基于集合卡尔曼变换的区域集合预报初步研究

为了深入研究集合卡尔曼变换(Ensemble Transform Kalman Filter,ETKF)初值扰动方法,提高集合预报质量,从全球大集合预报资料中提取初始扰动场,建立区域模式的ETKF初值扰动方案,对2008年7月22日发生在中国东部的一次暴雨过程进行集合预报试验,并分析ETKF方案构造的扰动场特征和集合预报效果。结果表明,由ETKF初始扰动方案产生的扰动场大小与分布合理,能够反映观测站点的空间分布,能够保持所有正交、不相关方向的误差方差。集合预报降水落区相对控制预报有所改善,集合平均小雨和中雨TS评分和BS评分总体优于控制预报。24h集合预报的Talagrand分布优于36h预报。试验结果揭示了ETKF初值扰动方案的基本性质及利用ETKF方法进行区域集合预报的可行性。     

GRAPES-Mesov3.1在西南地区2011年汛期的预报检验分析

基于GRAPES-Mesov3.1模式建立的GRAPES-Meso中尺度模式系统在西南区域气象中心运行稳定,该系统于2011年5月投入试验运行。应用GRAPES模式分析产品,NCEP的1°×1°再分析资料,实况资料以及2011年西南低涡探空加密观测资料等,对2011年汛期GRAPES-Meso系统的预报进行统计检验与天气过程分析。结果表明,模式对2011年8月川渝持续高温、9月16~18日四川东北部大暴雨等高影响天气过程有较强的预报能力,这对实际天气预报有着积极的指导意义。预报与实况偏差主要表现在模式通常超报云南地区降水,而对西南其他地区易漏报。模式通常低报青藏高原到四川西部气温,高报四川东部及重庆地区气温。预报高度场持续偏低,西南低空急流预报偏强,对流层中低层比湿偏低,这些可能是造成降水强度偏弱、降水落区偏北、强降水落区偏小的主要原因。对流层中低层高度场持续偏低,低空急流偏强与模式温度预报偏高和加热不均匀有关。同时模式对平原地区较高原山地预报要好,误差通常随等压面高度降低而增大,在一定程度上表明复杂地形对模式预报影响较大。     

动力气候模式输出产品释用技术的研究和应用

对国家气候中心１９９６年１～１２月用Ｔ６３模式所作的５００ｈＰａ３４次月延伸预报进行了检验评估,并对模式的系统性误差进行了分析和订正。在假定订正后的Ｔ６３延伸预报完全准确的条件下,用ＥＣＭＷＦ旬平均高度场和西南区域月平均气温和月雨量资料,建立了用Ｔ６３预报产品预测西南区域月要素场的一种完全预报（ＰＰ）的动力统计关系。并对西南区域１９９６年２月至１９９７年１月逐月平均气温和降水进行了预报试验。     

基于物理力学机制的滑坡数值预报模式: 综述、挑战与机遇

滑坡预测预报是地质灾害防治领域长期以来备受关注的前沿科学问题。当前的研究框架专注于滑坡的变形行为特征与外动力因素, 面临着普适性不强与预报精准度不高的双重瓶颈问题。基于目前研究现状, 系统梳理了滑带流变力学行为与强度弱化效应的内涵, 阐述了滑坡渐进破坏演化机制, 总结了滑坡预测预报模型所包含的类型, 并介绍了其中的典型模型, 指出当前研究主要存在如下问题: ①滑坡演化物理力学模型尚需扩展; ②预测预报模型未能充分结合滑坡演化过程和物理力学模型; ③物理力学模型预测与多场监测数据间的兼容性问题未能实际解决。针对上述问题, 阐述了基于物理力学过程的滑坡预测预报所面临的挑战, 并立足多学科融合与交叉, 提出了开展滑坡预测预报研究的新思路。新思路要求从滑坡滑带介质特性与流变力学行为出发, 建立滑坡演化过程物理力学模型, 紧密结合实时多场监测数据, 建立滑坡数值预报模式, 实现滑坡物理力学过程实时动态更新, 以期实现理论与技术的突破。      

GRAPES_Mesov3.1在西南地区2011年汛期的预报检验分析

基于GRAPES_ Mesov3.1模式建立的GRAPES_ Meso中尺度模式系统在西南区域气象中心运行稳定,该系统于2011年5月投入试验运行.应用GRAPES模式分析产品,NCEP的1°×1°再分析资料,实况资料以及2011年西南低涡探空加密观测资料等,对2011年汛期GRAPES_ Meso系统的预报进行统计检验与天气过程分析.结果表明,模式对2011年8月川渝持续高温、9月16～18日四川东北部大暴雨等高影响天气过程有较强的预报能力,这对实际天气预报有着积极的指导意义.预报与实况偏差主要表现在模式通常超报云南地区降水,而对西南其他地区易漏报.模式通常低报青藏高原到四川西部气温,高报四川东部及重庆地区气温.预报高度场持续偏低,西南低空急流预报偏强,对流层中低层比湿偏低,这些可能是造成降水强度偏弱、降水落区偏北、强降水落区偏小的主要原因.对流层中低层高度场持续偏低,低空急流偏强与模式温度预报偏高和加热不均匀有关.同时模式对平原地区较高原山地预报要好,误差通常随等压面高度降低而增大,在一定程度上表明复杂地形对模式预报影响较大.     

四川盆地雷暴动力及微物理模拟参量与地闪关系的研究

为探讨地闪活动与中尺度数值模式输出量之间的关系,利用新一代中尺度数值模式WRF模拟2011年7月23日四川盆地中的一次强雷暴过程,并对比研究模式输出的动力和微物理场量与地闪频数的关系。结果表明,在WRF模式中采用Kain-Fritsch积云参数化方案和Thompson微物理方案的组合方案能较为成功模拟出此次雷暴过程,模式输出的上升气流和冰相粒子与地闪关系密切,500hPa霰和300hPa冰晶含量在时间上比地闪频数超前约15min,300hPa雪晶含量与地闪频数在时间上基本同步;上升气流速度对冰相粒子的分布起关键作用,上升气流速度较大地区附近相应地闪活动比较频繁。     

西藏那曲一次特大冰雹的数值模拟研究

针对数值模式在模拟高原强对流天气时,如何选取各种微物理过程方案及表征量的问题。采用中尺度天气模式WRF,对2010年9月25日青藏高原那曲地区的一次午后热力雷暴及其带来的冰雹过程进行数值模拟,探讨了不同微物理过程方案在高原地区的适用性,以及高时空分辨率的诊断量对雷暴的反映。将模拟结果与实况卫星云图对比,显示当模式中微物理过程采用WSM3方案时,模拟效果较为准确。模式输出的高分辨率中尺度要素场能较准确地反映雷暴云团的位置情况,模式输出的各种参量(如CAPE,KIm,假相当位温等)随时间的变化情况,均能较准确地描述热力雷暴发生发展过程。     

温州地区一次暴雨过程分析及数值模拟

针对WRF中尺度数值模式较少应用在地形复杂地区,而在浙江省南部山区的温州地区则更缺乏实际应用的案例,利用WRF模式,对2010年5月13～14日温州地区出现的一次暴雨过程进行数值模拟,并利用模式输出的高分辨率资料进行诊断分析.通过分析模拟结果,验证了WRF模式较好地模拟了此次降水过程的时空分布特征,但仍存在着部分区域降水量偏高,暴雨落区偏差等误差.研究结果表明,暴雨中心的强上升运动及低层辐合、高层辐散的配置有利于中尺度对流系统的发生发展,高低空急流耦合是此次暴雨爆发的重要机制.暴雨区域高湿高能环境的维持,保证了暴雨过程的能量输送,也是此次暴雨能够长时间地持续发生的必要条件之一.降水量最大区域的中心与对流层中的最大垂直速度中心都出现在同一区域,而在垂直速度最大区域的两侧都存在有一个垂直的次级环流,这些有利条件对维持强烈的上升运动起关键作用.     

温州地区一次暴雨过程分析及数值模拟

针对WRF中尺度数值模式较少应用在地形复杂地区,而在浙江省南部山区的温州地区则更缺乏实际应用的案例,利用WRF模式,对2010年5月13～14日温州地区出现的一次暴雨过程进行数值模拟,并利用模式输出的高分辨率资料进行诊断分析.通过分析模拟结果,验证了WRF模式较好地模拟了此次降水过程的时空分布特征,但仍存在着部分区域降水量偏高,暴雨落区偏差等误差.研究结果表明,暴雨中心的强上升运动及低层辐合、高层辐散的配置有利于中尺度对流系统的发生发展,高低空急流耦合是此次暴雨爆发的重要机制.暴雨区域高湿高能环境的维持,保证了暴雨过程的能量输送,也是此次暴雨能够长时间地持续发生的必要条件之一.降水量最大区域的中心与对流层中的最大垂直速度中心都出现在同一区域,而在垂直速度最大区域的两侧都存在有一个垂直的次级环流,这些有利条件对维持强烈的上升运动起关键作用.     

因子集成及综合相似预测模型的研究和应用

针对西南区域（云、贵、川、渝）汛期（６－８月）旱涝预测问题,建立了一种以多种物理因子为基础的西南区域降水量预测模型,即因子集成及综合相似预测模型。该模型既考虑影响西南区域汛期降水的主要物理因素变化,又注重区域要素场自身演变规律。对于影响汛期旱涝的物理因子,采用对多个因素进行综合集成的办法,来体现所有因素对旱涝影响的整体作用。为了强调预测结果的客观性,我们借鉴目前ＮＷＰ集合预报思想,引入综合相似方法,给出西南区域大范围汛期降水距平百分率预测结果。近年来业务试用表明,该方法预测准确率稳定,有明显的预报技巧     

蒋庄煤矿16煤层矿井涌水量预计探讨

蒋庄煤矿的主采煤层将由开采3煤层逐渐变为16煤层。为了合理设置和调整矿井排水系统,更好地开展16煤层矿井防治水工作,尽可能准确地预计矿井涌水量。矿井16煤层已开采了多年,其矿井涌水规律需要根据近几年的资料进行总结和研究,对涌水量进行预计。简要分析了蒋庄煤矿矿井水文地质条件,结合矿井实际,对16煤层充水因素进行了分析,确定开采16煤层主要充水水源为十下灰含水层。在此基础上对16煤层涌水量进行了预计,并对预计结果进行了评价。该文分别采用大井法、比拟法和趋势线预测法等3种方法对矿井涌水量进行了预计,其中对传统的大井法进行了改进,并创造性地运用了趋势线预测法,并对各种方法的可靠程度进行了评价,对各种方法预计结果进行比较,在此基础上确定预计的矿井涌水量,从而使得预计的结果更加科学、合理,为下一步矿井防治水工作提供了可靠的依据。     

The sensitivity of numerical simulation to vertical mixing parameterization schemes: a case study for the Yellow Sea Cold Water Mass

The vertical mixing parameterization scheme,by providing the effects of some explicitly missed physical processes and more importantly closing the energy budgets,is a critical model component and therefore imposes significant impacts on model performance.The Yellow Sea Cold Water Mass(YSCWM),as the most striking and unique phenomenon in the Yellow Sea during summer,is dramatically affected by vertical mixing process during its each stage and therefore seriously sensitive to the proper choice of parameterization scheme.In this paper,a hindcast of YSCWM in winter of 2006 was implemented by using the Regional Ocean Modeling System(ROMS).Three popular parameterization scheme s,including the level2.5 Mellor-Yamada clo sure(M-Y 2.5),Generic Length Scale clo sure(GL S) and K-Profile Parameterization(KPP),were tested and compared with each other by conducting a series of sensitivity model experiments.The influence of different parameterization scheme s on modeling the YSCWM was then carefully examined and assessed based on these model experiments.Although reasonable thermal structure and its seasonal variation were well reproduced by all schemes,considerable differences could still be found among all experiments.A warmer and spatially smaller simulation of YSCWM,with very strong the rmocline,appeared in M-Y 2.5 experiment,while a spatially larger YSCWM with shallow mixed layer was found in GLS and KPP schemes.Among all the experiments,the discrepancy,indicated by core temperature,appeared since spring,and grew gradually by the end of November.Additional experiments also confirmed that the increase of background diffusivity could effectively weaken the YSCWM,in either strength or coverage.Surface wave,another contributor in upper layer,was found responsible for the shrinkage of YSCWM coverage.The treatment of wave effect as an additional turbulence production term in progno stic equation was shown to be more superior to the strategy of directly increasing diffusivity for a coastal region.     

Low Frequency Oscillations of the Heat Distribution in the Global Upper Ocean Layers

The heat distributions in the upper layers of the ocean have been studied and some important low frequency oscillations （LFOs） are already found and quantified by using various characteristic factors. In this paper, the ‘heat center＇ of a sea area is defined with a simple method. Then the temperature data set of the upper layer of the global ocean （from surface down to 400 m, 1955-2003） is analyzed to detect the possible LFOs. Not only some zonal LFOs, which were reported early, but also some strong LFOs of the vertical and meridional heat distribution, which might imply some physical sense, are detected. It should be noted that the similar vertical oscillation pattern can be found in the Pacific Ocean, Atlantic Ocean and Indian Ocean. Results from some preliminary studies show that the vertical LFO might be caused by the solar irradiance anomalies. This study may help reveal some unknown dynamical processes in the global oceans and may also benefit other related studies.     

巨型古滑坡的稳定性评价及发展趋势分析

利用尖点突变模型进行滑坡稳定性评价,再以集合经验模态分解、GM(1,1)模型和支持向量机等方法为基础,构建滑坡变形预测模型。以变电站滑坡为例进行分析,结果表明,各监测点的突变特征值均大于0,即处于稳定状态;所得变形预测结果的平均相对误差均较小,验证了本文预测模型的有效性;通过外推预测,发现滑坡变形仍会进一步增加,稳定性变差。     

Distribution of vertical turbulent mixing parameter caused by internal tidal waves and solitary waves in the South Yellow Sea

Many observations show that in the Yellow Sea internal tidal waves (ITWs) possess the remarkable characteristics of internal Kelvin wave, and in the South Yellow Sea (SYS) the nonlinear evolution of internal tidal waves is one of the mechanisms producing internal solitary waves (ISWs), which is different from the generation mechanism in the case where the semidiurnal tidal current flows over topographic drops. In this paper, the model of internal Kelvin wave with continuous stratification is given, and an elementary numerical study of nonlinear evolution of ITWs is made for the SYS, using the generalized KdV model (GKdV model for short) for a continuous stratified ocean, in which the different effects of background barotropic ebb and flood currents are considered. Moreover, the parameterization of vertical turbulent mixing caused by ITWs and ISWs in the SYS is studied, using a parameterization scheme which was applied to numerical experiments on the breaking of ISWs by Vlasenko and Hutter in 2002. It is found that the vertical turbulent mixing caused by internal waves is very strong within the upper layer with depth less than about 30m, and the vertical turbulent mixing caused by ISWs is stronger than that by ITWs.     

利用小波变换对暴雨过程中GNSS气象要素的初步探索

利用小波分解对地基GNSS获取的可降水量(PWV)、气压和对流层延迟(ZTD)等时序进行处理和分析,以暴雨的实际降水量作为判别依据。研究结果表明,1 h间隔PWV与ZTD的小波高频分解系数接近,均能够从中提取暴雨预报特征信息,可用高频ZTD代替PWV进行小波分析;频率在30 min-1h之间的ZTD,预报时间信息应在第1~3层级进行搜寻,30 min以下频率的应在第3~5层级进行搜寻;db4小波分解PWV的暴雨预报阈值可设为-1.2,db4小波分解ZTD的暴雨预报阈值可设置为-0.007,db2小波分解ZTD的暴雨预报阈值可设为-0.01。     

Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors

Based on an empirical orthogonal function (EOF) analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature (OISST) data in the South China Sea (SCS) after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index (DMI), Niño1+2, Niño3.4, Niño3, and Niño4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis (CCA) model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1–12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1–12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1–12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models: one using both DMI and Niño indices and the other using only Niño indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Niño indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Niño indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years (1993/94 to October 2004) by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1–12 month lead time (the persisted prediction). Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time (longer than 3 months). The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1–12 month lead time are also examined.     

基于全连接和LSTM神经网络的空气污染物预测

在空气污染日益严重的情况下进行空气污染物的预测工作是十分必要的。针对城市的空气污染物预测,提出了一种基于神经网络的混合模型方法:通过全连接神经网络方法,结合长短期记忆网络(Long Short-Term Memory,LSTM)方法,将历史空气污染物数据与大气数据进行空间与时间上的挖掘分析。运用全连接和LSTM两种神经网络方法混合的形式,与传统的单一模型方法相比,不仅能摆脱单一模型特征空间的局限性,还能提高预测的精度,具有更大的应用性和操作性。最后,以武汉市为例通过实验证明该混合模型较单一模型在空气污染物预测上具有更高的精度。     

Assimilation of high frequency radar data into a shelf sea circulation model

High Frequency (HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation (OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.     

相关系数稳定性检验及其在水文地质学中的应用

在利用相关分析预报地下水动态变化的过程中,由于忽视了回归方程中相关系数稳定性问题,经常会得到不理想的预报结果。本文在系统地介绍相关系数稳定性检验方法的基础上,通过对甘肃省武威盆地地下水动态变化的预报检验,提出了解决这一问题的办法,即分段截取法。该法使采用相关分析对地下水动态变化做出较高水平的预报成为可能,该法既简单又实用。