A New Algorithm of Rain Type Classification for GPM Dual-Frequency Precipitation Radar in Summer Tibetan Plateau

In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar(DPR) suitable over the Tibetan Plateau(TP) was proposed by analyzing Global Precipitation Measurement(GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm(simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP,four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 d BZ and 18 d BZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 d BZ to near 35 d BZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15 d BZ to 28 d BZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 d BZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%,followed by weak convective precipitation(25%–40%), and strong convective precipitation has the least proportion(less than 30%).

Operational Evaluation of the Quantitative Precipitation Estimation by a CINRAD-SA Dual Polarization Radar System

In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.

Predicting Precipitation Events Using Gaussian Mixture Model

In this paper, a Gaussian mixture model (GMM) based classifier is described to tell whether precipitation events will happen on a certain day at a certain time from historical meteorological data. The classifier deals with a two-class classification problem where one class represents precipitation events and the other represents non-precipitation events. The concept of ambiguity is introduced to represent cases where weather conditions between the two classes like drizzles, intermittent or overcast are more likely to happen. Six groups of experiments are carried out to evaluate the performance of the classifier using different configurations based on the observation data released by Shanghai Baoshan weather station. Specifically, a typical classification performance of about 75% accuracy, 30% precision and 80% recall is achieved for prediction tasks with a time span of 12 hours.

Feature Analysis and Classification of Particle Data from Two-Dimensional Video Disdrometer

We developed a ground observation system for solid precipitation using two-dimensional video disdrometer (2DVD). Among 16,010 particles observed by the system, around 10% of them were randomly sampled and manually classified into five classes which are snowflake, snowflake-like, intermediate, graupel-like, and graupel. At first, each particle was represented as a vector of 72 features containing fractal dimension and box-count to represent the complexity of particle shape. Feature analysis on the dataset clarified the importance of fractal dimension and box-count features for characterizing particles varying from snowflakes to graupels. On the other hand, performance evaluation of two-class classification by Support Vector Machine (SVM) was conducted. The experimental results revealed that, by selecting only 10 features out of 72, the average accuracy of classifying particles into snowflakes and graupels could reach around 95.4%, which had not been achieved by previous studies.

Detection of Changes on Temperature and Precipitation Features in Istanbul(Turkey)

In this study, the changes in the data of Istanbul’s precipitation and temperature and the features of these changes were analyzed by different methods. In the analyses the daily precipitation and temperature data sets of Florya and Goztepe Meteorological Stations which have similar locational features were used. These sets were recorded between 1960 and 2013 (for 54 years). In order to emphasize the differentiations in the last 15 years the analyses were conducted comparatively both for the 15-year and for the 54-year periods and then the results were evaluated. The changes in the monthly, annual and seasonal quantity, type and frequency of the precipitation in the form of rain and the features of the temperature’s monthly, annual and seasonal changes, the De Martonne aridity index and the Thornthwaite climate classification were carried out. The results showed that during the years from 1999 to 2013 the climate type of Istanbul changed from semi-humid climate to arid and less-humid climate. Most notably the precipitation during the warm periods has decreased, but the frequency of the intense rain has increased and the majority of these episodes of intense rain coincided with the warm periods. Other determinations were the rise in the annual average temperature and the extension of the warm periods in a year. This differentiation of the temperature features can lead to the aggravation of the evaporation and it can be effective for a longer period during the year. Being aware of this differentiation in the features of precipitation and temperature and taking these data into consideration in all sorts of planning and managing strategies have a special importance for the 14 million or more people living in Istanbul.

A Comprehensive Classification of Anomalous Circulation Patterns Responsible for Persistent Precipitation Extremes in South China

Based on observational precipitation at 63 stations in South China and NCEP NCAR reanalysis data during 1951 2010,a cluster analysis is performed to classify large-scale circulation patterns responsible for persistent precipitation extremes（PPEs） that are independent of the influence of tropical cyclones（TCs）.Conceptual schematics depicting configurations among planetary-scale systems at different levels are established for each type.The PPEs free from TCs account for 38.6%of total events,and they tend to occur during April August and October,with the highest frequency observed in June.Corresponding circulation patterns during June August can be mainly categorized into two types,i.e.,summer-Ⅰ type and summer-Ⅱtype.In summer-Ⅰ type,the South Asian high takes the form of a zonal-belt type.The axis of upstream westerly jets is northwest-oriented.At the middle level,the westerly jets at midlatitudes extend zonally.Along the southern edge of the westerly jet,synoptic eddies steer cold air to penetrate southward;the Bay of Bengal（BOB） trough is located to the north;a shallow trough resides over coastal areas of western South China;and an intensified western Pacific subtropical high（WPSH） extends westward.The anomalous moisture is mainly contributed by horizontal advection via southwesterlies around 20°N and southeasterlies from the southern flange of the WPSH.Moisture convergence maximizes in coastal regions of eastern South China,which is the very place recording extreme precipitation.In summer-Ⅱ type,the South Asian high behaves as a western-center type.The BOB trough is much deeper,accompanied by a cyclone to its north;and a lower-level trough appears in northwestern parts of South China.Different to summer-Ⅰ type,moisture transport via southwesterlies is mostly responsible for the anomalous moisture in this type.The moisture convergence zones cover Guangdong,Guangxi,and Hainan,matching well with the areas of flooding.It is these set combinations among different systems at different levels that trigger PPEs in South China.

Diurnal variation of precipitable water vapor over Central and South America

Annual and seasonal diurnal precipitable water vapor(PWV)variations over Central and South America are analyzed for the period 2007-2013.PWV values were obtained from Global Navigation Satellite Systems(GNSS)observations of sixty-nine GNSS tracking stations.Histograms by climate categories show that PWV values for temperate,polar and cold dry climate have a positive skewed distribution and for tropical climates(except for monsoon subtype)show a negative skewed distribution.The diurnal PWV and surface temperatures(T)anomaly datasets are analyzed by using principal components analysis(PCA).The first two modes represent more than 90%of the PWV variability.The first PCA mode of PWV variability shows a maximum amplitude value in the late afternoon few hours later than the respective values for surface temperature(T),therefore the temperature and the surface conditions(to yield evaporation)could be the main agents producing this variability;PWV variability in inland stations are mainly represented by this mode.The second mode of PWV variability shows a maximum amplitude at midnight,a possible explanation of this behavior is the effect of the sea/valley breeze.The coastal and valley stations are affected by this mode in most cases.Finally,the"undefined"stations,surrounded by several water bodies,are mainly affected by the second mode with negative eigenvectors.In the seasonal analysis,both the undefined and valley stations constitute the main cases that show a sea or valley breeze only during some seasons,while the rest of the year they present a behavior according to their temperature and the surface conditions.As a result,the PCA proves to be a useful numerical tool to represent the main sub-daily PWV variabilities.

太行山中南段暖季极端降水的水汽输送特征

利用自动气象站观测降水、ERA5(ECMWF reanalysis version 5)再分析资料和GDAS(Global Data Assimilation System)资料,基于SOMs(self-organizing maps)算法和天气学检验方法,归纳总结2012—2021年太行山中南段75次暖季极端降水事件的环流形势,探讨不同形势下的水汽输送特征及降水差异。结果表明:影响太行山中南段暖季极端降水的环流形势可分为高空槽型、低涡型、副高纬向型、副高经向型和西北气流型5种,其中以高空槽型最为常见,西北气流型最少。低涡型存在孟加拉湾、南海和西北太平洋水汽输送通道,其日降水极值、最大小时降水强度和影响范围在所有类型中均最大,与低涡型相比,高空槽型缺少西北太平洋水汽输送通道,而副高纬向型和副高经向型缺少孟加拉湾水汽输送通道。利用HYSPLIT(hybrid single-particle Lagrangian integrated trajectory)模型追踪气团发现:低涡型和副高纬向型均以来自西北太平洋的水汽输送贡献最大,高空槽型和副高经向型分别以来自黄海沿岸和南海的水汽输送贡献最大。整层水汽收支分析表明:太行山中南段暖季极端降水最主要的水汽流入来自南边界,其他流入边界及各边界水汽流入贡献的相对大小与环流形势有关。

多模式对四川盆地强降水过程的预报性能检验

为进一步认识当前数值预报模式的预报能力,选取2018—2020年发生在四川盆地的47次强降水过程进行分型,再基于多源降水融合产品和地面观测资料,通过TS评分、时空滑动等方法对欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报模式、国家气象中心区域中尺度数值预报模式(China Meteorological Administration Mesoscale Model,CMA_MESO)和西南区域数值预报系统(Southwest Center WRF ADAS Real-time Modeling System,SWC_WARMS)在强降水过程范围、强度、极值、时间和位移偏差等方面的预报能力进行检验评估。结果表明,各模式08:00(北京时,下同)预报优于20:00预报,ECMWF对中雨和大雨预报更优,SWC_WARMS的暴雨量级评分更高。各模式对中雨的预报范围普遍较实况偏大,随着降水量级增大,逐渐转为低估,其中SWC_WARMS更接近实况。对于降水强度,ECMWF和CMA_MESO的平均降水量和极值普遍较实况偏小,SWC_WARMS更接近实况。3种模式时间偏差不明显,仅个别起报时次有-6~3 h的时间偏差;ECMWF的位移偏差最小,纬向上ECMWF和SWC_WARMS以偏北为主,经向上ECMWF以偏西为主,CMA_MESO和SWC_WARMS以偏东为主。

东北冷涡分类及其对内蒙古东部降水的影响

利用1981—2022年6—8月内蒙古119个国家气象观测站逐日降水资料和NCEP/NCAR逐日再分析资料,分析东北冷涡年际、年代际变化特征及东北冷涡分类对内蒙古东部降水的影响。结果表明:东北冷涡发生频次在1992年之前,较常年值显著偏多,之后显著偏少,2003年以后有增多的趋势,近十年发生频次偏多。将东北冷涡分为10类,中涡东北移是致使东部降水偏多最异常的冷涡类型,中涡东移是次异常的冷涡类型。东亚地区呈现“北高南低”环流型,内蒙古东部位于负位势高度距平区,中心位于东部偏南地区,乌拉尔山和鄂霍次克海阻塞高压偏强,副热带高压偏强,内蒙古东部地区受气旋性环流控制,低层水汽通量辐合,配合越赤道气流共同作用引导的西太平洋水汽输送到东部地区,是东部降水异常偏多的主要环流配置型。

基于CRA技术对杭嘉湖地区梅汛期强降水过程的预报检验

本文根据24 h雨带移动,将杭嘉湖地区梅雨过程分为东移型、发展(北抬)型、少动型和南压型。基于CRA(contiguous rain area)技术对2020年梅汛期EC(european centre for medium-range weather forecasts)模式预报强降水过程进行检验,结果显示:误差主要为位移误差和形态误差,其中东移型和南压型位移误差较大,发展型和少动型以形态误差为主;模式对强降雨预报偏北偏东,以发展型和南压型更明显;预报落区偏差在1.5º内,占比69.2%;落区偏差大于1º时,预报纬向偏差明显,除少动型外,其他三型预报质心均偏东;预报强降雨范围及总雨量与实况相比都偏大;但预报平均雨强60 mm以上和200 mm以上雨量极值时,偏小;预报最大雨量时,发展型偏大,东移型和少动型偏小。对EC、NCEP-GFS模式(national centers for environmental predictionglobal forecast system)和OCF集成预报(optimal consensus forecast)的检验结果:强降雨范围预报总体都偏大;总雨量预报EC和OCF偏大;平均雨强EC和OCF预报与实况相近,NCEP-GFS预报偏小;最大雨量预报都偏小,但高于100 mm时,EC和OCF偏小不明显。

贵州省汛期降水特征及强降水过程分型研究

【目的】为探究贵州省汛期降水的时空分布特征及演变规律。【方法】利用贵州省81个气象观测站1981—2020年汛期降水资料,采用EOF、REOF及交叉小波分析等方法对贵州省汛期降水时空特征进行分析及强降水过程分型研究。【结果】贵州省1981—2020年汛期平均降水量为924.9 mm,降水量在682.7~1194.1 mm,呈显著上升趋势,上升速率为16.94 mm/10 a。贵州汛期降水大体上呈现西南向东北递减的趋势,强降水过程次数及持续天数分布及波动变化与汛期降水基本一致。【结论】贵州省汛期降水分布不均,具有显著的年代际变化。贵州省汛期强降水空间场主要有全省一致型、东西反向型和南北反向型3种典型模态。经REOF方法可将贵州省细分为3个强降水区域,根据环流场分析,又可进一步划分为东部型强降水(Ⅰ型和Ⅲ型)与西部型强降水(Ⅱ型),各类型强降水落区受500 hPa环流分布情况以及850 hPa水汽来源与强度的影响。

海陀山冬季降水天气分型及冬奥预报应用

基于2019—2021年1月1日至3月15日北京冬奥会延庆赛区(以下简称海陀山)降水观测资料和ERA5再分析资料,对期间34次降水过程进行天气分型,并对各天气型下不同海拔的降水实况特征开展统计分析。研究结果表明:冬季海陀山降水根据天气系统及地形影响可分为偏北气流型、偏东气流型、低涡低槽型、回流低涡低槽型四种天气型。不同天气型下海陀山地形高度以下主要气流方向和强度、水汽垂直分布等条件,以及与地形相互作用使得不同海拔之间降水量、持续时间等呈现显著差异。偏北气流型受500 hPa槽后整层强偏北气流控制,形成越山气流,降水集中在高海拔地区;偏东气流型受低层偏东气流影响,降水集中在低海拔地区,以上两种天气型无天气尺度系统配合,由地形强迫作用主导,降水量不大、持续时间相对较短。低涡低槽型受高空东移低涡低槽作用,配合低层西南气流,高海拔降水量更多,同时该型也是海陀山冬季最主要的降水天气型;回流低涡低槽型受高空东移低涡低槽影响,配合降水前东风回流对低层增湿并起到冷垫作用,低海拔降水量更多,以上两种天气型均存在天气尺度系统,并叠加海陀山地形作用,降水量显著且持续时间长,会对赛事运行造成较大影响。上述特征统计结果在2022年北京冬奥会期间一次强降雪预报服务中得到验证和应用,证明上述结果可以在冬季海陀山复杂地形降水预报中发挥作用。

基于雨滴谱参数反演的C波段双偏振雷达降水类型分类方法

降水类型分类对分析区域降水微物理特征、多源降水融合误差模型的构建以及雷达定量测量降水估计等都很重要。本文基于2015~2016年南京信息工程大学C波段双偏振雷达数据和南京地区滴谱仪观测资料,提出一种适用于南京地区的雷达降水类型分类方法,并对降水类型分类结果进行对比验证。首先,基于滴谱仪降雨率时序数据、地基雷达反射率因子平面位置显示数据和地基雷达反射率因子时间—高度显示数据,筛选出36次典型层状和对流降水过程。随后,统计3个滴谱仪站点典型层状(对流)降水的雨滴谱(DSD)参数,拟合得到适用于南京地区的降水类型分类线。将基于滴谱数据统计拟合的分类线应用于基于变分法反演的地基雷达DSD参数,进行地基雷达降水类型分类。根据典型层状(对流)过程降水类型分离指数的时间—高度分布,并对比星载双频测雨雷达(DPR)降水分类产品,对分类效果进行验证。最后,将分类结果应用于雷达分类定量降水估计,进一步说明降水分类的应用效果。结果表明,南京地区3个滴谱仪站点的拟合分类线非常一致,3个站点的典型层状(对流)过程均能够很好地分离在分类线两侧;与DPR降水分类产品进行对比分析,发现南京地区分类线的分类效果相对于其他典型降水分类方法,对层状和对流降水的识别率整体最高,分别为84.56%和72.64%;基于降水分类的雷达定量降水估计的测雨精度均优于未分类的测雨公式,且基于差分传播相移率的测雨公式[R(KDP)]在四种分类测雨公式中整体性能最优,基于水平反射率因子的测雨公式[R(ZH)]在层状云降水反演中性能最优,基于差分传播相移率的测雨公式[R(KDP)]在对流云降水反演中性能最优,基于水平反射率因子和差分反射率的测雨公式[R(ZH,ZDR)]对原有总体测雨公式降水精度的提升最为明显。

山地雷达估算降水的反射率因子订正方法研究

我国很多新一代多普勒天气雷达位于地形复杂的山区,低仰角地形遮挡问题突出,遮挡区雷达估算降水时需要采用更高仰角的观测数据,而由于降水粒子下落过程中的微物理变化和水平运动,同一地点高处的反射率因子常与近地表处有很大的差异,直接用于估算地表降水会增大估算误差。本文提出一种反射率因子垂直订正方法,首先建立无遮挡观测区不同降水类型的雷达反射率因子垂直廓线(VPR),然后依据廓线的垂直变化特征,确定待订正的高度阈值和近地表目标高度,并将待订正高度以上的观测值订正到目标高度处。对比检验结果表明:经过订正后的目标反射率因子数据与实际观测数据值差异减少,一致性提高;而且,考虑遮挡因素使VPR低处的数据更准确;由于不同降水类型VPR差别明显,区分降水类型能避免误订正。本订正方法不仅适用于波束遮挡区,也普遍适用于远距离处波束较高时观测数据的订正。

华南快速循环同化模式在湖南不同环流型下的小时降水预报性能检验

利用T-mode斜交旋转主成分分析法,对湖南2021年汛期(4—9月)逐小时850 hPa风场进行环流分型,在此基础上开展同期华南快速循环同化模式(CMA-GD-R3)小时降水预报性能检验。结果表明:影响湖南2021年汛期的主要环流型为西南涡切变型、切变型、副热带高压边缘南风型和台风外围东风型4类;模式小时降水预报的晴雨准确率和分级降水TS评分日变化特征明显,晴雨准确率夜间高于白天,分级降水TS评分峰值出现在早晨,各环流型的临近时效降水预报效果较好,短时强降水发生频次最高的西南涡切变型晴雨准确率较低,副热带高压边缘南风型在较大量级降水表现相对较差;SAL(structure amplitude and location)检验显示,西南涡切变型、切变型过程模式位置预报较接近实况,强度预报表现为前弱后强,副热带高压边缘南风型过程预报落区分散,位置预报不稳定,整体强度较实况明显偏弱,台风外围东风型过程在短时预报时效落区接近实况,强度预报显著偏弱,该方法能较客观地反映模式降水预报空间偏差。

安徽地区暖季降水回波日变化及环境场特征研究

安徽地处华东,地势复杂且受夏季风影响,通常于暖季(5-8月)发生频繁且复杂的降水过程。为加深安徽地区降水特征及降水环境场特征的理解,利用安徽7部S-Band新一代天气雷达和ERA5再分析数据,对安徽及周边地区2016-2019年暖季降水回波时空变化及相关环境特征进行分析。结果表明:(1)安徽地区暖季降水回波发生频率的日变化呈双峰结构,降水多发时段为06时和16时前后。(2)凌晨降水峰值主要出现在6月,午后降水峰值主要出现在7、8月。(3)6月降水主要分布在皖南地区,由大面积浅对流及层状云构成。7、8月降水空间分布零散,无明显地域特征,多为局地深对流导致。(4)利用ERA5数据分析暖季降水峰值时刻的环境特征发现,有利于对流发生的热力条件主要出现在7、8月的午后,而5、6月的凌晨具有更有利的动力和水汽条件。各月降水峰值时刻的回波特征与环境场的特征存在很好的一致性。

驻马店市2016—2020年暖季极端短时强降水天气分型与物理量特征

利用常规高空、地面气象观测资料和ERA5再分析资料,分析了2016—2020年暖季(4—9月)驻马店市极端短时强降水的时空变化特征,并对其进行天气背景分型与物理量特征诊断。结果表明:极端短时强降水多发在7月到8月,日变化呈三峰型,在每日16—18时、00时、05—07时较多发,而小时雨强80 mm/h以上的极端短时强降水多发在00时,且在驻马店市中东部的平原地区较为多发。根据主观分型和K均值聚类客观分型相结合的方法,将驻马店市极端短时强降水分为低槽低涡切变型、副高影响型、地面冷锋型、西北气流型和台风倒槽型5种,雨强80 mm/h以上的极端短时强降水发生在前3种天气型下。对每种天气型的物理量诊断结果表明,低槽低涡切变型低层水汽通量最大,地面冷锋型环境场高湿,而西北气流型层结偏干,只有近地面露点相对较大。各天气型均有一定的不稳定能量。低槽低涡切变型整层动力条件均最强,地面冷锋型的低层动力条件较强,而其余3型的动力条件相对较弱。各型0℃层高度除低槽低涡切变型外,其余各天气型平均值均在5.4 km以上。将本研究统计的极端短时强降水物理量平均值与张一平等统计的淮河上游地区短时强降水的物理量平均值进行了对比,除CAPE偏高1倍较为明显外,其余物理量差异均不明显。

广东两次飑线过程的微物理特征分析研究

基于中国气象局龙门云物理野外科学试验基地2DVD(Two-Dimensional Video Disdrometer)雨滴谱观测资料,分析广东地区2017年5月4日(槽前型飑线)和2017年8月22日(东风型飑线)两次不同飑线系统不同降水类型的雨滴谱特征。根据雨强和雷达反射率随时间变化将降水分成对流降水和层云降水,同时以20 mm/h为阈值将对流降水划分为对流前沿、对流中心和对流后沿。结果表明,两次飑线系统在不同降水时期的微物理特征参数变化有所差异。槽前型飑线过程中,对流降水的粒子分布较为分散,中等粒径的粒子比重较高,且对流区前半部分粒子尺寸大于“大陆性”对流特征,后半部分粒子尺寸小于“海洋性”对流特征;层云降水的粒子分布较为集中,小粒径粒子居多。而东风型飑线整个降水时期基本上是由高浓度中小粒径粒子组成,降水粒子粒径分布较为集中,对流降水粒子介于“海洋性”和“大陆性”对流区之间。

东北地区月平均大气环流型与哈尔滨气候关系的初步研究

利用基于英国Lamb(1950年)发展的大气环流分型方法的Jenkinson(1977年)法对东北地区1951—2002年的月平均海平面气压场(MSLP)进行环流分型。由月平均海平面气压场算出6个环流指数,并由此划分出27种环流类型,分析了其中出现频率最高的5种主要环流类型(N,NW,C,CSW,SW)在不同时间尺度下的变化规律及它们与哈尔滨月平均温度的关系,利用逐步回归方法得到了温度距平的拟合曲线。给出了各种环流类型的月平均降水量和与哈尔滨降水密切相关的C,CSW和SW 3种环流类型对应的平均海平面气压合成图。结果表明:哈尔滨冬季以N,NW型为主,夏季以C,CSW和SW型为主。出现N和NW型时气温偏低,降水偏少;而出现C,CSW和SW型时气温较高,降水偏多。用6个环流指数中的地转风V和大尺度平均温度t可以建立其与温度距平之间的一个统计模式,利用此模式,能解释哈尔滨1951—2002年温度变化方差的77.3%。C,CSW和SW 3种环流类型为哈尔滨的主要降水类型,C型与哈尔滨总降水的相关关系很好,并且近20年来哈尔滨主要以C型降水为主。这种研究大尺度大气环流与区域气候变量—温度、降水之间关系的方法是一种统计降尺度(statistical downscaling)方法,可以用于区域气候预测。