卫星区域大气探测业务处理系统

本文简要介绍目前卫星区域大气探测资料业务处理系统的软件结构、算法和生成的产品。将系统处理结果与常规观测值作比较分析的结果表明:大气温度反演平均均方根误差为2.45 ℃，水汽混合比平均均方根误差为1.47 g/kg，层平均厚度误差为21.4位势米，臭氧总含量均方根误差约为10%（Dobson），长波辐射通量均方根误差约为1.7 W/m2。     

Effective Accuracy of Satellite-Derived Hourly Irradiances

Summary  Estimates of hourly global irradiance based on geostationary satellite data with a resolution of several (2 to 10) kilometres reproduce ground-measured values with a Root Mean Square Error (RMSE) of typically 20% to 25%. The different components of this RMSE have been enumerated by several authors but, due to the lack of adequate measurements, their respective importance is not well settled. In the present study we attempt to quantify these components from a practical point of view, that is from the point of view of users having to rely on time/site specific irradiance data. We conclude that the intrinsic, or “effective” RMSE is more along the line of 12%. This effective RMSE is the measure of the methodological imprecision (satellite-to-irradiance conversion models). The remaining part of the overall RMSE is the amount by which spatially averaged satellite-derived estimates are, by their very nature, bound to differ from ground-measured local insolation. Received August 15, 1997 Revised March 4, 1998     

NCEP/NCAR再分析风速、表面气温距平在中国区域气候变化研究中的可信度分析

采用计算标准化均方根误差、相关分析和EOF分解等多种客观分析统计方法,对NCEP/NCAR再分析风速、表面气温距平在中国区域的可信度进行了研究,结果表明中国东部不同要素距平的标准化均方根误差均比西部地区的小,说明NCEP/NCAR再分析资料的可信度东部比西部要高,可能是受到模式地形和中国地面气象站点“东密西疏”分布格局的较大影响。随着高度升高,NCEP再分析风速距平的误差减小,进一步表明地形对NCEP再分析资料的可信度具有较大影响。另外,冬季再分析风速误差较大的特点在850,500和200 hPa等压面上均存在,表明冬季再分析风速距平的可信度受到再分析模式系统误差的较大影响。相关分析结果和标准化均方根误差计算结果之间具有很好的反向对应关系,即均方根误差大,NCEP再分析资料与实测资料的相关性就差,均方根误差小,则对应两者之间的相关性就较好。标准化均方根误差较小的要素,其NCEP再分析和站点实测资料距平EOF分解得到的特征向量空间分布较为相似,各特征向量对应时间系数的相关性也比较好;反之,标准化均方根误差大的要素,其NCEP再分析和站点实测资料距平EOF分解得到的特征向量空间分布则相差较大,对应时间系数的相关性也比较差,因此采用EOF分解方法,分析对应特征向量空间分布相似性及其时间系数变化的一致性,可以对NCEP再分析资料的可信度有一个更加客观的认识。综合上述各季节、各要素多种方法的分析结果,可以发现NCEP再分析风速距平在春、夏、秋季具有一定的可信度,但冬季的可信度较差;表面气温距平则是冬季的可信度最好,夏季的可信度较差。     

A Study on the Dynamic Mechanism of the Formation of Mesoscale Vortex in Col Field

The mesoscale vortex associated with a mesoscale low-level jet(mLLJ) usually causes heavy rainfall in the col field.The col field is defined as a region between two highs and two lows,with the isobaric surface similar to a col.Using a two-dimensional shallow water model,the meso-β scale vortex couplets(MβVCs) induced by eight types of mesoscale wind perturbations in an ideal col field were numerically simulated.With the sizes of ～100 km,the MβVCs induced by northerly perturbation(NP) and southerly perturbation(SP) moved toward the col point.The sizes of MβVCs induced by southwesterly perturbation(SWP),southeasterly perturbation(SEP),northwesterly perturbation(NWP),and northeasterly perturbation(NEP) were relatively small for the perturbations moving toward dilatation axis.The MβVC induced by easterly perturbation(EP) and westerly perturbation(WP) could not develop because they quickly moved away from the col point,before the circulation could form.The size of the circulation was determined by the distance between the vortex and the col point.The closer to the col point the vortex was,the larger the size of vortex.The comparisons of maximum vorticity and vorticity root mean square error(RMSE) of the NP,the SWP,and the WP show that the maximum vorticity and the vorticity RMSE of the NP decreased slower than other perturbations.Therefore,the weak environment of the col field favors the maintenance of vorticity and the formation of vortex.When a mesoscale vortex forms near the col point or moves toward the col point,it may maintain a quasi-stationary state in the stable col field.     

风廓线雷达资料对华南区域模式预报的影响

设计基于GRAPES_Meso的不同试验模拟2014年3月28日-4月8日的广东前汛期降水过程,评估风廓线资料对同化和预报的影响。对资料同化后分析增量的分析表明:相比同化时仅使用自动气象站资料,风廓线雷达资料对1000 hPa到850 hPa纬向风增量均有贡献,在850 hPa,700 hPa高度以上贡献迅速减小。应用3个试验的预报结果计算探空站、风廓线雷达站预报值与观测值的11 d均方根误差发现,同化加入风廓线雷达资料对各预报要素的改善在850 hPa高度最明显,其中风速预报误差显著降低,为0.7 m·s-1。此外,风廓线雷达资料对700 hPa风速预报有一定改善,而在925 hPa高度模拟效果反而降低。通过对2014年3月30日12:00（世界时）的个例分析发现,同化加入风廓线雷达资料的风速预报均方根误差在大雨级别以上的降水落区更大,其原因还有待于进一步研究。     

我国台风高发期东海和南海海区GIIRS/FY-4A温度反演廓线精度研究

基于欧洲中期天气预报中心再分析资料ERA5,对GIIRS/FY-4A温度反演廓线在我国台风高发期东海和南海海区的反演精度进行研究,结果表明:(1)东海海区,无云时GIIRS质量控制0的数据总体RMSE为1.71 K,150～450 hPa高度范围内RMSE小于1 K,450 hPa至近海面RMSE在2 K以内.质量控制...     

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s^?1, considerably smaller than the result (1.29 m s^?1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.     

GTS的温盐资料在BCC_GODAS中的同化结果分析

分析了从GTS(全球无线通讯系统)获得的2002-2007年海洋温盐观测资料在国家气候中心第2代全球海洋资料同化系统(BCC_GODAS 2.0)中的同化结果。与SODA(简易海洋同化数据)资料的比较表明:GTS中的海洋温盐资料同化对模式温盐场的改进之处主要表现在混合层暖区的范围和中心强度、温跃层中温度槽脊的深度、温跃层附近的温度梯度以及盐度高、低值区的范围和中心强度等方面,同化后全球温盐场的均方根误差得到一定程度的降低。挑选位于不同海区的单点温盐廓线与ARGO(地转海洋学实时观测阵)观测作了进一步比较,结果表明:大多数情况下,同化后温盐廓线的均方根误差得到明显降低,模拟的温盐垂向分布特征也更为准确。与TAO(热带大气海洋观测网)资料的比较也同样表明:同化后的温盐场特征会得到一定程度改善。     

风廓线雷达资料对GRAPES_MESO数值预报系统影响的初步研究

针对一个南方切变线系统降水个例,通过观测系统模拟试验(OSSEs)对我国拟建风廓线雷达观测网内的不同类型风廓线雷达观测资料在GRAPES_MESO系统中的影响、对风廓线雷达观测和探空观测及两者混合使用时在GRAPES_MESO系统中的影响差异进行了初步分析和讨论。试验结果表明,在GRAPES_MESO系统中,风廓线雷达资料对500 hPa高度以下水平风速分析场的修正作用明显优于探空资料;风廓线雷达资料对水平风速分析场的影响高度极限大约在300 hPa附近,经过6h的传播,其影响可以向上继续传播至250 hPa以上高度;在300 hPa高度以下,同化对流层Ⅱ型风廓线雷达资料对水平风速分析场的影响比边界层型风廓线雷达资料略大;单独同化风廓线雷达资料对降水预报的贡献较弱,与探空观测混合使用时有助改进降水预报。     

Reliability Analyses of Anomalies of NCEP/NCAR Reanalyzed Wind Speed and Surface Air Temperature in Climate Change Research in China

By means of varied statistical methods,such as normalized root mean square error(RMSE),correlation analysis,empirical orthogonal function(EOF)decomposition,etc.,the reliability of the varied seasonal anomalies of NCEP/NCAR reanalyzed wind speed and surface air temperature(SAT)data frequently used in the climate change research in China is studied.Results show that RMSEs of meteorological variables are smaller in eastern China than in western China,i.e.,the reliability of NCEP/NCAR reanalysis in eastern China is better than that in western China.This could be due to effects of the topography in the reanalysis model and the disposition of"dense-in-eastern-and-sparse-in-western"of meteorological stations in China. The RMSE of anomalies of reanalyzed wind speeds decreases with increasing height,further confirming the possible impact of topography on reliability of reanalysis.Results of correlation analysis inversely correspond to those of RMSE analysis,i.e.,if the RMSE is larger,the correlation between reanalyzed and observed data is worse,and vice versa.It is found from comparing the EOF eigenvectors of anomaly of reanalyzed and observed data that if a meteorological variable has smaller RMSE,the spatial patterns of corresponding EOF eigenvectors of anomaly of reanalyzed and observed data are similar and their time coefficients are significantly correlated,and vice versa.Therefore,the similarity of EOF modes and the consistency of their time coefficients can be used to objectively assess the reliability of the reanalysis.On the whole,the reliability of the reanalyzed wind speed is better in spring,summer,and autumn,but worse in winter;and for the reanalyzed SAT,it is the best in winter and the worst in summer.     

上海市土地利用资料优化方案对WRF模式模拟高温过程的影响

在WRF模拟中,默认的土地利用数据与实际土地利用情况差异较大,因此会影响模式的模拟效果。为此,许多学者提出了更新城市土地利用数据的方案。最简单的方法是仅就城市建成区面积进行修正。但因城市地表具有非均匀性,进而又提出了将建成区进一步精细化分类。然而,在研究土地利用资料对WRF模式影响的文献中,绝大多数研究仅是就某种资料更新前后的模拟效果进行比较,并未将城市面积改变、城市非均匀性这两个因子进行区分。本文综合考虑了面积修正与精细化分类这两个因子,根据面积修正方案和两种精细化方案生成了3种土地利用的优化数据,并结合默认土地数据共设置了4个算例对上海市2018年8月和2019年8月两次高温天气过程进行了模拟,通过对结果进行比较分析发现:1）对WRF土地利用数据进行优化后,改善了温度、相对湿度和风速的模拟效果。2）城市建成区面积是影响温度最关键的因子,面积修正使温度的平均均方根误差（RMSE）降低了0.86°C,在此基础上的精细化分类使平均RMSE最多降低了0.04°C。3）城市的精细化分类是影响风速和相对湿度的主要因子,面积修正使风速的平均RMSE仅降低0.04 m/s,而精细化分类可使其RMSE再进一步降低最多0.19 m/s;面积修正使相对湿度的平均RMSE仅降低0.23%,而精细化分类可使其RMSE再进一步降低最多2.25%。4）总体说来,精细化分类方案在一定程度上考虑了城市的非均匀性,因此对于温度、相对湿度和风速模拟结果的改善程度更大,且分类越细致,效果越好。     

Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia

In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error(RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia(POAMA)-1.5 general circulation model(GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.     

Data-model comparison using fuzzy logic in paleoclimatology

 Until now, most paleoclimate model-data comparisons have been limited to simple statistical evaluation and simple map comparisons. We have applied a new method, based on fuzzy logic, to the comparison of 17 model simulations of the mid-Holocene (6 ka BP) climate with reconstruction of three bioclimatic parameters (mean temperature of the coldest month, MTCO, growing degree-days above 5 °C, GDD5, precipitation minus evapotranspiration, P−E) from pollen and lake-status data over Europe. With this method, no assumption is made about the distribution of the signal and on its error, and both the error bars related to data and to model simulations are taken into account. Data are taken at the drilling sites (not using a gridded interpolation of proxy data) and a varying domain size of comparison enables us to make the best common resolution between observed and simulated maps. For each parameter and each model, we compute a Hagaman distance which gives an objective measure of the goodness of fit between model and data. The results show that there is no systematic order for the three climatic parameters between models. None of the models is able to satisfactorily reproduce the three pollen-derived data. There is larger dispersion in the results for MTCO and P−E than for GDD5. There is also no systematic relationship between model resolution and the Hagaman distance, except for P−E. The more local character of P−E has little chance to be reproduced by a low resolution model, which can explain the inverse relationship between model resolution and Hagaman distance. The results also reveal that most of the models are better at predicting 6 ka climate than the modern climate. Received: 27 May 1998 / Accepted: 8 January 1999     

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.     

High Resolution Deterministic Prediction System (HRDPS) Simulations of Manitoba Lake Breezes

The Effects of Lake Breezes On Weather–Manitoba (ELBOW-MB) field project, conducted around Lakes Manitoba and Winnipeg in July 2013, was the first in-depth field study of lake breezes in Manitoba, Canada. Using observational data collected during ELBOW-MB and output from the 2.5?km Canadian High Resolution Deterministic Prediction System (HRDPS), comparisons were made between HRDPS output and observational data to determine whether the HRDPS can simulate Manitoba lake breezes. The model comparisons considered various lake-breeze characteristics, such as depth, inland penetration distance, and initiation and dissipation time. In addition, cross-sections of lake-breeze circulations were analyzed. The results show that the HRDPS was able to correctly simulate lake breezes, or lack thereof, in 78% of cases on Lake Winnipeg and 68% of cases on Lake Manitoba. Modelled lake-breeze initiation and dissipation times were found to be too early in some cases and too late in others when compared with observations. Overall, it was found that the HRDPS was able to simulate most aspects of lake breezes, although inland penetration distance was one characteristic that the HRDPS was not able to simulate realistically.     

中国地面气象要素格点融合业务产品检验

对2018年5 km分辨率中国地面气象要素2m温度、10 m风速和24 h累积降水格点融合产品进行非独立和独立检验.非独立检验结果表明:(1)相比于站点观测,2m温度格点融合产品整体偏暖,各月平均均方根误差在1 ℃左右,35 ℃以上高温和-20 ℃以下低温天气时均方根误差分别在1 ℃和2 ℃以上.(2)10m风速格点融...     

利用ERA5资料进行桂林地区GNSS水汽反演精度分析

由于桂林地区地基GNSS站并未配置气象传感器，致使大量GNSS观测数据无法在大气水汽（PWV）监测中发挥作用.针对这一情况，本文将欧洲中期天气预报中心（ECMWF）最新发布的ERA5再分析资料中测站处的气压和温度气象数据加入到GNSS水汽反演中，并将反演结果与利用地面气象站反演的GNSS水汽做对比，以此评估ERA5在桂林地区反演GNSS水汽的精度和适用性.结果表明：1）以桂林地区2017年10个地面气象站的实测气压和温度数据为参考值，ERA5地表气压和温度的年均偏差分别为-0.35 hPa和0.86 K，年均均方根误差（RMSE）分别为0.65 hPa和1.66 K，该精度可用于GNSS水汽反演；2）以2017年6—7月GNSS利用地面气象站反演的PWV为参考值，ERA5反演的GNSS PWV的偏差和RMSE分别为0.17 mm和0.35 mm，且两者具较好的相关性和一致性.由此表明，ERA5地表温压产品可应用于桂林地区GNSS水汽反演，这些研究结果可为桂林地区的GNSS水汽反演及数据源的选用提供重要的参考依据.     

从FY-4A卫星遥感数据和GFS资料估算全天空状况下的地表长波辐射通量

本文介绍一种利用FY-4A成像仪遥感数据和全球预报系统(GFS)资料估算全天空地表长波辐射通量的反演方法。该方法通过辐射传输模拟和统计回归计算建立云天地表下行长波辐射通量的反演模式,并基于GFS资料处理云覆盖地区的地表上、下行长波辐射通量。这种全天空状况下两种通量的反演结合了FY-4A晴空地表长波辐射业务产品和本文反演模式处理的云天地表上、下行长波辐射通量。2018年9月1日的处理结果与Aqua/CERES同类产品相对比,精度为:RMSE=20.52 W·m^-2,R=0.9481,Bias=3.3 W·m^-2(夜间地表下行辐射通量对比);RMSE=25.58 W·m^-2,R=0.9096,Bias=5.4 W·m^-2(白天地表下行辐射通量对比);RMSE=10.97 W·m^-2,R=0.9762,Bias=-3.3 W·m^-2(夜间地表上行辐射通量对比);RMSE=19.97 W·m^-2,R=0.9283,Bias=5.0 W·m^-2(白天地表上行辐射通量对比)。这些结果表明本文发展的方法能够反演出精度较好的云天上下行长波辐射通量资料,为今后利用FY-4后续星处理生成全天空状况下的地表长波辐射通量产品奠定了理论基础。     

Accuracy of CM-SAF solar irradiance incident on horizontal surface

The Climate Monitoring Satellite Application Facility (CM-SAF) provides estimates of global solar irradiance incident on horizontal surface at Earth surface. Measurements performed in 2010 at five Romanian meteorological stations are used to test the accuracy of the CM-SAF irradiance data. The dataset contains null solar global irradiance values, which cannot be explained by very large values of the zenith angle neither by overcast sky conditions. Sub-databases have been created. The database Z85 consists of irradiance data, without filtering and processing. The database Z85SIS+ remove all null irradiance values. For a given database, the root mean square error (RMSE) with respect to the ground-based measurements is rather similar for all stations, i.e. around 35 % for Z85 and 24 % for Z85SIS+. On average, the database Z85SIS+ has smaller mean bias error (MBE) than the database Z85, independent of the degree of cloudiness. For the database Z85, MBE (RMSE) ranges, depending on station, between ?9.4 and ?1.2 % (35.3 and 39.1 %). For database Z85SIS+, the MBE (RMSE) ranges, depending on station, between ?4.0 and 0.1 % (23.0 and 29.1 %). On overcast sky, we found for some stations MBE?=??0.1 % and RMSE?=?46.4 % when the database Z85SIS+ has been considered. The accuracy of the database Z85 is lower; we found MBE?=??7.0 % and RMSE?=?58.8 % as extreme cases.     

RPG-HATPRO-G3地基微波辐射计反演产品评估

RPG-HATPRO-G3地基微波辐射计采用“多通道并行测量技术”,性能稳定,反演精度高。应用该微波辐射计和常规L波段探空数据,比较了微波辐射计反演数据与探空测值的差异。结果表明：温度、水汽密度与常规探空资料比较有很好的线性相关性,对仪器维护后可以提高数据质量;相对湿度数据离散度较高。比较无降水时不同高度下微波辐射计反演数据的精度,温度在1 000 m以下低层平均误差和均方根误差较小;水汽密度的平均误差和均方根误差均为近地面较大,随高度而减小;相对湿度的平均误差和均方根误差都明显较大,温度和水汽密度的准确性高于相对湿度。降水时不同高度的温度、水汽密度和相对湿度的平均误差和均方根误差变化趋势均与无降水时相似,但是误差值明显偏大;降水时反演温度在2 000 m以下误差较小,水汽密度在3 000 m以下反演值较探空测值大,相对湿度在降水时的误差较大。