ECMWF模式对2020年夏季江淮流域降水的预报偏差分析

目前华北区域实际应用的快速更新多尺度分析和短期预报系统(RMAPS-ST)耦合的陆面模式是Noah,Noah-MP是在Noah基础上发展的一种多物理过程陆面模式,其在RMAPS-ST中的应用效果值得深入探究。选取晴天、多云、降雪、降雨天气个例,在华北区域展开数值试验及分析,结果表明：(1) Noah-MP陆面模式相较Noah陆面模式在RMAPS-ST中有一定的应用优势,其对风速预报性能的改进比气温、比湿显著;(2) Noah-MP陆面模式的应用对晴天的气温、降雪天气的比湿以及多云天气的风速预报性能改进较显著,其中多云天气风速的预报准确率可提高约50.43%;(3) Noah-MP陆面模式的应用对气温预报性能的改进主要集中在山西中南部、河北中南部以及山东地区,对比湿预报性能的改进主要集中在山西中北部、河北北部,对风速预报性能的改进主要集中在山地附近;(4) Noah-MP陆面模式中冠层辐射传输、径流及地下水物理过程对北京地区比湿、风速的预报结果影响不显著,而湍流输送过程选择Chen97方案、径流及地下水过程选择SIMGM方案对地表能量通量模拟效果较优,且对气温的预报性能较好。

     

东北夏季天气分型及EC降水预报空间检验

利用SANDRA（SAN）方法将东北地区2018年5—9月环流背景分型,并在此基础上对EC模式预报的较强降水（>10 mm/24 h）进行空间检验和定量分析。东北地区主要的形势背景分为北部扰动低压型、副热带高压北抬型、东北扰动低涡型、扰动低压东移型。其中,前3类环流型对应较强降水过程发生频率相对较高;将3种类型对应的模式预报较强降水过程进行分析。结果表明：模式对于北部扰动低压型过程中大雨以上量级降水落区面积的预报较实况普遍偏小45%—60%;中雨量级降水落区面积,36 h时效预报较实况偏大40%,84 h时效偏小19%;36 h、60 h、84 h时效,较强降水预报位置偏西分别为0.19°、0.53°、1.39°,平均强度预报分别偏低2.9 mm、3.1 mm、3.4 mm,极值预报分别偏低7.3 mm、8.1 mm、9.4 mm;副热带高压北抬型过程预报面积与实况之间的偏差没有一致的倾向性,预报位置较实况分别偏南0.25°、0.15°、0.37°,降水强度上有65%—72%的个例表现为平均强度及极值预报较实况偏弱的特征;东北扰动低涡型过程,预报位置偏差分别为36 h偏东0.18°、偏南0.55°,60 h偏东0.20°、偏南0.58°和84 h偏东0.74°;另外,3个时效对应平均强度预报分别偏低3.3 mm、3.7 mm、3.9 mm,极值预报平均偏低为10.2 mm、10.6 mm、11.6 mm。     

Evaluation of the ECMWF Precipitation Product over Various Regions of Iran

Easy access to accurate and reliable climate data is a crucial concern in hydrological modeling.In this regard,grid-ded climate data have recently been provided...     

ECMWF极端天气指数在华东台风降水预报中的应用和检验

选取2015—2018年影响华东地区的13个台风个例,分析降水极端天气指数EFI(Extreme Forecast Index)和SOT(Shift of Tails)与台风降水之间的统计关系。结果表明：EFI和SOT与降水气候百分位之间存在明显的正相关关系。EFI和SOT越大,强降水发生概率越高。随着预报时效的增加,EFI和SOT指数对暴雨和大暴雨的预报效果逐渐变差。对于短期(72 h以内的时效),EFI预报技巧优于SOT,而随着预报时效的延长,SOT的预报技巧逐渐接近并超过EFI。以TS评分最大为标准兼顾合理的预报偏差,得到两种极端天气指数不同预报时效、不同等级暴雨的预报阈值。总体而言,事件越极端,EFI和SOT的预报阈值越大,对于暴雨和大暴雨,EFI指数的预报阈值随着预报时效的延长有减小趋势,而SOT的预报阈值基本保持不变。在台风极端降水预报中,EFI和SOT可以作为EC定量降水预报的补充,有助于减少强降水的漏报,并提早发出预警信息。     

Nonparametric kernel estimation of annual precipitation over Iran

In this paper, annual precipitation data sets from five old rain gauge stations (Bushehr, Isfahan, Meshed, Tehran, and Jask) in Iran were fitted to nonparametric kernel function by using rectangular, triangular, and Gaussian or normal as kernel functions. The smoothing parameter was calculated by four methods including rule of thumb, Adamowski criterion, least squares cross-validation, and Sheater and Jones plug-in. The Adamowski criterion showed a better performance compared to other methods due to goodness of fit tests. The results of these proposed nonparametric methods will be then compared to the results of the parametric density functions including normal, two and three parameter log-normal, two parameter gamma, Pearson and log-Pearson type 3, Gumbel or extreme value type 1 and also Fourier series method which were applied by a previous study for the same stations. It was concluded that the annual precipitation data were fitted to nonparametric methods better than parametric methods.     

ECMWF降水极端天气指数在浙江的应用评估

利用2016年6月—2017年5月ECMWF降水极端天气指数(EFI)预报资料,分析了降水EFI与不同量级强降水、降水气候百分位在浙江的关系。结果表明:总体而言,浙江省降水EFI与实况降水存在明显的正相关关系。随着EFI阈值的增加,暴雨发生频次先增加后减少,而且暴雨发生的概率随着EFI阈值的增加而增大。综合考虑TS、BS评分,EFI阈值随着预报时效的延长而减小;随着降水量级的增加而增大。降水EFI值与降水气候百分位存在明显的正相关关系,当EFI值较高时,预示着较大的几率出现极端降水,此时可参考当地相对应的气候百分位的降水量来估计降水。     

Bayesian regression model for seasonal forecast of precipitation over Korea

In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N?C42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June?CJuly?CAugust; JJA) for the period of 1979?C2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.     

中国区域降水偏差订正的初步研究

基于中国气象局公共气象服务中心提供的降水预报资料,利用频率匹配法和阈值法对2015年全年的降水预报进行偏差订正并对订正后的结果进行检验。结果表明:(1)偏差订正可显著减少集成系统降水预报的小雨空报现象,改善"有雨或无雨"的定性预报性能,提高集成系统的晴雨预报准确率。(2)订正后降水量级大小更接近实况降水,并且集成系统预报的平均绝对误差和面积偏差(干偏差或湿偏差)均有所降低。(3)偏差订正对降水预报的改善程度与系统本身预报性能有关,系统本身预报误差越大,订正效果越好。     

ECMWF集合预报产品在重庆暴雨预报中的检验与应用北大核心

利用重庆地区34个国家气象站降水资料和ECMWF集合预报降水资料,系统检验和评估了集合预报统计量产品及后处理技术产品对2014—2016年5—9月重庆暴雨的预报性能。结果表明:集合统计量产品中最大值、90%分位数、融合产品、概率匹配平均、75%分位数对暴雨预报有一定参考性,其中90%分位数和融合产品对暴雨落区预报较好,最大值对暴雨强度预报有一定指示意义,但表现为明显的湿偏差。集合预报后处理技术产品的暴雨TS评分较控制预报和集合平均有明显提高,其中概率预报、最优百分位、融合—概率匹配、频率匹配法的暴雨TS评分超过最大值,对暴雨强度预报具有较好的指导意义,其预报偏差均表现为湿偏差,融合—概率匹配和频率匹配法对暴雨落区预报较好,概率匹配—融合对降低暴雨空报率较好。     

Trends in monthly precipitation over the northwest of Iran (NWI)

Increasing global temperatures during the last century have had their own effects on other climatic conditions, particularly on precipitation characteristics. This study was meant to investigate the spatial and temporal monthly trends of precipitation using the least square error (LSE) approach for the northwest of Iran (NWI). To this end, a database was obtained from 250 measuring stations uniformly scattered all over NWI from 1961 to 2010. The spatial average of annual precipitation in NWI during the period of study was approximately 220.9–726.7 mm. The annual precipitation decreased from southwest to northeast, while the large amount of precipitation was concentrated in the south-west and in the mountainous areas. All over NWI, the maximum and minimum precipitation records occurred from March to May and July to September, respectively. The coefficient of variation (CV) is greater than 44 % in all of NWI and may reach over 76 % in many places. The greatest range of CV, for instance, occurred during July. The spatial variability of precipitation was consistent with a tempo-spatial pattern of precipitation trends. There was a considerable difference between the amounts of change during the months, and the negative trends were mainly attributed to areas concentrated in eastern and southern parts of NWI far from the western mountain ranges. Moreover, limited areas with positive precipitation trends can be found in very small and isolated regions. This is observable particularly in the eastern half of NWI, which is mostly located far from Westerlies. On the other hand, seasonal precipitation trends indicated a slight decrease during winter and spring and a slight increase during summer and autumn. Consequently, there were major changes in average precipitation that occurred negatively in the area under study during the observation period. This finding is in agreement with those findings by recent studies which revealed a decreasing trend of around 2 mm/year over NWI during 1966–2005.

     

中国东南地区复杂地形下降水概率预报的订正研究

使用TIGGE (the THORPEX interactive grand global ensemble)资料集下欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasts, ECMWF)逐日起报的预报时效为24～168 h的日降水量集合预报资料,集合预报共包括51个成员,利用左删失的非齐次Logistic回归方法(left-Censored Non-homogeneous Logistic Regression, CNLR)和标准化的模式后处理方法(Standardized Anomaly Model Output Statistics, SAMOS)对具有复杂地形的中国东南部地区降水预报进行统计后处理。结果表明：采用CNLR方法能够有效改进原始集合预报的平均绝对误差(Mean Absolute Error, MAE)和连续分级概率评分(Continuous Ranked Probability Score, CRPS),提升了降水的定量预报和概率预报的预报技巧。而使用SAMOS方法对数据进行预处理,考虑地形...     

Impact of ECMWF,NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian Region

The global model analysis has significant impact on the mesoscale model forecast as global model provides initial condition (IC) and lateral boundary conditions (LBC) for the mesoscale model. With this objective, four operational global model analyses prepared from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS), NCEP Global Forecasting System (GFS), and National Centre for Medium Range Weather Forecasting (NCMRWF) are used daily to generate IC and LBC of the mesoscale model during 13th December 2012 to 13th January 2013. The Weather Research and Forecasting (WRF) model version 3.4, broadly used for short-range weather forecast, is adopted in this study as mesoscale model. After initial comparison of global model analyses with Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles, daily WRF model forecasts initialized from global model analyses are compared with in situ observations and AIRS profiles. Results demonstrated that forecasts initialized from the ECMWF analysis are closer to AIRS-retrieved profiles and in situ observations compared to other global model analyses. No major differences are occurred in the WRF model forecasts when initialized from the NCEP GDAS and GFS analyses, whereas these two analyses have different spatial resolutions and observations used for assimilation. Maximum RMSD is seen in the NCMRWF analysis-based experiments when compared with AIRS-retrieved profiles. The rainfall prediction is also improved when WRF model is initialized from the ECMWF analysis compared to the NCEP and NCMRWF analyses.     

夏季淮河流域大气环流型在降水趋势预测中的应用

采用NCEP/NCAR逐日海平面气压场资料,利用Lamb-Jenkinson大气环流分型方法对淮河流域夏季环流进行分型,并利用逐步回归方法建立了淮河流域夏季降水趋势的预测模型。结果表明:影响夏季淮河流域的主要环流型有南风型、西南风型、东南风型及气旋性环流,这些环流型都具有显著的年际和年代际变化特征,利用大气环流型建立的夏季淮河流域降水趋势预测模型具有一定的预报能力。     

Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km², by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t _47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t _47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.     

基于ECMWF模式的四川夏季强降水订正试验

利用2016—2018年6—8月四川地面观测降水资料(含加密自动站)及同时段ECMWF模式各要素预报场资料,根据基于"配料法"计算所得出的3 h间隔短时强降水概率预报,统计各格点各个转换概率阈值的次数,探索了一种针对模式24 h累计降水预报的强降水订正方法,并运用该方法对2018年6—8月降水集中时段24—72 h时效ECMWF模式降水预报进行逐日试验检验。试验结果表明:(1)从大雨、暴雨降水量级综合检验指标来看,各时效订正后命中率、漏报率、TS评分均有明显改善,且随着预报时效的延长,各指标数值提高的幅度愈大。空报率虽然0—24 h、24—48 h时效预报有所增加,但空报率增加幅度远小于漏报率减小幅度;(2)从个例检验结果来看,订正后的模式预报相比订正前的预报而言,降水量级明显增加,50 mm以上降水落区预报效果有较大程度提升,尤其是0—24 h时效预报,订正后降水落区分布与实况基本一致。     

基于降水极端预报指数的福建台风极端降水预报研究

应用1961—2017年中国气象局热带气旋最佳路径数据集、国家地面气象观测站日降水观测资料和2015年8月—2017年12月欧洲中期天气预报中心(ECMWF)集合预报系统降水极端预报指数(EFI)数据,根据百分位法定义台风影响期间福建省各站点的台风极端降水阈值,采用最小阈值法剔除台风极端降水时EFI箱线图中的异常值,保...     

Spatiotemporal changes in precipitation concentration over Iran during 1962–2019

Climatic Change - Changes in precipitation pattern can lead to widespread impacts across natural and human systems. This study assesses precipitation variability as well as anthropogenic and...     

基于CEEMDAN-SE-ARIMA组合模型的东北夏季降水预测

针对传统时间序列模型无法有效预测模态混叠数据的不足,本文提出了一种基于CEEMDAN-SE-ARIMA的组合模型,并且对东北地区2016—2020年夏季降水量进行了实证分析。首先,基于完全自适应集合经验模态分解方法,将降水时间序列分解为多个本征模态分量,并根据不同分量样本熵的计算结果进行分量序列重构。然后,针对每一个重构分量,构建自回归移动平均预测模型。最后,将各分量的预测值进行叠加,得到组合模型的预测值。此外,还构建了ARIMA单一模型和其他组合模型,旨在与CEEMDAN-SE-ARIMA组合模型对比。结果表明：CEEMDAN-SE-ARIMA组合模型考虑了时间序列的模态混叠特征,能有效提高东北地区夏季降水时序模型的预测能力,具有良好的预测应用价值。预测结果较单一模型和其他组合模型均有所提高,MASE降低了0.02～0.91 mm, RMSE降低了0.80～130.49 mm, MAE降低了2.52～129.84 mm, MAPE降低了1.08～35.53 mm。CEEMDAN-SE-ARIMA模型在降水变率较小的西北部区域预测效果更好,对东南部区域的极值分布中心预测较为准确。     

CFSv2对2018年浙江梅雨降水多尺度预测的性能评估

从梅雨预测的业务需求出发,系统开展了CFSv2模式对2018年浙江梅雨期降水预报能力的多时间尺度评估。结果发现3月1日—5月31日的起报结果整体上未能较准确地预测6月浙江大部降水偏少的趋势、仅5月31日的预测结果与实况相符;在延伸期尺度上,CFSv2预测的梅雨期总降水量较实况偏少30%左右;基于相关系数、均方根误差和新定义的综合预报技巧指数等指标分析模式的延伸期预报性能,发现对梅雨期总降水量、逐日区域平均降水量和逐日全省各站降水量的预报技巧有限,对浙江梅雨区的预报水平总体高于浙江全省。评估结果表明CFSv2预报产品表现出显著的系统性干偏差;在延伸期尺度上,随着预报时效的缩短,预报效果并非逐步提升、而是客观存在一个最佳预报时效,各起报日也分别对应着不同的最优预报时段,整体而言梅雨降水的延伸期预测可能对初值并不敏感。