the inverse problem that the information of historical analogue data is utilized to estimate model errors is put forward and a method of analogue correction of errors (ACE) of model is developed in this paper. The ACE can combine effectively statistical and dynamical methods, and need not change the current numerical prediction models. The new method not only adequately utilizes dynamical achievements but also can reasonably absorb the information of a great many analogues in historical data in order to reduce model errors and improve forecast skill. Furthermore, the ACE may identify specific historical data for the solution of the inverse problem in terms of the particularity of current forecast. The qualitative analyses show that the ACE is theoretically equivalent to the principle of the previous analogue-dynamical model, but need not rebuild the complicated analogue-deviation model, so has better feasibility and operational foreground. Moreover, under the ideal situations, when numerical models or historical analogues are perfect, the forecast of the ACE would transform into the forecast of dynamical or statistical method, respectively.     

THE DESIGN OF A LIMITED AREA MESOSCALE MODEL AND PREDICTION EXPERIMENTS

Based on the dynamic frame of a 6-layer primitive equations model, a mesoscale primitive equations model is designed that includes larger range of physical processes. It is run in (x, y, σ) system with the model atmosphere topping at 10 hPa and availability of multiple conditions of horizontal boundaries, both horizontal and vertical resolution are adjustable; the precipitation scheme includes large scale and deep cumulus convective precipitation; the ground temperature is computed using surface heat budget equations; exchanges between land atmosphere and between ocean and atmosphere are considered, the Liouis format is used in the computation of vertical exchange budget; a scheme that combines the second and fourth order is employed in horizontal diffusion in which the coefficient is the function of the location of grid points and wind fields; the integration scheme is in the form of economic central difference.With the resolution that horizontal grids are spaced at intervals of 80 km and vertical length is unequally spaced into 16 layers, the model is experimented with 26 cases of forecast. The result has shown stable model computation, good prediction of major synoptic patterns and close reproduction of real precipitation. Statistics for a number of assessment indexes are given in this paper and comparisons are made to the original 6-layer model in respect of the forecasting ability and model properties.     

Dynamical and Microphysical Retrieval from Simulated Doppler Radar Observations Using the 4DVAR Assimilation Technique

Based on a cloud model and the four-dimensional variational (4DVAR) data assimilation method developed by Sun and Crook (1997), simulated experiments of dynamical and microphysical retrieval from Doppler radar data were performed. The 4DVAR data assimilation technique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields were determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities and their model predictions. The adjoint of the numerical model was used to provide the gradient of the cost function with respect to the control variables. Experiments have demonstrated that the 4DVAR assimilation method is able to retrieve the detailed structure of wind, thermodynamics, and microphysics by using either dual-Doppler or single-Doppler information. The quality of retrieval depends strongly on the magnitude of constraint with respect to the variables. Retrieving the temperature field, cloud water and water vapor is more difficult than the recovery of the wind field and rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. The inclusion of a background term, even mean fields from a single sounding, helped reduce the retrieval errors. Less accurate velocity fields were obtained when single-Doppler data were used. It was found that the retrieved velocity is sensitive to the location of the retrieval domain relative to the radars while the other fields have very little changes. Two radar volumetric scans are generally adequate for providing the evolution, although the use of additional volumes improves the retrieval. As the amount of the observations decreases, the performance of the retrieval is degraded. However, the missing observations can be compensated by adding a background term to the cost function. The technique is robust to random errors in radial velocity and calibration errors in reflectivity. The boundary conditions from the dual-Doppler synthesized winds are sufficient for the retrieval. When the retrieval is mainly controlled by the observations in the regions away from the boundaries, the simple boundary conditions from velocity azimuth display (VAD) analysis are also available. The microphysical retrieval is sensitive to model errors.     

Impact of 4DVAR Assimilation of AIRS Total Column Ozone Observations on the Simulation of Hurricane Earl

The Atmospheric Infrared Sounder(AIRS) provides twice-daily global observations of brightness temperature, which can be used to retrieve the total column ozone with high spatial and temporal resolution.In order to apply the AIRS ozone data to numerical prediction of tropical cyclones, a four-dimensional variational(4DVAR) assimilation scheme on selected model levels is adopted and implemented in the mesoscale non-hydrostatic model MM5. Based on the correlation between total column ozone and potential vorticity(PV), the observation operator of each level is established and five levels with highest correlation coefficients are selected for the 4DVAR assimilation of the AIRS total column ozone observations. The results from the numerical experiments using the proposed assimilation scheme for Hurricane Earl show that the ozone data assimilation affects the PV distributions with more mesoscale information at high levels first and then influences those at middle and low levels through the so-called asymmetric penetration of PV anomalies.With the AIRS ozone data being assimilated, the warm core of Hurricane Earl is intensified, resulting in the improvement of other fields near the hurricane center. The track prediction is improved mainly due to adjustment of the steering flows in the assimilation experiment.     

A strategy for merging objective estimates of global daily precipitation from gauge observations,satellite estimates,and numerical predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates(SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps.First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions(MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation(OI)–based merging scheme(referred as the HL-OI scheme)is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d(Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period(2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality.Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD(GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.     

改进的最优窗宽理论对年径流量的插值预测研究——以黄河利津等站为例

针对小样本数据造成年径流量预测效果不理想的情况,以及非对称和非正态资料的处理问题,引入信息扩散和模糊映射思想,同时运用遗传算法改进最优窗宽理论,建立了新的扩散插值模型。该模型通过对零散数据点的信息进行模糊扩散,进而实现对有限数据点信息向其邻近区域点的概率插值。选取黄河利津站为例,根据其近70 a(1942—2011年)径流量实测数据,进行了缺损数据的插值和预测试验,同时与正态扩散插值模型进行对比分析,结果表明:1)预测值能较好地模拟实际径流序列的波形变化,对丰水年(如2007年)和枯水年(如2009年)的预报都比较准确;2)中长期预报(10a)平均相对误差仅为11.59%,相较传统方法有较大的改进;3)以黄河流域2个站点(花园口和兰州)和长江流域的3个站点(朱沱、宜昌和大通)年径流量预测试验以及海温资料的插值试验作为补充,验证了该算法的有效性和普适性。该模型可为实际水文数据资料的客观分析和中长期预报提供参考。     

基于Shepard和OI方法对雨量计逐时资料的分析

引入地形影响效应的日降水量气候分析场,分别运用Shepard和最优插值(OI)两种插值方法对广东和广西2007年5月20日—8月30日汛期小时雨量计降水量进行插值,得到0.125°×0.125°分辨率的规范化网格资料。结果表明:无论是直接插值还是用降水比率(地形影响效应的日降水量气候分析场)插值,两种方法均能很好地体现广东和广西雨量计站点观测降水的季内变化和日变化特征。虽然用直接插值方法比用降水比率插值方法得到的降水空间分布更为平滑,但估值精度没有用降水比率插值方法高。通过交叉检验进一步表明,总体上OI方法优于Shepard方法,而考虑地形影响效应的降水比率OI方法为最优,能有效提高相关性,并减少均方根误差和系统误差。     

徐州农业生产面临气象灾害风险的评估

根据徐州市历年来各种气象灾害对农业造成影响的资料, 应用信息扩散的模糊数学理论模型, 对徐州地区洪涝、干旱、风雹和霜冻等气象灾害进行风险评估, 并对客观风险评估值进行分析, 得到与历史上实际灾害发生的概率较吻合的结论, 使用这种方法为我们开展气象灾害风险评估提供了客观的数据。     

徐州农业生产面临气象灾害风险的评估

根据徐州市历年来各种气象灾害对农业造成影响的资料, 应用信息扩散的模糊数学理论模型, 对徐州地区洪涝、干旱、风雹和霜冻等气象灾害进行风险评估, 并对客观风险评估值进行分析, 得到与历史上实际灾害发生的概率较吻合的结论, 使用这种方法为我们开展气象灾害风险评估提供了客观的数据。     

基于台站降水资料对不同空间内插方法的比较

如何把离散的气象台站资料通过合适的空间内插方法转变成规则的网格数据,对于气候变化分析和模拟研究具有重要的意义.作者利用中国区域160个常用台站10年降水观测资料为例,分别采用克里格(Kriging)插值、反距离加权、Delaunay三角剖分线性插值、双谐样条(Biharmonic Spline)插值和Cressman客观分析等几种常见的空间内插方法,较为系统地分析和比较了这几种内插方法插值结果之间的异同,对其优缺点和适用范围进行了适当讨论,并采用不同的网格分辨率和影响半径对Cressman客观分析方法做了进一步分析比较.结果表明:在台站分布密集的区域不同空间内插方法之间差异较小,在台站分布稀疏的区域则差异较大;与更加密集的台站观测资料的比较显示,自动调节影响半径的Cressman客观分析方法与双谐样条插值方法误差相对较小.     

台风“潭美”(2013)偏心不对称结构特征分析及数值模拟

利用NCEP再分析资料、FY-2E卫星资料和常规观测资料等,采用天气学分析、物理量诊断等方法,对1312号台风"潭美"空心结构特征及成因以及其造成的暴雨天气过程进行了初步分析。结果表明,台风"潭美"深对流云系一块位于台风中心北侧,一块位于南侧,且后期北侧减弱,几乎完全偏向台风的南侧,台风中心周围仅有浅薄稀疏的低云云线。"潭美"动力、热力结构均存在明显空心和不对称性特征,其与中高层气旋性环流偏离、高层辐散不对称、水汽输送不对称等有关;强降水分布与台风不对称性结构及地形等有关。WRF中尺度模式能较好地模拟出不对称性结构以及降水强度和分布。     

双极值模糊集研究综述

大数据时代背景下,不但数据的不确定性愈发明显,数据的冲突性(不相容)现象亦越来越突出.两极性和模糊性一样是事物的本质属性,模糊集虽然是处理不确定信息的有力工具,却一直忽略了不相容两极性问题.双极值模糊集首次将不相容两极性引入到模糊集理论中,为分析和解决带有不相容特性的大数据问题提供了新的思路和方法,其研究渐渐成为热点.综述首先阐述了两极性的种类与特点,以及双极值模糊集的相关概念、理论,特别是和其他已有模糊集的区别;接着分析了双极值模糊集国内外发展现状;最后分析了双极值模糊集未来的发展方向.通过综述,将对双极值模糊集有一个全面的认识,为下一步研究打下基础.     

基于深度学习的数值模式降水产品降尺度方法

提出一种基于深度学习的数值模式降水产品降尺度方法。利用深度学习的非线性映射能力和对栅格数据的信息提取能力,建立深度超分辨率模型提取不同分辨率数值模式降水产品间相对应的有效信息,从而将低分辨率数值模式降水产品利用提取的信息重构为高分辨率产品,继而通过构建多时次组合降尺度深度模型提取时间关联性进一步提升了重构准确性。基于欧洲中期天气预报中心不同尺度数值模式降水产品的实验表明所提方法能够比常用的双三次插值方法更有效地将低分辨率降水产品转换为对应的高分辨率产品。      

GIS技术在台风预报服务产品制作系统中的应用

根据台风预报业务的实际需求,建立了基于GIS技术的台风预报服务产品制作系统。提出了采用地图表达技术的台风数据可视化方案,讨论了基于空间插值与动态缓冲的台风预报概率圆生成算法,利用叠加分析技术完成了台风路径影响范围分析功能,并提供了一键式材料快速制作功能。业务化运行结果表明:GIS技术能够实现功能建设,较好地满足了目前中央气象台台风预报服务产品制作需求。     

应用样条插值提高GRAPES模式物理过程反馈精度

GRAPES (Global/Regional Assimilation and PrEdiction System) 模式动力框架中垂直方向变量的跳层设置采用Charney-Phillips分布,在整层上进行位温、水物质的计算,物理过程中在半层上对其进行处理。这样在GRAPES模式中,进入物理过程之前和物理过程计算完毕之后,都要采用线性插值进行整层和半层之间物理量的转换。由于线性插值精度欠佳,为提高上述反馈过程的精度,并保证水物质的正定性,该研究引入样条插值,并在水物质的插值过程中进行保单调处理,有效减小了位温场、水物质场的预报偏差,并提升了模式的综合预报性能。