A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

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.     

Temporal and spatial variations of global solar radiation over the Qinghai–Tibetan Plateau during the past 40 years

Global solar radiation is of great significance to the balance of ground surface radiation, the energy exchange between the Earth’s surface and atmosphere, and the development of weather and climate systems in various regions. In this study, the monthly global radiation recorded at 23 stations over the Qinghai–Tibetan Plateau (QTP) was utilized to estimate global solar radiation (Q) from sunshine duration and to obtain improved fits to the variation coefficients of the monthly Angström–Prescott model (APM). The modeling results were evaluated by calculating the statistical errors, including mean bias error, mean absolute error, root mean square error, and mean relative error. We demonstrate that the monthly Q values can be predicted accurately by APM over the QTP. We also assess the variations of Q values at 116 meteorological stations by APM over the QTP during 1961–2000. The analysis shows that the annual mean sunshine duration amounted to more than 3,000 h over the whole plateau, implying promising prospects for economic applications of solar energy. During the past 40 years, the mean global solar radiation has been relatively high in the western QTP, extending northward to the Inner Mongolian Plateau. Although its decadal variations in the QTP and surrounding regions were inconsistent, the anomaly values of global solar radiation were generally positive during the 1960s and 1970s, indicating that the QTP’s global solar radiation has increased during those periods. The anomaly values were negative during the 1980s and 1990s, showing that the plateau’s global solar radiation has decreased during those periods. Global solar radiation over the QTP is negatively proportional to latitude but positively proportional to altitude and relative sunshine duration. Three factors, the sunshine duration, latitude, and altitude, exert great influence on global surface radiation, of which sunshine duration is most significant. A high-variation-coefficient zone of global solar radiation occurred in the western part of the QTP but, on average, the variation coefficient of the plateau’s global solar radiation was only 0.031, suggesting that the variation in global radiation was relatively stable over the whole QTP.     

Retrieving daily global solar radiation from routine climate variables

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m² day^?1 and 2.25 MJ m² day^?1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.     

几种水平面太阳总辐射量计算模型的对比分析

利用中国区域1961-1999年39 a间98个常规气象观测数据,建立6个模型分别以天文辐射、干洁大气总辐射和湿洁大气总辐射为起始数据,进行太阳辐射日总量的模拟,对比分析了6个水平面太阳总辐射量计算模型的性能.结果表明:在三种起始数据中,干洁大气总辐射和湿洁大气总辐射均能较好地体现宏观地势对太阳辐射空间分布的影响,以湿洁大气总辐射为起始数据的计算模型拟合精度相对较高.对6个水平面太阳总辐射量计算模型的对比分析发现:2个以日照百分率为主导因子,气温日较差为修正项的综合模型拟合误差最小,精度最高;经典的日照百分率模型次之,但其模型系数最稳定可靠;3个气温日较差模型拟合效果最差.最终选用经验系数稳定、拟合精度较高的日照百分率模型,制作了2001年中国水平面太阳辐射日总量空间分布图.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables

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.     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

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.     

Precise estimation of hourly global solar radiation for micrometeorological analysis by using data classification and hourly sunshine

We have developed a method for estimating hourly global solar radiation (GSR) from hourly sunshine duration data. This procedure requires only hourly sunshine duration as the input data and utilizes hourly precipitation and daily snow cover as auxiliary data to classify time intervals into six cases according to weather conditions. To obtain hourly GSR using a simple algebraic form, a quadratic function of the solar elevation angle and the sunshine duration ratio is used. Daily GSR is given by a sum of hourly GSRs. We evaluated the performance of the newly developed method using data obtained at 67 meteorological stations and found that the estimated GSR is highly consistent with that observed. Hourly and daily root-mean-square misfits are approximately 0.2 MJ/m²/h (~55 W/m²) and 1.4 to 1.5 MJ/m²/day (~16 to 17 W/m²), respectively. Our classification of weather conditions is effective for reducing estimation errors, especially under cloudy skies. Since the sunshine duration is observed at more meteorological stations than GSR, the proposed new method is a powerful tool for obtaining solar radiation with hourly resolution and a dense geographical distribution. One of the proposed methods, GSRgrn, can be applicable to hourly GSR estimations at different observation sites by setting local parameters (the precipitable water, surface albedo, and atmospheric turbidity) suitable to the sites. The hourly GSR can be applied for various micrometeorological studies, such as the heat budget of crop fields.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

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.     

Variations and trends in global solar radiation for Turkey

Summary Since measured solar radiation data in Turkey have rather high random errors, sunshine duration data covering the period from 1960 through 1994 from 34 stations in Turkey were taken to derive estimates of monthly mean global solar radiation by a quadratic correlation. The least square linear regression method was applied for trend analysis. Significant negative trends of the annual means were observed with 71 percent of the stations A 3.44 percent decrease in global solar radiation was observed over the last 35 years in Turkey. The decrease in solar radiation is an indication of increased air pollution, as statistical parameters show that Turkey is rapidly expanding economically, and thus air quality has deteriorated correspondingly.With 3 Figures     

Dynamics of photosynthetic photon flux density (PPFD) and estimates in coastal northern California

Plants require solar radiation for photosynthesis and their growth is directly related to the amount received, assuming that other environmental parameters are not limiting. Therefore, precise estimation of photosynthetically active radiation (PAR) is necessary to enhance overall accuracies of plant growth models. This study aimed to explore the PAR radiant flux in the San Francisco Bay Area of northern California. During the growing season (March through August) for 2?years 2007?C2008, the on-site magnitudes of photosynthetic photon flux densities (PPFD) were investigated and then processed at both the hourly and daily time scales. Combined with global solar radiation (R _S) and simulated extraterrestrial solar radiation, five PAR-related values were developed, i.e., flux density-based PAR (PPFD), energy-based PAR (PARE), from-flux-to-energy conversion efficiency (fFEC), and the fraction of PAR energy in the global solar radiation (fE), and a new developed indicator??lost PARE percentages (LPR)??when solar radiation penetrates from the extraterrestrial system to the ground. These PAR-related values indicated significant diurnal variation, high values occurring at midday, with the low values occurring in the morning and afternoon hours. During the entire experimental season, the overall mean hourly value of fFEC was found to be 2.17???mol?J^?1, while the respective fE value was 0.49. The monthly averages of hourly fFEC and fE at the solar noon time ranged from 2.15 in March to 2.39???mol?J^?1 in August and from 0.47 in March to 0.52 in July, respectively. However, the monthly average daily values were relatively constant, and they exhibited a weak seasonal variation, ranging from 2.02?mol?MJ^?1 and 0.45 (March) to 2.19?mol?MJ^?1 and 0.48 (June). The mean daily values of fFEC and fE at the solar noon were 2.16?mol?MJ^?1 and 0.47 across the entire growing season, respectively. Both PPFD and the ever first reported LPR showed strong diurnal patterns. However, they had opposite trends. PPFD was high around noon, resulting in low values of LPR during the same time period. Both were found to be highly correlated with global solar radiation R _S, solar elevation angle h, and the clearness index K _t. Using the best subset selection of variables, two parametric models were developed for estimating PPFD and LPR, which can easily be applied in radiometric sites, by recording only global solar radiation measurements. These two models were found to be involved with the most commonly measured global solar radiation (R _S) and two large-scale geometric parameters, i.e., extraterrestrial solar radiation and solar elevation. The models were therefore insensitive to local weather conditions such as temperature. In particular, with two test data sets collected in USA and Greece, it was verified that the models could be extended across different geographical areas, where they performed well. Therefore, these two hourly based models can be used to provide precise PAR-related values, such as those required for developing precise vegetation growth models.     

四川省太阳能资源气候学计算

利用SMARTS模式计算晴天总辐射,充分考虑大气对太阳辐射的削弱作用和海拔高度的影响,以四川省为例,建立了复杂自然环境条件下基于日照百分率的太阳能资源气候学计算方程。该方法不仅物理意义明确,而且计算结果误差明显降低;与实测值相比,7个辐射站年地面太阳总辐射曝辐量的相对误差均低于7%;与初始值采用天文辐射曝辐量的方法相比,无论是相对误差值还是离散程度,均降低一半以上。该方法较好地解决了在一个地形复杂、气候多变的区域采用同一计算方程的难题,从而有效避免了过去采用分区方法带来的边界不连续问题,对我国东西高差大、干湿变化明显的特殊情况具有应用价值。     

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

Long-term,ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region.The fixed station was established in 1989,and conventional radiation observations started much later in 2008.In this study,a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data,and a highprecision,long-term DGSR dataset is constructed.Then,the trend of DGSR from 1990 to 2019 at Zhongshan Station,Antarctica is analyzed.The RF model,which performs better than other models,shows a desirable performance of DGSR hindcast estimation with an R~2 of 0.984,root-mean-square error of 1.377 MJ m~(-2),and mean absolute error of 0.828 MJ m~(-2).The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004.Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station.In addition to clouds and water vapor,bad weather conditions (such as snowfall,which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station.The high-precision,longterm estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow,ice,and atmosphere.     

我国散射辐射的气候计算方法及其分布特征

使用全国６４个日射站的散射辐射资料，首先计算与建立了各地１月、４月、７月和１０月的月散射辐射值与总云量、日照百分率之间的相关系数与经验关系式，并对经验关系式进行了方差检验。该经验关系式为：Ｄ＝Ｑ０（ｓ１＋０．０１）（ａ＋ｂＮ）。应用该经验关系式和２００多个地面气象站的资料，计算了各地的１月、４月、７月和１０月的散射辐射值。最后对我国四季散射辐射的分布及其年变化作简要的分析。     

Climate and climate change in the Austrian–Swiss region of the European Alps during the twentieth century according to Feddema

We present a 1-km² gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down- and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km². This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted. The Rhine River Valley, for example, exhibited more than 100 summer days in 2003, whereas in 1996, the number was low everywhere in Germany. The dataset is useful for applications in various climate-related studies, hazard management and for solar or wind energy applications and it is available via doi: 10.5676/DWD_CDC/TRY_Basis_v001.     

我国太阳日总辐射计算方法的研究

对全国23个站点的日照时数、日最高气温、日最低气温、太阳日总辐射量等气象要素实测资料进行统计分析,利用回归分析法建立了以日照百分率和气温日较差为主要相关因子的各地日总辐射估算模型。结果表明：除了高原站拉萨以外,推算模型的复相关系数均介于0．80～0．93之间,拟合效果较好。在春、夏季使用独立的季节模型有一定的必要性,该方法适用于我国各地太阳日总辐射的推算,     

太湖无锡地区太阳总辐射的气候学计算及特征分析

为了便于研究水气界面辐射传输、水下光辐照度以及湖泊储热量，探讨太湖地区总辐射概况及其变化。文章在概述当前太阳总辐射气候学计算的主要方法及公式基础上，采用最小二乘法，利用上海、南京、杭州3站1961~2000年共40年的历史资料，确定各站的经验系数，然后内插求出太湖无锡地区的经验系数。由此推导出太湖无锡地区太阳总辐射的气候学计算公式，并利用无锡站日照百分率资料求算出近40年到达地面的太阳实际总辐射。然后利用太湖站1998年的太阳总辐射实测资料检验其公式精度，确定公式的可信度。最后对计算值进行分析，阐述了近40年来太湖无锡地区太阳总辐射的变化特征及其原因。研究结果表明:无锡地区太阳总辐射呈下降趋势，而这种下降主要是由于大气中悬浮物增加所致；总辐射年内变化趋势基本上与天文辐射相吻合，但又存在差异，这主要与梅雨的存在有关。     

Simple new methods to estimate global solar radiation based on meteorological data in Egypt

Three simple methods to estimate global solar radiation are proposed in addition to (Solar Energy 63 (1998) 147). All were tested seasonally and at different sky conditions at seven locations in Egypt. The methods use ground-based measurements of maximum and minimum temperature, daily mean of cloud cover and extraterrestrial global radiation. Average of root mean square differences (RMSD) for a comparison between observed and estimated global radiation for all locations tested was around 10% for the new methods and 13% for Supit–Van Kappel method. The coefficient of determination R² is higher for the new methods for all tested locations. Better results were obtained when applying the new methods to different seasons. The differences in root mean square error (RMSE) between the new methods and Ångstrom–Prescott method that is based on sunshine duration data were less than 1.0 MJ m⁻² day⁻¹ at all sites. On the whole, the performance statistics demonstrate that the new methods are better when compared by Ångstrom–Prescott method.     

Analysis and estimation of the effects of missing values on the calculation of monthly temperature indices

Long and complete climatic data series are a fundamental resource for scientific research on climate change. Data quality is important, and missing value or data gap management is a key process that must be dealt with carefully to produce reliable datasets. Although a large variety of techniques are available for gap-filling, a widespread strategy is to consider a dataset reliable if the rate of missing data is below a given threshold. However this strategy varies from study to study. The aim of this paper is to analyze the impact of missing daily values on the estimation of monthly average temperature indices. The relationship between the error of the estimate and the presence of random or consecutive missing values, as well as data series autocorrelation is also analyzed. A theoretical, a linear and a nonlinear model to estimate the maximum error at the 95 % confidence interval are tested on data series provided by national and worldwide networks of stations. Consecutive missing values have an important effect on error estimation due to autocorrelation of temperature data series. On our dataset, the mean and standard deviation of the error for five consecutive missing values (0.27?±?0.05 °C) on a normalized daily series (σ?=?1) was higher than for five random missing values (0.14?±?0.006 °C). A nonlinear model taking into account the number of consecutive missing values is able to estimate the error and its performance is less affected by the presence of consecutive missing values than the other proposed models.     

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.