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1.
Soil temperature data are critical for understanding land–atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.  相似文献   

2.
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   

3.
中国区域多种数值模式资料的地面气象要素评估   总被引：3，自引：0，他引：3  
利用中国气象局国家级自动站(2421个站)的观测资料,分别对2010年7月1日至2013年6月30日的欧洲中期数值预报中心(ECMWF)、日本气象厅(JMA)和美国国家海洋大气局的全球预测系统(GFS)数值模式资料的地面气温、相对湿度和风速在中国区域的适用性进行了比较研究。结果表明,3种数值模式资料都能在一定程度上反映观测资料所具有的时间和空间分布特征,东部地区的适用性要高于西部地区。不同的地面气象要素,差异较大:对于地面气温和地面相对湿度,ECMWF比JMA和GFS更接近实际观测,ECMWF和JMA的分析场数据质量要好于预报场;ECMWF和JMA地面气温在冬季与观测差异最大,GFS地面气温在夏季与观测差异最大;3种数值模式资料的地面相对湿度在秋季和冬季与观测差异最大;对于地面风速,在江淮流域和华南等东部区域JMA与实际观测差异最小,在北部和西部区域ECMWF最好,JMA和GFS地面风速在冬季与实际观测差异最大。  相似文献   

4.
This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET0) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET0 and that the ANNs with an R 2 of 0.95 and a root mean square error (RMSE) of 0.41 mm day?1 simulated ET0 better than the Hargreaves equation, which had an R 2 of 0.91 and a RMSE of 0.51 mm day?1.  相似文献   

5.
统计降尺度法对华北地区未来区域气温变化情景的预估   总被引：31，自引：1，他引：31  
迄今为止，大部分海气耦合气候模式（AOGCM）的空间分辨率还较低，很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先，采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型，然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景， 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下，无论1月还是7月，统计降尺度方法模拟的温度与观测的温度有很好的一致性，而且在大多数台站，统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面，无论1月还是7月，也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型，结果表明大多数的站点都存在温度的明显上升趋势，同时7月的上升趋势与1月相比偏低。  相似文献   

6.
Ambient air quality in respect of SO2, NO2 and total suspended particulate matter (TSPM) was monitored at Pantnagar, India from May, 2008 to April, 2009 and statistically analyzed with meteorological variables such as relative humidity (RH), wind speed (WS), precipitation (P) and mean air temperature (T). TSPM was found to be the major air pollutant causing significant deterioration of air quality with annual mean concentrations of 280 μg/m3. Further, weekly mean air pollutant concentrations were statistically analyzed through stepwise multiple linear regression analysis in respect of independent meteorological variables to develop suitable statistical models. Both NO2 and TSPM concentrations were found to have been influenced by meteorological variables with coefficient of determination (R2) of 82.21 and 92.84%, respectively. However, atmospheric SO2 revealed only 22.87% of dependencies on meteorological variables. Partial correlation coefficients revealed that wind speed has the maximum influence (77.80 and 31.50%) on proposed equations for NO2 and SO2, closely followed by weekly mean temperature (73.60 and 24.30%). However, in case of TSPM, individual contribution of ambient temperature (94.40%) was found maximum, followed by relative humidity (86.50%). Model performances were evaluated through both quantitative data analysis techniques and statistical methods. Nearly 98 and 95% of potential error has been explained by the model developed for TSPM and NO2, while in case of SO2, it is found as only 61%. Therefore, performances of models (for TSPM and NO2) to predict ambient weekly mean concentrations based on forecasted weather parameters were found to be excellent, however, performance of model developed for SO2 was found only satisfactory.  相似文献   

7.
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   

8.
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   

9.
This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.  相似文献   

10.

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.

  相似文献   

11.
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   

12.
利用2015年1月至2017年6月桂林国家基本气象站能见度、相对湿度、气温、气压、降水等气象要素和PM10、PM2.5、PM1.0颗粒物质量浓度资料,分析桂林城区大气能见度与颗粒物浓度和气象因子之间关系。结果表明：桂林城区大气能见度和PM10、PM2.5、PM1.0呈对数关系,相关系数分别为-0.341、-0.461、-0.509,颗粒物对大气能见度影响在相对湿度为60%—70%时最为显著。在各气象因子中,大气能见度与风速的相关性最好,其次为相对湿度,与风速呈二次函数关系,与相对湿度呈幂指数关系,与温度相关性较小,与气压在秋冬季节呈正相关,相关系数冬季可达0.301,但在春、夏季节相关性不显著;利用颗粒物浓度和气象要素建立8种大气能见度非线性统计回归模型,比较后发现利用PM1.0、风速、相对湿度、气温等因子建立的不同季节大气能见度拟合公式在实际检验中效果最优,能较好地模拟桂林地区大气能见度的变化。  相似文献   

13.
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   

14.
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   

15.
ECMWF 和 GRAPES(Global/Regional Assimilation and Prediction System)预报产品是国内目前主要的应用服务产品.为了了解ECMWF和GRAPES预报产品的性能,使用户在实际应用中,根据需求可选择性地应用上述预报产品,本文利用中国气象局2421个国家级自动站和81...  相似文献   

16.
刘惠民 《气象》1998,24(7):55-57
根据人体舒适度指标,考虑到温度和湿度的关系,研究了长江中下游地区夏季高温期间,没有减湿条件的空调房间的最佳降温幅度,并计算出有降温降湿条件的不同场所的最佳降温降湿幅度  相似文献   

17.
潘延  张洋  李舒婷 《气象科学》2022,42(4):440-456
本文评估了36个CMIP5模式和39个CMIP6模式对近期观测中揭示的北半球冬季大气环流与高原冬春气温之间的相关关系的模拟能力。利用最大协方差(MCA)分析方法,计算并比较了观测和模式中冬季北半球200 hPa位势高度场与同后期青藏高原近地面气温的耦合关系。整体而言,大部分CMIP模式能够模拟出显著的冬季北半球大气环流与青藏高原气温之间的相关关系,且CMIP6模式模拟相关特征和作用机制的能力较CMIP5均有所提升。与观测相比,历史情景下36个CMIP5模式中有26个能够模拟出显著的大气环流与同后期高原气温之间的相关关系,其中对于相关的位势高度场空间模态的模拟明显好于对高原气温异常场空间模态的模拟。同情景下39个CMIP6模式中有37个能模拟出显著相关关系,且CMIP6模式更能模拟出观测中MCA模态的位势高度场上北极涛动(AO)和西太平洋遥相关型(WP)反相位叠加的大气环流特征。在对MCA模态时间变率的模拟上,大部分模式都能重现青藏高原整体变暖的趋势,部分模式能够模拟出观测中位势高度场时间主成分的年际变率,并且CMIP6表现要优于CMIP5。对耦合环流型的动力诊断显示,相比CMIP5模式,CMIP6中有更多模式可以模拟出极地—高原之间的遥相关波列,且对波列结构的模拟更完整。  相似文献   

18.
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   

19.
北京夏季日最大电力负荷预报模型建立方法探讨   总被引：2，自引：1，他引：1  
为了探索夏季(6～8月)日气象负荷的最佳分离方式和引起日最大电力负荷波动的主要因子,以及建立预报模型最佳个数,基于北京市2005～2010年逐日最大电力负荷和同期的气象资料,分析了北京地区日最大电力负荷的变化规律,采用不同方法将气象负荷从夏季日最大电力负荷中分离出来,分析北京夏季气象负荷与气温、相对湿度、降水及炎热指数、高温持续日数、炎热日数持续时间、前一日气象负荷等因子之间的关系,并基于2005～2009年夏季逐日气象负荷和其主要影响因子采用逐步回归方法建立日最大电力负荷的预报模型,将2010年夏季北京日最大电力负荷作为预报效果的独立样本检验。结果显示:2005～2010年,北京逐日最大电力负荷具有明显的线性增长趋势,夏季日最大电力负荷具有显著的星期效应;与去掉逐年夏季日最大电力负荷趋势和夏季平均日最大电力负荷趋势相比,去掉全年逐日最大电力负荷变化趋势的夏季日气象负荷预报模型的拟合能力更优;北京夏季日气象负荷与当日气温的相关系数最高,与前一日气象负荷也关系密切;利用前一日相对气象负荷和当日气象要素一周逐日分别建立预报模型的拟合和预测效果较好。  相似文献   

20.
Summary This paper presents an the evaluation of three mathematical models for the estimation of 10-day drain discharge using weather, pedological and phenological parameters. One model uses modified Palmer’s procedure for the calculation of water balance components and the second two use empirical multiple linear regression. The models have been applied to data of an amelioration experimental field in central Croatia (south-western part of the Pannonian Lowland), where a set of average 10-day air temperature and relative humidity data, average wind speed, cumulative precipitation and drain discharge amounts, insolation and soil moisture data and vegetation indices have been collected. The experiment has been performed during the period 1990–1992. The models have been tested by comparing estimated and observed 10-day drain discharge amounts. All models mainly give acceptable cumulative results although their accuracy depends on the season.  相似文献   

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