共查询到19条相似文献，搜索用时 120 毫秒
1.
在集合卡尔曼滤波方法中, 根据预报集合统计提供的依流型而变的预报误差协方差对同化起到决定性的作用。但在集合样本容量不足及模式存在系统误差时, 由预报集合估计的预报误差协方差会出现明显偏差。既要减小这种估计偏差对同化产生的影响而又不增加计算量, 一种可供选择的方法是将定常或准定常的高斯型预报误差协方差和由预报集合估计的预报误差协方差加权平均用于集合卡尔曼滤波同化。利用浅水方程模式, 通过观测系统模拟试验检验在不同的模式误差、 集合成员数以及观测密度条件下, 将这种混合预报误差协方差矩阵用于在集合平方根滤波的效果。试验结果表明, 当预报集合成员数较多而模式又无误差时, 不必采用混合的预报误差协方差矩阵, 否则, 采用混合的预报误差协方差矩阵都有可能改进分析和预报。混合预报误差协方差的最优的权重系数与模式误差关系密切, 模式误差越大, 定常预报误差协方差的权重越大。最优的权重系数与集合成员数及观测密度也有一定关系。 相似文献
2.
集合卡尔曼滤波同化探空资料的数值试验 总被引：4，自引：1，他引：3
应用集合卡尔曼滤波（Ensemble Kalman Filter;EnKF）方法,同化了2005年7月一次暴雨过程的探空观测资料,并用非静力中尺度模式MM5进行数值模拟试验。结果表明：在理想模式的假设下,即假设真实模拟和所产生的集合用的是同一个模式并有相同的初始误差,EnKF方法同化的分析结果较好。如果不运用EnKF方法同化探空观测资料,则集合预报结果和不加扰动的单个数值预报结果都没有EnKF方法同化过的好。 相似文献
3.
在一个简化气候模式的混沌态上,针对模式存在参数误差,改变观测误差量级,对滞后时间集合预报的效果进行分析。数值试验结果表明:滞后时间集合预报的预报效果与滞后时段有关,并非过去时刻资料使用的越多,预报效果就越好。存在观测误差的滞后时间集合预报也是有效果的,滞后时间集合预报效果要明显好于控制试验。只使用过去多个精确初始场进行滞后时间集合预报并不能订正模式误差,观测误差越大,最优回溯时段越短。 相似文献
4.
利用江西七一水库水电站2016—2017年水文资料,基于遗传算法对七一水库来水流量新安江模型进行了参数优化率定,并采用参数率定前后2种来水流量模型对七一水库2018年的预报结果进行检验,重点分析了水库来水流量预报结果的日误差、月误差和年误差以及强降水过程流量预报误差等。结果表明：1) 遗传算法参数优化后的模型流量预报误差减小;2) 日误差在7月中旬以后明显减小;3) 月误差在7月之前以正偏差为主,7月以后以负偏差为主;4) 参数优化后的年误差整体好于优化前;5) 在强降水过程实例流量预报误差分析中,参数优化后的误差明显减小,但不同强降水过程误差程度相差较大,可能与降水空间分布的不连续性、面雨量计算方法的局限性以及降水的天气类型有关。 相似文献
5.
利用2016—2018年4月1日至6月30日三个全球数值预报业务中心（CMA、ECMWF和NCEP）的24 h降水集合预报资料和辽宁省降水观测资料,采用TS评分、预报偏差B、Talagrand分布以及BS评分等方法对辽宁省春季透雨（4—6月）CMA、ECMWF和NCEP三套全球集合预报结果进行对比分析。结果表明：三个集合预报中心的集合预报系统的离散度均具有偏小的特征,Talagrand都呈U型分布,即各集合预报系统对量级较小的降水预报值偏大,空报率高;对量级较大的降水预报能力不足,极值偏小,容易产生降水预报偏差。将各中心的确定性检验结果和概率性检验结果进行对比后发现,ECMWF相比CMA和NCEP的TS评分值更高,预报偏差B值更接近于1,也就是说另外两个预报中心对辽宁省春季透雨预报漏报更为明显。从BS评分值和其分解评分值结果来看,ECMWF优于另外两个预报中心。ECMWF对辽宁省春季透雨预报的结果与实况最为接近,检验结果最好,可在日后的预报服务工作中作为主要参考。 相似文献
6.
GRAPES区域集合预报系统对登陆台风预报的检验评估 总被引：4，自引：6，他引：4
针对2015年7—9月登陆中国大陆沿海的台风,利用GRAPES-REPS区域集合预报资料和集合统计诊断分析方法,对登陆台风的移动路径、时间、地点、强度和降水等进行检验评估,以期为预报员应用GRAPES登陆台风概率预报提供依据。检验结果表明,(1)集合平均移动路径要优于控制预报,集合预报各成员登陆地点存在20~340 km差异,但实况登陆地点均能落在集合成员登陆地点中。(2)对24 h和48 h登陆地点误差而言,集合平均较控制预报更接近实况。(3)随着预报时间的趋近,集合平均、控制预报和集合成员登陆地点距离误差逐渐缩小,登陆地点空间位置预报也没有明显的系统性误差。(4)集合成员对台风登陆时间预报偏早,平均提前2.3 h。(5)在强度预报中,尽管最低气压和近中心最大风速存在登陆前偏弱而登陆后偏强的趋势,但登陆点预报值区间包含了实况观测值,表明GRAPES-REPS集合预报能够较好展示多种可能信息。(6)不同量级降水AROC评分为0.56~0.76,具有预报参考价值;另外AROC评分的高低及台风暴雨落区的准确性与台风登陆点和登陆时间误差密切相关。可见,GRAPES-REPS区域集合预报可以在台风登陆地点、时间、强度和降水预报等方面提供更多的预报不确定性信息,有助于做出正确的预报决策。 相似文献
7.
利用TIGGE(THORPEX Interactive Grand Global Ensemble)单中心集合预报系统(ECMWF、United Kingdom Meteorological Office、China Meteorological Administration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesian model averaging,BMA)参数,从而建立地面日均气温BMA概率预报模型.由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好.多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好.它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%.基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义. 相似文献
8.
CoLM模式地表温度变分同化研究 总被引：2，自引：1，他引：1
本文采用变分方法对通用陆面模式 (CoLM) 中的地表温度进行同化.同化伴随约束条件采用CoLM模式中的地表及植被能量平衡方程,调节因子采用裸土及植被蒸发比.采用美国通量网 (AmeriFlux) 中的Bonville站数据对同化方法进行了单点验证,验证结果表明同化后地表温度以及蒸散结果更加接近于实测值.选取中国华北地区对同化方法进行区域验证,结果显示每天仅采用白天一次观测值对地表温度进行同化的方法是有效的.通过对同化前后地表温度误差直方图比较可以发现,在有MODIS观测值的区域,同化后白天地表温度误差大大降低,同时,同化后地表蒸散空间分布图也发生了变化.单点验证以及区域验证结果都表明了变分同化方法是可靠的.变分同化方法可以改进陆面模式模拟结果,对于地表过程研究中的植被生态、水文等研究具有重要意义,同时,陆面模式可以与数值预报模式进行耦合,改进数值预报结果. 相似文献
9.
ENSO集合预报系统的检验评价 总被引：7，自引：2，他引：5
讨论了一个热带太平洋海气耦合集合预报系统集合预报的检验问题。该集合预报系统模式为一个中等复杂程度的耦合模式,其中大气部分为统计模式,海洋部分为动力模式。初始扰动利用集合Kalman滤波同化得到,模式误差扰动由一个一阶马尔可夫随机微分方程生成,预报集合样本为100个。利用1995~2005年的观测资料进行了确定性预报检验,包括相关系数和均方根误差。在概率预报检验方面,包括Talagrand概率分布、离散度、Brier评分(BS)、命中率以及空报率的统计检验,并且根据检验结果对预报系统进行了初步评价。确定性检验表明,集合样本均值的预报水平在热带中太平洋区域要高于热带东太平洋和沿岸区域。同时概率预报检验结果表明,集合预报系统有较高的概率预报技巧,对确定性预报是一个完善和补充。 相似文献
10.
基于全球集合预报系统(GEFS)资料,利用WRF中尺度模式及GEFS动力降尺度获取区域集合预报初值场,通过对同化后的分析场进行模式积分实现华南前汛期区域集合预报。对2019年6月10日的一次华南前汛期暴雨过程进行不同同化方案的试验:混合同化(Hybrid)、三维变分(3Dvar)、集合卡尔曼滤波(EnKF)和对比试验(Ctrl)四组试验的对比分析,探讨具有不同背景误差协方差矩阵的同化方案对区域集合预报集合扰动和集合离散随时间演变特征的影响,评估不同试验的降水模拟效果。(1) Hybrid对模式初始场有较好的改善作用,而3DVar和EnKF对初始场的改善作用不明显。(2) 对风场、温度场和湿度场,在前期预报中Hybrid的预报误差小于3DVar和EnKF,在中后期的预报中,3DVar和EnKF的预报误差得到改善,且好于Hybrid。同样,集合扰动能量,Hybrid和Ctrl在前期预报发展好于3DVar和EnKF,而在中后期的预报3DVar和EnKF好于Hybrid和Ctrl。(3) 从24 h累积降水评分中,整体上同化试验好于Ctrl,3DVar和EnKF好于Hybrid,且3DVar对大中雨级别的降水评分较好,而EnKF对暴雨以上级别的降水评分较好。(4) 对于集合统计检验分析,同化试验的AUC值都大于Ctrl的AUC值,24 h累积降水量阈值在10~100 mm的AUC值,3DVar最好;而125 mm阈值的AUC值,EnKF最好。 相似文献
11.
利用淮河流域加密站点2008年6月1日—8月31日逐日降水资料、对应的T213模式的24 h, 48 h以及72 h集合预报,采用贝叶斯模型平均 (Bayesian Model Averaging,BMA) 方法对集合预报15个成员的降水预报进行了概率集成与偏差订正,采用排序概率评分 (CRPS)、平均绝对误差 (MAE) 对BMA的订正结果进行检验,并将订正后的降水预报输入VIC (Variable Infiltration Capacity) 水文模型中进行水文概率预报。结果表明:经BMA订正后的24 h, 48 h, 72 h降水预报精度较订正前有所提高;BMA模型给出的有效区间 (第25百分位数至第75百分位数) 预报将实况降水量包含在内的可能性比订正前更大;由水文概率预报检验指标分析可知,经BMA订正的降水集合预报,由VIC水文模型模拟得到的径流量变化趋势与实况较吻合。 相似文献
12.
Over the mid-latitude North Pacific, there is a close relationship between interannual variations of the sea surface temperature (SST) and surface shortwave radiation during boreal summer. The present study evaluates this relationship in coupled model simulations, forced model simulations, and retrospective forecasts. It is found that the simulation of this relationship in climate models is closely related to the model biases in the meridional gradients of mean SST and surface shortwave radiation. A southward shift in the region of large mean meridional gradients leads to a similar southward shift in the region of large correlation between the SST and shortwave radiation variations. The relationship is enhanced (weakened) when the mean meridional gradients are stronger (weaker) compared to observations. The shortwave radiation?CSST correlation is weak in individual forced simulations because of the interference of internally generated shortwave radiation variations. The shortwave radiation?CSST correlation increases significantly in the ensemble mean due to reduction of internally generated variability. The long-lead Climate Forecast System (CFS) forecasts have a better simulation of the shortwave radiation?CSST correlation compared to the short-lead forecasts. Estimation based on the CFS ensemble forecasts indicates that the high-frequency atmospheric variations contribute importantly to the SST variability over the mid-latitude North Pacific during boreal summer. 相似文献
13.
Influence of SST biases on future climate change projections 总被引：1，自引：0，他引：1
We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection. 相似文献
14.
The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me... 相似文献
15.
In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations. 相似文献
16.
水文集合预报是近几年正在形成和发展的水文预报分支,其发展大致可分为两个阶段:第1阶段是1970年至20世纪末进行的长期径流预报,第2阶段从21世纪开始,主要学习气象数值预报中集合预报的概念在短期水文集合预报中的应用。目前,除了单一预报中心的集合预报系统在水文集合预报中应用外,多个预报中心的集合预报大集合也逐渐被应用于流域水文预报,甚至一些小流域的洪水预报。如利用TIGGE(THORPEX Interactive Grand Global Ensemble)集合预报驱动形成的大气-水文-水力的串联系统进行早期的洪水预警研究,将全球集合预报作为洪水模型输入的有限区域模式的初始条件和侧边界条件的研究。这些均表明,基于水文集合预报的洪水预报增加了预报附加值,并能够延长预警提前时间。以欧洲中期天气预报中心的欧洲洪水预警系统(EFAS)和美国NOAA的先进水文预报系统(AHPS)为代表,实现了集合预报在洪水中的实时业务预报,但仍存在数据处理和计算量大,以及如何基于集合水文预报做决策等问题。对于水文集合预报的前处理和后处理的各种技术已处于探索和验证阶段,如何更好地理解基于概率预报的洪水预警决策仍存在许多困难和挑战。 相似文献
17.
Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used. 相似文献
18.
利用对流许可尺度集合预报系统,针对2015年4月28日夜间移动到江苏南部和上海地区,伴随短时强降水和冰雹的一次强对流过程,使用初始多源融合分析场对集合预报结果影响进行了分析。结合上海南汇双偏振雷达基数据观测,对12～14 h预报时效的反射率因子、差分反射率及冰雹集合预报结果进行了定性和定量的评估,分析了改进初始水物质分布,同时增加小尺度信息对于模式预报结果的影响。主要结果为:(1)对反射率因子预报的评估显示,初始场调整了水物质分布且增加了小尺度信息的试验(以下简称ADAS试验),对降水的范围、分布特征及评分都有明显改进(2)由于差分反射率在较小的距离内变化剧烈,对其准确预报难度较大,ADAS试验虽然预报强度偏强,但整体的位置和强度与实况更为接近,特别在大粒子预报方面具有更高的技巧,能够对微物理过程相关特征更好地进行描述;(3)使用地面人工观测和双偏振雷达观测对冰雹概率预报评估的结果显示,ADAS试验预报的高概率降雹区与观测落区接近,对冰雹落区预报具有一定的指示意义。通过多源融合分析调整初始水物质分布并增加小尺度信息的集合预报试验改善了较长预报时效的强降水和冰雹概率预报,具有更高的可信度,双偏振变量预报具有区分强降水与冰雹的优势,通过与观测的对比可以更好地评估模式对微物理过程描述的准确性。 相似文献
19.
Lili LEI Yangjinxi GE Zhe-Min TAN Yi ZHANG Kekuan CHU Xin QIU Qifeng QIAN 《大气科学进展》2022,39(11):1816-1832
An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (WRF) is cycled and evaluated for western North Pacific (WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone (TC) minimum sea level pressure (SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient variance for TC intensity. The 6-h ensemble priors from the WRF/EnKF system tend to overestimate the intensity for weak storms but underestimate the intensity for strong storms. The 5-d deterministic forecasts launched from the ensemble mean analyses of WRF/EnKF are compared to the NCEP and ECMWF operational control forecasts. Results show that the WRF/EnKF forecasts generally have larger track errors than the NCEP and ECMWF forecasts for all TC categories because the regional simulation cannot represent the large-scale environment better than the global simulation. The WRF/EnKF forecasts produce smaller intensity errors and biases than the NCEP and ECMWF forecasts for typhoons, but the opposite is true for tropical storms and severe tropical storms. The 5-d ensemble forecasts from the WRF/EnKF system for seven typhoon cases show appropriate variance for TC track and intensity with short forecast lead times but have insufficient spread with long forecast lead times. The WRF/EnKF system provides better ensemble forecasts and higher predictability for TC intensity than the NCEP and ECMWF ensemble forecasts. 相似文献