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Solar radiation is an essential and important variable to many models. However, it is measured at a very limited number of meteorological stations in the world. Developing method for accurate estimation of solar radiation from measured meteorological variables has been a focus and challenging task. This paper presents the method of solar radiation estimation using support vector machine (SVM). The main objective of this work is to examine the feasibility of SVM and explore its potential in solar radiation estimation. A total of 20 SVM models using different combinations of sunshine ratio, maximum and minimum air temperature, relative humidity, and atmospheric water vapor pressure as input attributes are explored using meteorological data at 15 stations in China. These models significantly outperform the empirical models with an average 14 % higher accuracy. When sunshine duration data are available, model SVM2 using sunshine ratio and air temperature range is proposed. It significantly outperforms the empirical models with an average 26 % higher accuracy. When sunshine duration data are not available, model SVM19 using maximum temperature, minimum temperature and atmospheric water vapor pressure is proposed. It significantly outperforms the temperature-based empirical models with an average of 18 % higher accuracy. The remarkable improvement indicates that the SVM method would be a promising alternative over traditional approaches for estimation of solar radiation at any locations. 相似文献
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Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m?2?d?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m?2?d?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area. 相似文献
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M. A. Ghorbani R. Khatibi B. Hosseini M. Bilgili 《Theoretical and Applied Climatology》2013,114(1-2):107-114
In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz. 相似文献
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Ming-Ko Woo 《Climatic change》1992,20(4):313-330
Stochastic modelling provides a tool for exploring the full implications of the statistical behavior of historical records and can be used to predict the changing probabilities that events of various magnitudes will occur for different climatic change scenarios. Two simulation models are presented, one for daily air temperature, and the other for daily precipitation. The simulation procedures are: (1) extract salient parameter values from historical records; (2) simulate long sequences of data using the stochastic models, with or without a climatic change scenario as provided by a general circulation model; and (3) using the simulated data as inputs, derive the probability distributions of other variables based on known deterministic or probabilistic relationships between the input and the predicted variables.Given a doubling of carbon dioxide concentration in the atmosphere, the climatic models produce varying degrees of temperature and precipitation changes. Examples of application, including the derivation of snowfall and riverice data using simulated temperature and precipitation, illustrate that stochastic modelling offers a suitable approach to quantify the possible hydrologic impacts of climatic change. 相似文献
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北京雾与霾天气大气液态水含量和相对湿度层结特征分析 总被引：3，自引：0，他引：3
为了研究空气中的水汽层结变化对雾、霾生消的影响,对北京2011年10月至2012年2月雾、霾天气个例中能见度变化和地基微波辐射计观测的相对湿度及液态水含量资料进行分析,结果表明:大气总液态水含量时序图对预报雾、霾没有参考意义,无论是大气总液态水含量数值的大小,还是大气总液态水含量随时间的变化都不能预测雾、霾的生成与消散。但不同时刻大气液态水含量的廓线图对雾、霾天气的预报还是具有指示意义的,因为雾、霾生消前后大气液态水含量层结变化明显。进一步分析不同情况的雾、霾天气发现:雾、霾生消前后均无降水出现和先出大雾后降水的情况,即降水后消散的雾、霾天气,大气相对湿度的变化和液态水含量的变化主要集中在3 km以下;对于先降水后出大雾的情况,整层大气相对湿度的变化都很明显,液态水含量的变化主要在3~7 km之间。由于降水既可以增加近地面的空气湿度,又可以消耗空气中的水汽,因此降水既是大雾形成的有利条件,也是大雾消散的有利条件。有降水出现的大雾天气,有饱和层(空气相对湿度达到或接近100%),无降水出现的重霾天气,则没有饱和层,且整体相对湿度偏低。 相似文献
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根据1958—2015年我国北方地区8个主产省（市）小麦蚜虫分省发生面积和发生程度资料、1958—2015年601个气象站点相应逐日气象资料和农业气象站小麦发育期资料,采用相关分析、主成分分析和逐步回归等方法,并利用相关系数法进行因子普查,结合小麦蚜虫适宜生理气象指标和华北、黄淮小麦生育期规律,筛选影响小麦蚜虫年发生程度的关键气象因子,构建分区域的小麦蚜虫气象适宜度预报模型,并将气象适宜度指数划分为非常适宜、适宜、较适宜、不适宜4个等级,以反映气象条件对小麦蚜虫发生发展的适宜程度。结果表明:筛选出影响华北小麦蚜虫年发生程度的8个关键气象因子,影响黄淮小麦蚜虫年发生程度的6个关键气象因子。建立的华北、黄淮模型回代检验等级准确率分别为91.2%,93.1%,2016—2018年3年外推预报平均准确率均在75%以上;利用黄淮模型反演苏皖两省2016—2018年小麦蚜虫发生等级、异地检验3年预报效果均较理想。模型适用于从气象角度对华北、黄淮及江淮地区小麦蚜虫发生等级进行监测和预报。 相似文献
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利用浙江省义乌市2015—2019年逐小时气象观测数据(相对湿度、风速、地气温差、能见度)和空气质量指数(Air Quality Index, AQI)数据, 分析了义乌地区低能见度天气(观测能见度lt; 10 km)的分布特征和气象要素条件。利用长短期记忆神经网络(Long Short Term Memory Neural Network, LSTM)模型对逐小时能见度进行模拟, 分别对比了观测能见度作为输入变量与否的模拟效果; 根据义乌地区低能见度天气条件的特征, 将模拟时段分为三个时期(11月至翌年2月, 3—6月, 7—10月), 对比了分时期模拟的效果; 以及评估了模型的预报步长。结果表明: 高湿、高污染、气温高于地温和低风速是义乌地区低能见度天气的主要特征。LSTM模型对单站能见度有较好的模拟效果, 当输入参数中加入历史观测能见度时, 能大幅提高模拟准确度, 日均能见度模拟结果均方根误差RMSE=0.63 km, 平均绝对误差MAE=0.51 km, 拟合优度R2=0.99;分时期进行模拟能得到更精准的模拟结果。本研究中选用的输入要素在冬季(11月至翌年2月)模拟效果最好, RMSE=2.35 km, MAE=1.46 km, 低能见度均方根误差RMSE_10 km=1.81 km, 低能见度平均绝对误差MAE_10 km=1.13 km, R2=0.83; 3—6月的模拟中, 输入变量中不加AQI模拟效果更好, 这意味着3—6月义乌地区的低能见度天气以雾天气为主导, 加入过多变量并不一定能提高模型准确度; 随着预报步长增大, 模型预报效果变差, 预测步长等于3 h, R2=0.71, 预测结果已不具备实际应用意义。 相似文献
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The statistical scheme is proposed for the forecast of surface air temperature and humidity using operative weather forecasts with 3–5-day lead time from the best forecasting hydrodynamic models as well as the archives of forecasts of these models and observational data from 2800 weather stations of Russia, Eastern Europe, and Central Asia. The output of the scheme includes the forecasts of air temperature for the standard observation moments with the period of 6 hours and extreme temperatures with the lead times of 12–120 hours. The accuracy of temperature and humidity forecasts for the period from July 2014 till June 2017 is much higher than that for the forecasts of original hydrodynamic models. The skill scores for extreme temperature forecasts based on the proposed method are compared with the similar results of the Weather Element Computation (WEC) forecasting scheme and forecasts by weathermen. 相似文献
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A. R. Ivanova 《Russian Meteorology and Hydrology》2009,34(6):354-363
Results of verification of the relative humidity forecasting performed by using a number of numerical models are presented. Efficiency of forecasting of possible conditions combinations that cause the aircraft icing, such as negative temperature and high relative humidity, is assessed. The best results are demonstrated by the NCEP global model and semi-Lagrangian model with varying resolution. Significant discrepancies in the humidity initial fields modeled by using different models are revealed. 相似文献
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Summary In this paper two mesoscale meteorological models are applied to the Lisbon region. A special concern is directed to the initial conditions and input parameterization in order to assure that equal simulation constraints were imposed to both models. Results obtained for 4 August 1992 (a typical summer day) are compared to meteorological data acquired in three monitoring stations. Both simulations show similar reactions to the mesoscale forcings that occur in the modelling region. The minor differences found between simulation and reality for the meteorological situation can cause major errors in predicting air quality.With 8 Figures 相似文献
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Attenuation Correction Procedures for Water Vapour Fluxes from Closed-Path Eddy-Covariance Systems 总被引：1，自引：1，他引：0
Benjamin R. K. Runkle Christian Wille Michal Gažovič Lars Kutzbach 《Boundary-Layer Meteorology》2012,142(3):401-423
Evapotranspiration is a source of water vapour to the atmosphere, and as a crucial indicator of landscape behaviour its accurate
measurement has widespread implications. Here we investigate errors that are prevalent and systematic in the closed-path eddy-covariance
measurement of latent heat flux: the attenuation of fluxes through dampened cospectral power at high frequencies. This process
is especially pronounced during periods of high relative humidity through the adsorption and desorption of water vapour along
the tube walls. These effects are additionally amplified during lower air temperature conditions. Here, we quantify the underestimation
of evapotranspiration by a closed-path system by comparing its flux estimate to simultaneous and adjacent measurements from
an open-path sensor. We apply models relating flux loss to relative humidity itself, to the lag time of the cross-correlation
peak between the water vapour and vertical wind velocity signals, and to models of cospectral attenuation relative to the
cospectral power of simultaneous sensible heat-flux measurements. We find that including the role of temperature in modifying
the attenuation–humidity relationship is essential for unbiased flux correction, and that physically based cospectral attenuation
methods are effective characterizers of closed-path instrument signal loss relative to the unattenuated flux value. 相似文献
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BP神经网络在油菜花期预报中的应用 总被引：1，自引：0，他引：1
建立花期预报模型,发布观赏性植物的精准花期预报,为旅游活动提供重要参考依据,已经成为气象服务领域一个新的发展方向。为了解高淳旅游区油菜花期的变化规律,探索其预报方法,指导高淳油菜花节旅游活动,根据1985—2010年高淳站日最高气温、日最低气温、日平均气温、日降水量、日日照时数、日平均5 cm地温、日平均相对湿度和日小型蒸发量等气象观测数据,利用主成分分析法,得到其与油菜花期相关系数较大的3个主成分,即温度因子、天气因子和辐射因子,以此为输入因子,建立基于BP神经网络的油菜花期预报模型,探讨BP神经网络在花期预报领域的应用。结果表明,传统的有效积温方法预报结果与实际开花期平均相差4.25天,BP神经网络方法预报结果与实际开花期平均相差1.5天,与有效积温预报油菜花期的方法相比,BP神经网络技术具有预测结果准确率高和操作简单等特点,在花期预报领域具有广阔的应用前景。 相似文献
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Sang-Hyun Lee 《Boundary-Layer Meteorology》2011,140(2):315-342
The vegetated urban canopy model (VUCM), which includes parametrizations of urban physical processes for artificial surfaces
and vegetated areas in an integrated system, has been further developed by including physical processes associated with grass-covered
surfaces in urban pervious surfaces and the photosynthesis effects of urban vegetation. Using measurements made from three
urban/suburban sites during the BUBBLE field campaign in 2002, the model’s performance in modelling surface fluxes (momentum
flux, net radiation, sensible and latent heat fluxes and storage heat flux) and canopy air conditions (canopy air temperature
and specific humidity) was critically evaluated for the non-precipitation and the precipitation days. The observed surface
fluxes at the urban/suburban sites were significantly altered by precipitation as well as urban vegetation. Especially, the
storage heat at urban surfaces and underlying substrates varied drastically depending on weather conditions while having an
important role in the formation of a nocturnal urban surface layer. Unlike the nighttime canopy air temperature that was largely
affected by the storage-heat release, the daytime canopy air conditions were highly influenced by the vertical turbulent exchange
with the overlying atmosphere. The VUCM well reproduced these observed features in surface fluxes and canopy air conditions
at all sites while performing well for both the non-precipitation and the precipitation days. The newly implemented parametrizations
clearly improved the model’s performance in the simulation of sensible and latent heat fluxes at the sites, more noticeably
at the suburban site where the vegetated area fraction is the largest among the sites. Sensitivity analyses for model input
parameters in VUCM elucidated the relative importance of the morphological, aerodynamic, hydrological and radiative/thermal
properties in modelling urban surface fluxes and canopy air conditions for daytime and nighttime periods. These results suggest
that the VUCM has great potential for urban atmospheric numerical modelling for a range of cities and weather conditions in
addition to having a better physical basis in the representation of urban vegetated areas and associated physical processes. 相似文献
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The study presents a critical assessment of the possibility of global solar irradiation computation by using air temperature instead of sunshine duration with the classical Ångström equations. The reason for this approach comes from the fact that, although the air temperature is a worldwide measured meteorological parameter, this is rarely used in solar radiation estimation techniques. More than that, the literature is very silent concerning the testing of such models in Eastern Europe. Two new global solar irradiation models (to be called AEAT) related to solar irradiation under clear sky conditions and having the minimum and maximum daily air temperature as input parameters were tested and compared with others from the literature against data measured at five stations in Romania in the year 2000. The accuracy of AEAT is acceptable and comparable to that of the models which use sunshine duration or cloud amount as input parameters. Since temperature-based Ångström correlations are strongly sensitive to origin, the approach for AEAT as a tool for potential users is presented in detail. Additionally reported is a new method to increase the generality of AEAT concerning the extension of the geographical application area. Based on overall results it was concluded that air temperature successfully substitutes sunshine duration in the estimation of the available solar energy. 相似文献
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The most appropriate indices with which to quantify Australian bushfire danger are the McArthur fire danger meters. These meters use meteorological information to produce a fire danger index that is directly related to the chance of a fire starting - and to the severity of a fire once it has started. The Mark 5 forest-fire danger meter uses air temperature, relative humidity and wind speed, plus a drought factor that is calculated using daily rainfall and temperature information.Three years of daily data generated from the CSIRO four-level general circulation model, and thirty years of daily data generated from the CSIRO nine-level model were used to estimate the daily McArthur forest fire danger index for simulations corresponding to present conditions, and to those corresponding to doubled atmospheric CO2. The performance of these models with respect to fire danger was tested by comparing the fire danger index for Sale (in the Eastern part of Victoria, South-eastern Australia) calculated from analysis of daily climatological data with the modelled annual cumulative forest fire danger index for the grid point that was representative of Sale. Data from both models for all Australian grid points were also examined. Both models predict an increase in fire danger over much of Australia for their doubled CO2 scenarios.The results from the models confirm that annually averaged daily relative humidity is the single most important variable in the estimation of forest fire danger on an annual basis, yet the models tend to produce relative humidities that are slightly too low so that the fire danger is overestimated. A simple one-box model of evaporation indicates that the value of relative humidity to be expected under an altered climatic regime depends on the modelled relation between actual and potential evaporation, the present values of relative humidity and evaporation rate, as well as on the expected changes in wind speed. 相似文献