共查询到20条相似文献，搜索用时 31 毫秒
1.
Important meteorological variables for statistical long-term air quality prediction in eastern China
Weather is an important factor for air quality. While there have been increasing attentions to long-term (monthly and seasonal) air pollution such as regional hazes from land-clearing fires during El Niño, the weather-air quality relationships are much less understood at long-term than short-term (daily and weekly) scales. This study is aimed to fill this gap through analyzing correlations between meteorological variables and air quality at various timescales. A regional correlation scale was defined to measure the longest time with significant correlations at a substantial large number of sites. The air quality index (API) and five meteorological variables during 2001–2012 at 40 eastern China sites were used. The results indicate that the API is correlated to precipitation negatively and air temperature positively across eastern China, and to wind, relative humidity and air pressure with spatially varied signs. The major areas with significant correlations vary with meteorological variables. The correlations are significant not only at short-term but also at long-term scales, and the important variables are different between the two types of scales. The concurrent regional correlation scales reach seasonal at p < 0.05 and monthly at p < 0.001 for wind speed and monthly at p < 0.01 for air temperature and relative humidity. Precipitation, which was found to be the most important variable for short-term air quality conditions, and air pressure are not important for long-term air quality. The lagged correlations are much smaller in magnitude than the concurrent correlations and their regional correction scales are at long term only for wind speed and relative humidity. It is concluded that wind speed should be considered as a primary predictor for statistical prediction of long-term air quality in a large region over eastern China. Relative humidity and temperature are also useful predictors but at less significant levels. 相似文献
2.
Prediction of soil temperature using regression and artificial neural network models 总被引：3，自引：0，他引：3
Mehmet Bilgili 《Meteorology and Atmospheric Physics》2010,110(1-2):59-70
In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献
3.
The present study investigates meteorological conditions for the day-to-day changes of particulate matter (PM) concentration in Beijing city during the period 2008–2015. The local relationship of PM concentration to surface air temperature, pressure, wind speed, and relative humidity displays seasonal changes and year-to-year variations. The average correlation coefficient with PM10 in spring, summer, fall, and winter is 0.45, 0.40, 0.38, and 0.30 for air temperature; –0.45, –0.05, –0.40, and –0.45 for pressure; 0.13, 0.04, 0.53, and 0.50 for relative humidity; and –0.18, –0.11, –0.45, and –0.33 for wind speed. A higher correlation with wind speed is obtained when wind speed leads by half a day. The heavily polluted and clean days, which are defined as the top and bottom 10% of the PM values, show obvious differences in the regional distribution of air temperature, pressure, and wind. Polluted days correspond to higher air temperature in all the four seasons, lower sea level pressure and anomalous southerly winds to the south and east of Beijing in spring, fall, and winter, and a northwest–southeast contrast in the pressure anomaly and anomalous southerly winds in summer. Higher relative humidity is observed on polluted days in fall and winter. The polluted days are preceded by an anomalous cyclone moving from the northwest, accompanied by lower pressure and higher air temperature, in all four seasons. This feature indicates the impacts of moving weather systems on local meteorological conditions for day-to-day air quality changes in Beijing. 相似文献
4.
Abdulkadir Yasar Erdo?an Simsek Mehmet Bilgili Ahmet Yucel Ilhami Ilhan 《Meteorology and Atmospheric Physics》2012,115(1-2):81-87
The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed. 相似文献
5.
Comparative analysis of regression and artificial neural network models for wind speed prediction 总被引：1，自引：1，他引：0
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献
6.
7.
利用相关统计法分析2006—2008年厦门市地面气象要素对空气污染指数变化的影响规律。结果表明：风速、气温、降水、相对湿度和水汽压对空气污染指数有显著负效应作用,气压起显著正效应作用;风向影响较为复杂, NNW-N-E-ESE风起正效应作用, SE-S-W风起负效应作用,厦门风向总体不利于空气质量提高,全年仅夏季盛行SE-S-W风。API指数与风向、气温、降水、气压、相对湿度和水汽压的季节变化规律有高相关性。API指数与气象要素中的水汽压关系最相关,其次是气压。 相似文献
8.
Mohammad Zounemat-Kermani 《Meteorology and Atmospheric Physics》2012,117(3-4):181-192
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. 相似文献
9.
洱海盆地水面与地面气象要素变化特征的比较 总被引：1，自引：0，他引：1
依据大理国家气候观象台在洱海湖中建立的自动观测系统以及大理国家基本气象站观测资料,对2008至2009年的风、温、湿、压、降水要素的日变化和季节变化特征进行了对比分析。结果表明：受局地复杂地形和洱海的影响,洱海盆地近地层常年存在湖陆风、山谷风、峡谷风三者叠加效应引起的局地环流。水面盛行风向白天以东南风为主,夜间以东南风和西西南风为主,而地面白天以东东南风为主,夜间以静风和西西北风为主。年平均风速小,水面为2.9m/s,地面为2.4m/s。水面年平均气温为16.8℃,而地面为16.0℃。两观测点气温、相对湿度、气压均表现出明显的日变化特征,水面气温、相对湿度出现极大值和极小值的时间均比地面晚。全年降水多集中在5—10月。 相似文献
10.
利用普定国家气象观测站1971年1月1日-2022年2月28日逐日平均气压、平均气温、平均相对湿度、平均风速、降水量等气象资料以及普定县空气污染物日均浓度、空气质量指数（AQI）等资料,运用人体舒适度指数、气象要素与污染物浓度的相关性、人体舒适度与空气质量相关性分析方法,分析普定县近50a的人居环境气候条件。结果表明：普定县平均气温、平均风速以及日照时数呈增加趋势,相对湿度、降水量以及气压呈降低趋势。常年体感主要为凉（3级）～最舒适（5级）之间,全年体感无寒冷及酷热等级,且体感舒适（含凉舒适及最舒适）月份主要为4-10月,占全年58%。普定县气温上升、风速增大、气压下降的趋势有利于污染物浓度降低,空气质量好,而相对湿度降低、降水量减少的趋势不利于污染物浓度降低,影响空气质量。普定县空气质量以4-5月、7-11月为优,且其四季空气质量均为优,表明空气质量好,人体舒适度高,适宜人居。 相似文献
11.
根据2002—2007年北京朝阳医院逐月慢性阻塞性肺病（COPD）入院患者例次和北京朝阳气象站同期逐月地面气象资料,利用统计方法,进行相关分析,旨在探讨慢性阻塞性肺病与气候因素、气候变化的关系,保护人类免受不利气象条件的影响,利用有利的气象条件和气候资源来增强体质,预防疾病。结果表明：慢性阻塞性肺病与气温、相对湿度、气压和风速等气象因子有密切的相关性,当平均温度大于等于19.5℃时,慢性阻塞性肺病的发病例数较低;当平均温度小于19.5℃时,发病例数较高。当平均相对湿度大于等于53%时慢性阻塞性肺病的发病例数较低;当平均相对湿度小于53%时发病例数较高。当平均气压大于等于1009 hPa时,慢性阻塞性肺病的发病例数有升高的趋势;当平均气压小于1009 hPa时有下降的趋势,当平均风速大于等于3.0 m/s时,慢性阻塞性肺病的发病例数较高;当平均平均风速小于3.0 m/s时,发病例数较低。慢性阻塞性肺病发病的高发期相对于风速极大值滞后15 d。慢性阻塞性肺病发病存在着明显的月际变化和年际变化,从月际变化曲线来看慢性阻塞性肺病发病最高例数出现在4月,最低例数出现在7月;从年际变化曲线来看,慢性阻塞性肺病发病例数有逐年升高的趋势。根据气候变化、季节变化对慢性阻塞性肺病进行预测预防,以减少慢性阻塞性肺病的发生,可为该病的气象预报预警提供参考。 相似文献
12.
Urban areas are especially vulnerable to high temperatures, which will intensify in the future due to climate change. Therefore, both good knowledge about the local urban climate as well as simple and robust methods for its projection are needed. This study has analysed the spatio-temporal variance of the mean nocturnal urban heat island (UHI) of Hamburg, with observations from 40 stations from different suppliers. The UHI showed a radial gradient with about 2 K in the centre mostly corresponding to the urban densities. Temporarily, it has a strong seasonal cycle with the highest values between April and September and an inter-annual variability of approximately 0.5 K. Further, synoptic meteorological drivers of the UHI were analysed, which generally is most pronounced under calm and cloud-free conditions. Considered were meteorological parameters such as relative humidity, wind speed, cloud cover and objective weather types. For the stations with the highest UHI intensities, up to 68.7 % of the variance could be explained by seasonal empirical models and even up to 76.6 % by monthly models. 相似文献
13.
利用2016—2018年常州市区环境空气细颗粒物数据,结合同期地面气象资料,分析了常州市区PM2.5以及气象因素的变化特征,并统计分析气象因素对PM2.5浓度的影响。结果表明：常州市区PM2.5、降水量、相对湿度和气温等具有明显季节性,呈夏季较高冬季较低,而气压夏季较低冬季较高的特征。相对湿度与PM2.5呈正相关,即随着相对湿度的增加PM2.5超标率和平均浓度均增加;降水对PM2.5具有一定的清除作用,清除率与降水前PM2.5浓度、降水量、降水强度有关,降水量、降水强度越大,则降水清除效果越好,而降水前PM2.5浓度较小,则清除率不明显;常州市区偏西风时PM2.5的超标率和平均浓度较其他风向较高;风速对常州市区PM2.5的影响呈负相关,即风速越大PM2.5超标率和平均浓度均减小;常州市区地面天气形势可以分为两种类型,第一种类型表现为气压较低气温较高,PM2.5超标率以及平均浓度相对较低,而第二种类型表现为气压较高气温较低,PM2.5超标率以及平均浓度相对较高。 相似文献
14.
Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China 总被引：1，自引：0，他引：1
Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs. 相似文献
15.
福州市PM10突变特征与气象条件的关系研究 总被引：6，自引：1，他引：5
利用2004～2006年福州市PM10逐日资料,及同期地面气象要素资料和08时850 hPa天气图资料,采用统计分析方法,综合分析了PM10发生突变时气象条件的变化特征,结果表明:当地面气象要素场出现气压下降、风速下降、温度上升、相对湿度上升、降水量下降或出现气压上升、风速下降、温度下降、相对湿度下降、降水量下降的配置时PM10易发生正突变;当出现气压上升、风速上升、温度下降、相对湿度维持、降水量上升或出现气压下降、风速上升、温度上升、相对湿度上升、降水量上升的配置时PM10易发生负突变;当受大陆高压后部、暖区辐合系统影响时,PM10发生正突变的概率较高,受切变线、大陆高压前部系统影响时,PM10发生负突变的概率较高. 相似文献
16.
To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets. 相似文献
17.
Research in developing countries concerning the relationship of weather and climate conditions with tourism shows a high importance not only because of financial aspects but also an important part of the region’s tourism resource base. Monthly mean air temperature, relative humidity, precipitation, vapor pressure, wind velocity, and cloud cover for the period 1985–2005 data collected from four meteorological stations Tabriz, Maragheh, Orumieh, and Khoy were selected. The purpose of this study is to determine the most suitable months for human thermal comfort in Ourmieh Lake, a salt sea in the northwest of Iran. To achieve this, the cooling power and physiologically equivalent temperature (PET) calculated by the RayMan model and the Climate Tourism/Transfer Information Scheme (CTIS) were used. The results based on cooling power indicate that the most favorable period for tourism, sporting, and recreational activities in Ourmieh Lake is between June and October and based on PET between June to September. In addition, the CTIS shows a detailed quantification of the relevant climate–tourism factors. 相似文献
18.
Based on the data of monthly mean air temperature and precipitation from about 400 stationsin 1951—1995.and the data of maximum and minimum air temperatures,relative humidity,totalcloud cover and low-cloud cover,sunshine duration,evaporation,wind speed,snow-covered daysand depth,and soil temperatures in 8 layers from 0 m down to 3.2 m from 200 odd stations in 1961—1995.the climate change and its characteristics in China in recent 45 years have been analyzedand studied comprehensively.This paper,as the first part of the work.has analyzed the climatechange and regularities of such meteorological elements as mean air temperature,maximum andminimum air temperatures,precipitation,relative humidity and sunshine duration.The possiblemechanism on climate change in China and the climate change and regularities of othermeteorological elements will be discussed in another paper as the second part. 相似文献
19.
上海市大气降尘污染的地面气象背景特征 总被引：10，自引：0，他引：10
对上海市11 a(1993-2003年)的月降尘量和同期气象要素资料分析表明,降尘量的年际变化呈下降趋势、月际变化呈双峰型分布,气象因子是这种分布的主要影响因素;重点运用聚类分析方法将降尘地面气象背景分为A、B、C、D、E 5个类型,不同天气类型对降尘量的影响不同,其中A型最有利于降尘,其气象特征是偏南风频率、3 m/s以上风速频率、蒸发量和气温位居其他各类之首,降水量、雨日数、湿度中等,气压低.冬季,降水与降尘量呈现为一定的正相关关系. 相似文献
20.
Least squares support vector machine for short-term prediction of meteorological time series 总被引：3，自引：1，他引：2
The prediction of meteorological time series plays very important role in several fields. In this paper, an application of least squares support vector machine (LS-SVM) for short-term prediction of meteorological time series (e.g. solar irradiation, air temperature, relative humidity, wind speed, wind direction and pressure) is presented. In order to check the generalization capability of the LS-SVM approach, a K-fold cross-validation and Kolmogorov–Smirnov test have been carried out. A comparison between LS-SVM and different artificial neural network (ANN) architectures (recurrent neural network, multi-layered perceptron, radial basis function and probabilistic neural network) is presented and discussed. The comparison showed that the LS-SVM produced significantly better results than ANN architectures. It also indicates that LS-SVM provides promising results for short-term prediction of meteorological data. 相似文献