达州市2016年元旦期间持续重污染天气气象条件分析

利用空气质量监测资料、高空和地面气象观测资料、NCEP再分析资料,对达州市2016年元旦节期间重污染天气过程特征及气象条件进行分析。结果表明:达州市此次重污染天气过程为长时间无冷空气活动,无降雨,大气污染物不断积聚形成。AQI日变化受污染源排放情况影响更大,早上低,白天逐渐增加,天黑后达到峰值。大气污染物的积累一般发生大气稳定度为中性或以上。AQI与08时和17时混合层厚度负相关,但日平均混合层厚度与AQI没有通过相关性检验。重污染时近地面有逆温层且逆温层较厚。AQI与逐日最高气温、日平均风速和日最大风速正相关,降雨对大气污染物稀释作用明显,特别是降雨持续时间长,雨量大效果更为显著。AQI逐时变化与温度正相关,与风速负相关。      

基于两种数据传输途径的国家级地面自动站逐小时数据差异分析

对2006-2009年两种途径（A文件和Z文件）上传的2400多国家级自动站观测的小时常规六要素资料进行了对比分析。结果表明：两种途径得到的小时数据中,2 min风和10 min风的不一致率较高,主要由两种文件对于风速为零时风向的规定不同造成,其他要素的不一致率相对较低;在不一致数据中,各要素在仪器误差允许范围内的比率分别为气温23.27 %、气压5.4 %、相对湿度23.19 %、降水65.42 %、2 min风12.93 %、10 min风14.16 %。     

Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature

In this study, the ability of two models of multi linear regression (MLR) and Levenberg–Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash–Sutcliffe efficiency coefficient $ \left( {\left| {{\text{Log}}({\text{NS}})} \right|} \right) $ were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.     

Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada

Summary A regression-based methodology was used to downscale hourly and daily station-scale meteorological variables from outputs of large-scale general circulation models (GCMs). Meteorological variables include air temperature, dew point, and west–east and south–north wind velocities at the surface and three upper atmospheric levels (925, 850, and 500 hPa), as well as mean sea-level air pressure and total cloud cover. Different regression methods were used to construct downscaling transfer functions for different weather variables. Multiple stepwise regression analysis was used for all weather variables, except total cloud cover. Cumulative logit regression was employed for analysis of cloud cover, since cloud cover is an ordered categorical data format. For both regression procedures, to avoid multicollinearity between explanatory variables, principal components analysis was used to convert inter-correlated weather variables into uncorrelated principal components that were used as predictors. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response; for example, most hourly downscaling transfer functions could explain over 95% of the total variance for several variables (e.g. surface air temperature, dew point, and air pressure). Downscaling transfer functions were validated using a cross-validation scheme, and it was concluded that the functions for all weather variables used in the study are reliable. Performance of the downscaling method was also evaluated by comparing data distributions and extreme weather characteristics of downscaled GCM historical runs and observations during the period 1961–2000. The results showed that data distributions of downscaled GCM historical runs for all weather variables are significantly similar to those of observations. In addition, extreme characteristics of the downscaled meteorological variables (e.g. temperature, dew point, air pressure, and total cloud cover) were examined. Authors’ addresses: Chad Shouquan Cheng, Guilong Li, Qian Li, Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch-Ontario, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, Canada M3H 5T4; Heather Auld, Adaptation and Impacts Research Division, MSC Branch, Environment Canada, Toronto, Canada.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

一种简易恒温加热防冻结装置在三穗国家基准气候站的实践应用

低海拔地区冬季由于凝冻常常会使风向风速传感器冻结,造成风要素资料失真或监测数据缺测,影响数据可用性,造成气象数据历史延续空白,导致无法弥补的历史数据损失。该文利用三穗县国家基准气候站2008—2020年冬季逐日、逐时、逐分钟风向风速、气温、相对湿度以及降水等地面气象监测资料,详细分析冬季冰冻期间风向风速缺测时段的气温、相对湿度、降水等相关信息,找出风向风速冻结与气温、相对湿度、风速大小的对应关系,从而判定风向、风速易冻结的气象要素阈值,采用USB碳纤维发热片,科学搭建低压加热方式,确保风向、风速传感器设备加热时不被灼伤受损,ZQZ-TF风向、风速监测资料连续可靠。     

2011~2013年中国冻雨、冻毛毛雨和冻雾的特征分析

研究冰冻天气的特征对于更好的认识冰冻天气,提高此类天气的预警预报能力,从而达到防灾减灾有重要意义。利用2011~2013年间中国民航机场的一小时或半小时一次的例行观测和特殊观测资料,分析了冻雨、冻毛毛雨和冻雾天气的时空分布、持续时间和气象条件等特征。研究结果表明:我国大部分地区均会发生冰冻天气。冻雨和冻毛毛雨的频发区与其持续时间较长区比较一致,而冻雾的少发区,其持续时间也可能较长。我国冰冻天气最容易在冬季(12~2月)发生,在1月出现的比例最高;在凌晨[00~07时(当地时间,下同)]出现频率相对较高,午后(13~14时)出现频率最低。一次冰冻天气的持续时间一般不超过2个小时。温度在-3~-1℃,露点温度在-4~-1℃,温度露点差在0~1℃时冰冻天气发生的频率最大。出现冰冻天气时的风向以东北风和北风为主,而平均风速在2~3.9 m s-1时冻雨和冻毛毛雨发生的频率最大。我国冻雨和冻毛毛雨发生时,同时常会有雾或轻雾;冻雾出现时,一般不会伴随其他天气。     

我国北方沙尘天气演变趋势及其气候成因分析

利用地面气象观测资料,分析了我国北方1954～2001年年、季沙尘天气发生日数的演变规律及其与主要气候要素,风速、相对湿度、降水、气温和干燥度的相关关系,用NCEP/NCAR再分析资料分析了冬春气压梯度的变化趋势。结果表明:近50年来,造成我国北方沙尘天气频率显著下降的直接自然原因是沙尘源区和发生区平均风速和大风日数的减少、主要沙尘源区降水量特别是春季及其前冬降水量的增加以及由于源区降水增加引起的大气和土壤湿润程度的改善。冬春季节气压梯度的减小是风速减小、进而导致沙尘日数减小的关键间接因素。     

Measurement and modelling of dew in island,coastal and alpine areas

We compare the characteristics of dew at nearly the same latitude (42–45°N) for the Mediterranean island of Corsica (Ajaccio, France) and two continental locations (Bordeaux, France, Atlantic coastal area; Grenoble, France, alpine valley). Dew amount was measured on a horizontal reference plate made of polymethylmethacrylate (PMMA) and placed at 1 m above the ground. Data are correlated with plate and air temperature, air relative humidity, wind speed and cloud cover during the period from 14-08-1999 to 15-01-2003.General features as well as particularities of the sampling sites are discussed. The average daily dew yield is higher for the island station at Ajaccio (0.070 mm) than the Bordeaux coastal area (0.046 mm) or the Grenoble valley (0.036 mm). However, the accumulated dew yield was highest for the coastal station (9.8 mm/year) as compared to the island (8.4 mm/year), and much larger than in the alpine valley (4 mm/year). The difference between cumulated and average dew yield stems from the greater number of dew days in the coastal area (58%) versus 33% for the island and 30% in the valley. The higher wind speeds at the island station (average wind during dew is 2 m/s) and lower relative humidity explain the smaller number of dew days. The dew rate seasonal variation is negligible in Bordeaux and exhibits during summer a maximum in Ajaccio and a minimum in Grenoble.A computer model that includes simple meteorological data (air temperature and relative humidity, wind speed, cloud cover) is used to determine the thermal balance and fit to dew mass evolution. Two parameters that account for heat and mass exchange can be adjusted. It was found that, within the uncertainties, these two numbers are the same for the two continental sites, thus allowing dew formation on plates to be evaluated from only simple meteorological measurements. Somewhat larger values are found for the island, due to limitations in the model, which are discussed.     

黑龙江省草地螟第三暴发周期气候特征及风险概率

1996年以来,黑龙江省草地螟第三暴发周期持续至今,危害日益加重。以1959—2008年黑龙江省28个国家基本站气象资料以及草地螟暴发期等资料为基础,分析了黑龙江省草地第三暴发周期的气候特征及风险概率。结果表明：近50 a来,黑龙江省年平均气温、年平均最低气温呈显著的上升趋势,通过0.001显著性检验,气温在1988年前后出现突变。其他要素如降水量、降水日数、风速和日照时数均呈减少趋势,但只有年日照时数和年平均风速的减少趋势通过了显著性检验。哈尔滨草地螟始发日期与同期平均气温、日最高气温、最低气温、最小相对湿度关系密切,本地越冬虫源春季羽化对气温、相对湿度敏感性较高;同时,始见日期与最大风速的风向相关较为密切,可能是由于外地虫源对风向敏感性较高。通过对黑龙江省草地螟高峰期首日的风险概率分析,得出草地螟高峰期出现在5月最后一个候的风险概率为23.6%;出现在6月上旬的风险概率为36.6%;出现在6月中旬的风险概率为24.0%;出现在6月下旬的风险概率为14.4%。     

SVM方法在热带气旋风雨及温度预报中的应用

热带气旋的强弱和移动路径会直接影响到周围大气中气压、温度、露点等气象要素的变化.为更好地了解热带气旋对海口市的影响,通过收集影响海口市热带气旋关键因子,建立热带气旋风雨影响预报因子库,基于SVM方法对热带气旋在过程降水量、最大风速和平均温度进行趋势预报.结果表明,该方法对影响海口市热带气旋的过程降水量、最大风速和平均温度都有较好的预测效果,但对于超过15 m/s的最大风速和200 mm以上降水量级上存在一定的偏差,这可能与SVM模式中预报因子库中关键因子不全及模式的择中原理使结果趋于平均化相关.     

2015—2017年唐山市PM2.5重污染生消气象条件分析

利用2015—2017年唐山市空气质量日空气质量指数、小时PM_2.5浓度和气象数据,分析了唐山市重污染特征及PM_2.5重污染生成、消散气象条件。结果表明：2015—2017年唐山市重污染天数为减少趋势,年平均重污染天数36 d。冬季发生重污染天数最多,秋季次之。重污染天气中首要污染物为PM_2.5、PM₁₀和O₃,PM_2.5为首要污染物占比87%,PM₁₀占比6%,O₃占比7%。小时PM_2.5浓度与相对湿度、总云量、24 h变温正相关,与风速、气温、风向、1 h降水负相关。冬季相关性最好,其次是秋季和春季。90%PM_2.5重污染相对湿度均为50%以上,冬季和秋季高达98%;风速大于4 m·s^-1时,有0.7%的PM_2.5达到重污染;降水对PM_2.5有一定清除作用。升温、湿度增加和负变压有助于污染天气形成,生成过程中平均风速为1.8 m·s^-1,主导风向为SW,其次是S、W。降温、湿度下降、正变压、降水有助于污染天气消散,消散过程中平均风速为3.1 m·s^-1,主导风向为E,其次是NE、N。各方位3 m·s^-1的风具有清除能力,偏北风具有较好清除能力,风速较其他方向风速小。     

甘肃崆峒站新旧址对比观测资料分析

为了解甘肃崆峒国家基本气象站新旧址气象要素对比观测数据差异,选用2016年1～12月新站和旧站气温、气压、相对湿度、风向风速、降水量等要素资料进行对比分析,重点利用均值差异、相关与回归、显著性检验等统计方法分析了新旧测站气温差异及产生原因,结果表明：新址气温、气压均低于旧址,风速高于旧址,降水量、相对湿度差异大,变化无规律,其产生的主要原因是地理位置、海拔高度及观测场周边环境的不同造成;新址与旧址气温存在高度正相关,两站气温差异不显著,可以合并计算,研究结果可为资料使用者提供订正的依据。     

新都区冬季空气污染扩散与气象要素相关性研究

利用2014~2018年冬季空气质量和污染物浓度数据,结合地面观测、探空及风廓线雷达资料,对新都区冬季气象要素及其污染扩散条件进行分析。结果表明:（1）新都区不同污染物具有相同的日变化特征,在11时左右浓度最高,18时达到最低。（2）新都区污染物浓度与风速、气温、降水、相对湿度有密切关系。当风速大于(小于)平均风速时,污染物浓度减小（增加）;气温越高且相对湿度越大,污染也越强;降水较弱时,反而会加重污染。（3）新都区污染天气过程中,逆温强度与厚度的大小将影响污染物的垂直扩散,强度和厚度偏大,污染偏严重。      

2022北京冬季奥运会冬季两项赛场山谷风现象初探

基于2019年1—3月张家口站探空资料与张家口市崇礼区B1638、B1640区域自动站的每小时2分钟平均风向、2分钟平均风速、整点气温及系留气艇探空资料分析第24届冬奥会冬季两项赛场的山谷风特征，为冬奥会天气预报提供参考。结果表明：环境风场较弱时，冬季两项赛场中存在山谷风现象，白天多上坡风及上谷风，夜间多下坡风及下谷风；山谷风系统一天具有两次风向的转变，下谷风转上谷风一般在日出后，而上谷风转下谷风一般在日落后；山谷风系统强度较弱，具有明显日变化，白天偏大而夜间偏小，并且受盛行西风影响明显；风向转变时，会伴随剧烈的气温升降，其原因与冷湖结构密切相关；对山谷风的预报需要综合考虑环境风场强弱、风向的转换时间、风速的分布、风向转换时气温的变化等。     

自动与人工观测数据的差异

该文概述了造成自动观测与人工观测数据差异的各种原因，其中包括仪器的测量原理与观测方法不同，观测时间和空间不同，采样方式与算法不同，观测时次不同等等。通过对比分析基本气象要素，如气压、气温、地温、风向风速、降水、湿度等的两种观测数据，认为自动气象站的观测结果更接近大气中的实际情况。自动站对气压、气温和风向风速的观测有明显的优势，但在雨量累计量的测量和高温高湿下的湿度测量效果不理想。     

Variations in Some Climatological Characteristics at the Aerodromes of the Russian Federation in 2001–2015

The variations in several climatological characteristics are studied on the basis of hourly (half-hourly) meteorological terminal observations at 51 aerodromes of the Russian Federation in 2001–2015. For every aerodrome extreme temperature, wind speed and gusts, and QNH are analyzed for the above period. Using data for three consecutive 5-year periods, variations in the number of days with temperature above 30°C or below -30°C, with wind speed of ≥10 m/s and gusts of ≥15 m/s are considered. The occurrence frequency of significant weather events affecting the takeoff and landing (fog, blizzard, freezing precipitation, thunderstorm) is investigated. The results for aerodromes with positive or negative trends in the occurrence frequency of weather phenomena in 2001–2015 are presented.     

2012—2015年北京市空气质量指数变化及其与气象要素的关系

根据2012—2015年的空气质量指数（AQI）日报数据与同时段的气象数据,采用统计方法和广义加性模型（GAM）对空气质量指数的时间变化及其与气象要素的关系进行了分析,结果表明：2012—2015年北京市空气质量整体呈现下降趋势,冬春季空气质量较差,夏秋季的较好,冬季容易产生重污染天气,春季污染天气频发。北京空气质量存在一定程度的周末效应,表现为周末空气质量较差,工作日相对较好。整体上空气质量指数与风速、日照时数、降水量、平均气温和最高气温呈负相关,与湿度呈正相关,不同季节和不同级别空气质量下的AQI与气象要素相关性差异较大。通过广义加性模型得到AQI与降水量呈线性关系而与其他气象要素均呈非线性关系,气象要素在不同数值范围内对AQI的影响趋势和程度存在显著差异。     

AW11型自动气象站的原理及应用

本文介绍一种新型的自动气象站，它是针对小机场、直升机场和无人值守的飞机跑道而设计的独立天气观测和报告系统，可以测量全部标准的航空天气参数和生成精确实时的天气报告。     

探测环境对北京气象站气温观测值的影响分析

为深入了解气象探测环境对气温观测数据的影响,利用2017年北京市观象台（54511）与南海子站（A1274）逐小时地面气象要素数据,分析两站气温差异以及因站点探测环境导致的日照、风速和降水对两站气温差异的影响。结果表明：2017年两站气温差异较明显,年平均气温54511站比A1274站高0.75℃;两站逐月平均气温54511站全年高于A1274站,两站差值7月最低为0.60℃,9月最高为1.09℃;两站平均日最高气温较接近,平均日最低气温差异较大,54511站较A1274站高1.24℃;两站气温的日变化特征相似,呈单峰分布,54511站气温日较差低于A1274站。两站小时气温差值随着日照时长和强度的增加而增加,短波辐射效应最强的10-14时和长波辐射效应最强的19-23时两站气温差值与当日白天直接辐射曝辐量的相关系数分别为0.459和0.601;水平风速对两站气温差值的影响较大。水平风速超过5 m·s^-1时,两站气温差小于0.1℃;当水平风速不超过1 m·s^-1时,两站观测气温差值达到1.28℃;降水天气下两站的气温差值小于非降水天气,出现降水时次54511站平均气温仅比A1274站高0.2℃。两站相距4.3 km,气候均一,测站周边2 km范围内建设用地占比54511站比A1274站高约30%,植被占比低28%,水体占比相差不大。另外,54511站附近的五环路具有低反射率和高热容的特征,白天能够吸收太阳辐射储存较多的热量,这些热量在夜间释放,可能是两站探测环境对太阳辐射吸收的差异决定了两站温差受太阳辐射和风速的影响较大,而受降水影响较小。