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1.
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models’ accuracy was also investigated. Including periodicity component in models’ inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.  相似文献   

2.
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   

3.
Estimation of reference evapotranspiration (ET0) is needed to support irrigation design and scheduling, and watershed hydrology studies. There are many available methods to estimate evapotranspiration from a water surface, comprising both direct and indirect methods. In the first part of this study, the generalized regression neural networks model (GRNN) and radial basis function neural network (RBFNN) are developed and compared in order to estimate the reference ET0 for the first time in Algeria. Various daily climatic data, that is, daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature, and wind speed from Dar El Beida, Algiers, Algeria, are used as inputs to the GRNN and RBFNN models to estimate the ET0 obtained using the FAO-56 Penman-Monteith equation (PM56). The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. In the second part of the study, the empirical Hargreaves-Samani (HG) and Priestley-Taylor (PT) equations are also considered for the comparison. Based on the comparisons, the GRNN was found to perform better than the RBFNN, Priestley-Taylor and Hargreaves-Samani models. The RBFNN model is ranked as the second best model.  相似文献   

4.
This study describes the results of artificial neural network (ANN) models to estimate net radiation (R n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m?2 and correlation coefficient (R 2) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m?2 and R 2 between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R n prediction using commonly measured meteorological parameters.  相似文献   

5.

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R 2)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

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6.
李佳英  俞小鼎  王迎春 《气象》2006,32(7):13-17
提高对流天气临近预报准确率的关键问题之一是了解大气的垂直稳定度和垂直风切变。中尺度数值模式产品提供了高时空分辨率的大气稳定度和垂直风切变信息，需要首先检验其精度才能进一步考虑其在对流天气预报中的应用。利用北京加密探空资料检验北京市气象局3km分辨率的MM5模式结果，对强对流天气的背景参数包括温湿风垂直廓线、对流有效位能CAPE和垂直风切变进行模式分析和预报与探空对比检验。结果表明：模式模拟的各种大气廓线中，风廓线和温度廓线都具有一定的参考价值，与实况有较好的一致性，但在廓线出现转折的地方，如：逆温层和风向转折时，模式预报较差。露点（湿度）廓线的预报误差较大，不能反映出真实水汽场的分布。因此，模式预报的深层（地面至500hPa）垂直风切变与探空具有较好的一致性，而模式给出的对流有效位能CAPE由于露点预报结果不理想，其值与实际偏差较大。因此模式输出的对流有效位能CAPE必须经过适当订正才能用于诊断强对流天气发生的可能性。  相似文献   

7.
Sunshine duration data are desirable for calculating daily solar radiation (R s) and subsequent reference evapotranspiration (ET0) using the Penman–Monteith (PM) method. In the absence of measured R s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R s and ET0 was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R 2), the root mean square error (RMSE) and the mean bias error (MBE). Average R 2, RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R s and ET0 by using Ångström and FAO-PM equations, respectively.  相似文献   

8.
Soil temperature (T S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T a), solar radiation (R S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T a, RH, R S and P were reasonably correlated with T S at various depths, but the most effective parameters influencing T S at different depths were T a and RH.  相似文献   

9.
人工神经网络法和线性回归法对降水相态的预报效果对比   总被引：2，自引：1，他引：1  
董全  黄小玉  宗志平 《气象》2013,39(3):324-332
本文主要对相同条件下线性回归法(LR)和人工神经网络法(ANN)对降雨、雨夹雪和降雪3种降水相态的预报效果进行了对比检验.选取降水发生时和发生前6h的地面2 m温度、露点温度作为预报因子,对降雨、雨夹雪和降雪进行预报.应用国家气象中心2001-2011年我国地面756站实况观测资料,其中应用2001-2010年资料对方法进行训练,2011年资料用来对比检验预报效果.结果显示,(1)两种方法对3种相态降水都有一定的预报能力,对降雪预报最好,其次是降雨和雨夹雪;(2)两种方法对北方的雨雪分界线预报比对南方的好;(3)无论是对全国还是长江中下游流域,在相同条件下,ANN法的预报效果大都优于LR法,当温度和露点温度预报准确时,ANN法对北方的雨雪分界线能进行较准确的预报.  相似文献   

10.
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   

11.
We report on the development of an inexpensive radiative condenser for collecting atmospheric vapor. Based on the experience gained using a small working model in Grenoble (France), a prototype of 10×3 m2 was established in Ajaccio (Corsica, France). The condensing surface is a rectangular foil made of TiO2 and BaSO4 microspheres embedded in polyethylene and has an angle of 30° with respect to horizontal. The hollow part of the device, thermally isolated, faces the direction of the dominant nocturnal wind. Dew measurements were correlated with meteorological data and compared to dew condensed on a horizontal polymethylmethacrylate (PMMA, Plexiglas) reference plate. The plate served as a reference standard unit and was located nearby. Between July 22, 2000 and November 11, 2001 (478 days), there were 145 dew days for the reference plate (30%), but 214 dew days for the condenser (45%). This yield corresponds to 767 l (3.6 l, on average, per dew day). The maximum yield in the period was 11.4 l/day. Dew mass can be fitted to a simple model that predicts dew production from simple meteorological data (temperature, humidity, wind velocity, cloud cover). Chemical analyses of the water collected from the plate were performed from October 16, 1999 to July 16, 2000 and from the condenser, from July 17, 2000 to March 17, 2001. The following parameters were investigated: suspended solids, pH, concentration of SO42−, Cl−, K+, Ca2+ ions. Only Cl− and SO42− ions were sometimes found significant. Wind direction analyses revealed that Cl− is due to the sea spray and SO42− to the combustion of fuel by an electrical plant located in the Ajaccio Gulf. Except for a weak acidity (average pH≈6) and high concentration of suspended solids, dew water fits the requirements for potable water in France with reference to the above ions.  相似文献   

12.
Mean radiant temperature (T mrt) based on two measurement methods and outputs from three models are compared in this study. They are the six direction radiation method, globe thermometer method, RayMan model, ENVI-met model and SOLWEIG model. The comparison shows that globe thermometer method may overestimate the T mrt since wind velocity is a key variable in the estimation based on this method. For better estimation, T mrt measured by the globe-thermometer method be corrected by the imported wind speed (stable, low and assuming wind speed) and validated by the six-direction radiation method. The comparison of models shows that the RayMan model’s evaluation of T mrt involving global radiation with fine time resolution was better than the corresponding evaluations under the other two models (ENVI-met and SOLWEIG) in this case. However, the RayMan model can only assess T mrt for a one-point one-time context, whereas the other two models can evaluate two-dimensional T mrt. For two-dimensional evaluations of T mrt, SOLWEIG have a better prediction of T mrt than ENVI-met, and ENVI-met can simulate several different variables, which are wind field, particle distribution, CO2 distribution and the other thermal parameters (T a, surface temperature and radiation fluxes), that SOLWEIG cannot.  相似文献   

13.
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   

14.
The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R 2?>?0.88 and RMSE?<?1.2 mm day?1), but the ANN method gave better estimates than the calibrated Hargreaves method.  相似文献   

15.
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   

16.
The aim of this study is to calculate the low-level atmospheric motion vectors （AMVs） in clear areas with FY-2E IR2 window （11.59-12.79 μm） channel imagery,where the traditional cloud motion wind technique fails.A new tracer selection procedure,which we call the temporal difference technique,is demonstrated in this paper.This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature （TB） differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions.The TB difference corresponding to a 10％ change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission （MODTRAN4） radiative transfer model.The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions：tropical,midlatitude summer,U.S.standard,and midlatitude winter.The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB ＂wind＂.This technique is valid over cloudfree ocean areas.The proposed algorithm exhibits encouraging statistical results in terms of vector difference （VD）,speed bias （BIAS）,mean vector difference （MVD）,standard deviation （SD）,and root-mean-square error （RMSE）,when compared with the wind field of NCEP reanalysis data and rawinsonde observations.  相似文献   

17.
This study creates a database on incoming solar radiation using artificial neural networks (ANN) and information on altitude, air temperature and pressure, water vapor pressure, dry air density, water vapor density, and mixing ratio obtained at five weather stations in Turkey. The coefficients of correlation of the calculation results for three regimes of air density with observational data for the training sample (2000–2001) are 99.24%, 99.82%, and 96.67%; for the testing sample (2002), 95.97%, 82.32%, and 95.11%. These values indicate that the usage of artificial neural networks and data of at-mosphere parameters is a correct and effective method for estimation of solar radiation and creation solar databases.  相似文献   

18.
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   

19.
Summary The Reitan (1963) regression equation of the form lnw =a +bT d has been examined and tested to estimate precipitable water vapor content from the surface dew point temperature at different locations. The results of this study indicate that the slopeb of the above equation has a constant value of 0.0681, while the intercepta changes rapidly with latitude. The use of the variable intercept technique can improve the estimated result by about 2%.With 6 Figures  相似文献   

20.
Daily global solar irradiation (R s) is one of the main inputs in environmental modeling. Because of the lack of its measuring facilities, high-quality and long-term data are limited. In this research, R s values were estimated based on measured sunshine duration and cloud cover of our synoptic meteorological stations in central and southern Iran during 2008, 2009, and 2011. Clear sky solar irradiation was estimated from linear regression using extraterrestrial solar irradiation as the independent variable with normalized root mean square error (NRMSE) of 4.69 %. Daily R s was calibrated using measured sunshine duration and cloud cover data under different sky conditions during 2008 and 2009. The 2011 data were used for model validation. According to the results, in the presence of clouds, the R s model using sunshine duration data was more accurate when compared with the model using cloud cover data (NRMSE = 11. 69 %). In both models, with increasing sky cloudiness, the accuracy decreased. In the study region, more than 92 % of sunshine durations were clear or partly cloudy, which received close to 95 % of total solar irradiation. Hence, it was possible to estimate solar irradiation with a good accuracy in most days with the measurements of sunshine duration.  相似文献   

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