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.     

Models for obtaining daily global solar irradiation from air temperature data

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.     

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.     

1960~2000年黄河流域太阳总辐射气候变化规律研究

运用数据集群技术，在黄河流域建立了用日照百分率拟合太阳总辐射的不同时空尺度估算式，对比分析了不同数据集群下总辐射的拟合精度。利用逆距离加权插值法，将获得的黄河流域及其周边35个日射站1~12月总辐射拟合的经验系数进行空间内插，获得了黄河流域1~12月总辐射拟合经验系数的空间分布。结合黄河流域及其周边164个常规气象站日照百分率观测资料，对黄河流域1960~2000年太阳总辐射进行了计算，分析了其气候变化的时空分布规律。结果表明:黄河流域近年来太阳总辐射呈下降趋势，在季节上主要表现在夏季和冬季。     

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,     

Daily soil temperature modeling using neuro-fuzzy approach

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models’ performances at arid and semiarid locations showed that the CANFIS models’ performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.     

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.     

Decreasing trend of sunshine hours and related driving forces in Southwestern China

Total hours of sunshine are one of the most important factors affecting climate and environment, and its long-term variation is of much concern in climate studies. Trends of temporal and spatial patterns in sunshine hours and related climatic factors over southwestern China are evaluated for the period 1961–2009 based on data from 111 standard meteorological stations. The results showed that southwestern China is experiencing a statistical decrease of sunshine hours, at the rate of 31.9 h/10a during 1961–2009. The decline was particularly strong in summer, whereas it is nonsignificant in winter. Spatially, statistically significant decreases of sunshine hours mainly occurred in lower altitude regions, especially in the Sichuan basin and Guizhou plateau. Sunshine hours have a high correlation with wind speed, relative humidity, precipitation, cloud cover, surface downwards solar radiation flux, and cloud water content, with wind speed showing the strongest relationship to sunshine hours, implicit in the close correlation (temporally and spatially) between the two variables. Changing water vapor and cloud cover influence sunshine hours in southwestern China. In addition, the increased surface downwards solar radiation flux also made some contribution to a rise of sunshine hours during 1991–2009. The larger decreasing trends of sunshine hours at urban stations than rural stations may reflect the effect of urbanization on sunshine hours. Variations are dominated by the comprehensive functions of multiple factors owing to the complex nature of effects on sunshine hours.     

GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions

Accurate estimation of reference evapotranspiration (ET₀) becomes imperative for better managing the more and more limited agricultural water resources. This study examined the feasibility of developing generalized artificial neural network (GANN) models for ET₀ estimation using weather data from four locations representing different climatic patterns. Four GANN models with different combinations of meteorological variables as inputs were examined. The developed models were directly tested with climatic data from other four distinct stations. The results showed that the GANN model with five inputs, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed, performed the best, while that considering only maximum temperature and minimum temperature resulted in the lowest accuracy. All the GANN models exhibited high accuracy under both arid and humid conditions. The GANN models were also compared with multivariate linear regression (MLR) models and three conventional methods: Hargreaves, Priestley–Taylor, and Penman equations. All the GANN models showed better performance than the corresponding MLR models. Although Hargreaves and Priestley–Taylor equations performed slightly better than the GANN models considering the same inputs at arid and semiarid stations, they showed worse performance at humid and subhumid stations, and GANN models performed better on average. The results of this study demonstrated the great generalization potential of artificial neural techniques in ET₀ modeling.     

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.     

山西省太阳能资源时空分布特征及利用潜力评估

按照中国气象局发布的太阳能资源评估方法,利用山西省近30 a 3个辐射观测站的太阳总辐射资料和105个站的日照资料,采用气候学方法计算了山西省的太阳总辐射,在分析山西省太阳总辐射的空间分布和时间演变特征的基础上,计算了山西省太阳能资源的各种参数,对区域太阳能资源潜力进行了客观评估。结果表明山西省太阳能资源储备丰富、稳定、可开发利用日数较多,特别是山西北部地区,尤其适合进行太阳能资源开发利用。     

A practical model to estimate photosynthetically active radiation using general meteorological elements in a temperate humid area and comparison among models

Although accurately evaluating photosynthetically active radiation is important, much effort is required to measure this radiation using a quantum sensor. We develop a new model that makes estimates using only general meteorological data—solar radiation, atmospheric pressure, air temperature, and relative humidity. Root mean square deviations for eight datasets at five sites in Japan were smaller than 5.2 %, similar to error in other studies and to individual differences of quantum sensors. Most root mean square deviations of nine previous models and our eight datasets are larger than that of the new estimation model, which performed well. This suggests that the model is useful for estimating photosynthetically active radiation in a temperate, humid area of Japan.     

韶关地区太阳能资源分析与评估

根据1981-2015年广州、韶关等9站气象观测资料,采用气候学统计、线性趋势分析和有关行业标准等,对韶关地区太阳能资源分布特征进行了分析评估。结果表明:近35年来,韶关地区属于太阳能资源丰富区,年太阳总辐射的线性变化趋势不显著;夏季辐射丰富,冬季偏少;月平均总辐射7月最多,2月最少;年总辐射仁化最多,乐昌最少。年日照时数的线性变化趋势也不显著;夏季日照时数多,春季的少;月平均日照时数7月最多,3月最少;年日照时数也是仁化最多,乐昌最少。日照时数6 h的平均天数为139.6天,太阳能资源利用价值一般,但7-12月太阳能资源利用价值较高。月最大日照时数7月6 h的天数为18.2天,是月最小日照时数3月的3.96倍,太阳能资源较稳定,可进行太阳能资源的开发利用。     

锡林浩特市太阳辐射变化及相关气象要素分析

利用1990—2010年锡林浩特气象站太阳总辐射、日照时数、日照百分率、降水量、气温以及总云量的观测资料,运用5a滑动平均及最小二乘原理等方法,分析了锡林浩特市1990—2010年来的总辐射变化规律,并分析了气温、降水、日照、云量等的变化以及与太阳总辐射的相关关系。研究结果表明,锡林浩特市太阳总辐射呈现波动增加的趋势,但四季太阳辐射量在近21a的年际波动略有不同。相关要素分析表明,太阳总辐射与日照时数和夏季、秋季气温有很显著的正相关关系,与降水量和春季、夏季的总云量呈负相关关系。     

中国不同时间尺度地表太阳总辐射估算研究

利用全国95个气象站点逐日地表太阳总辐射和日照时数资料,通过最小二乘法拟合回归建立地表太阳总辐射气候学计算模型。通过对比分析以日值和月值为起点的地表太阳总辐射计算模型的精度,确定了全国不同省份和区域的不同时间尺度（月、季节、生长季和年）地表太阳总辐射计算模型,并探讨了经验系数a、b值的分布及变化特征。结果表明,以日值和月值为起点建立的月、四季、生长季和年地表太阳总辐射计算模型精度无显著性差异,相对误差均低于8.5%,但以日值为起点的计算模型a、b值变异性更小。在以日值为起点建立计算模型的前提下,全国各地a、b值自西北部向南部减小,且从四季到生长季再到年尺度,随着时间尺度增大,a、b值振幅减小。根据不同省份年地表太阳总辐射计算模型经验系数a、b值,全国可划分为新甘蒙地区、青藏高原地区和中东部地区3个区域,分别确定了每个区域四季、生长季和年尺度下地表太阳总辐射计算模型。各区域不同时间尺度地表太阳总辐射计算模型均通过了显著性检验（p<0.01）,其中青藏高原地区和新甘蒙地区模型相对误差低于8.0%,模拟精度较高。     

Precipitable water vapor estimation in India from GPS-derived zenith delays using radiosonde data

One of the most recent applications of global positioning system (GPS) is the estimation of precipitable water vapor (PWV). It requires proper modeling to extract PWV from zenith wet delay (ZWD). The existing global models take no account of latitudinal and seasonal variation of meteorological parameters in the atmosphere. In fact, they ignore the atmospheric conditions at a specific location. Therefore, site-specific PWV models have been developed for five stations spread over the Indian subcontinent, using 3-year (2006–2008) radiosonde data from each of these stations. Furthermore, a similar regional PWV model is also developed for the Indian region. The purpose of the developed site-specific as well as regional model was to convert ZWDs into PWV without using surface meteorological parameters. It has been found that the developed regional and site-specific PWV models show about mm-level accuracy in estimating PWV using derived ZWD from radiosonde as input. The developed site-specific, regional models were also used to extract PWV from GPS-derived ZWD at Bangalore and New Delhi. The accuracy of the developed site-specific and regional model is of the same level. The PWV accuracy obtained with the developed regional model is about 6.28, 6.6 mm in comparison to radiosonde PWV at Bangalore and New Delhi, respectively.     

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.     

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.     

Comparison of the performance of net radiation calculation models

Daily values of net radiation are used in many applications of crop-growth modeling and agricultural water management. Measurements of net radiation are not part of the routine measurement program at many weather stations and are commonly estimated based on other meteorological parameters. Daily values of net radiation were calculated using three net outgoing long-wave radiation models and compared to measured values. Four meteorological datasets representing two climate regimes, a sub-humid, high-latitude environment and a semi-arid mid-latitude environment, were used to test the models. The long-wave radiation models included a physically based model, an empirical model from the literature, and a new empirical model. Both empirical models used only solar radiation as required for meteorological input. The long-wave radiation models were used with model calibration coefficients from the literature and with locally calibrated ones. A measured, average albedo value of 0.25 was used at the high-latitude sites. A fixed albedo value of 0.25 resulted in less bias and scatter at the mid-latitude sites compared to other albedo values. When used with model coefficients calibrated locally or developed for specific climate regimes, the predictions of the physically based model had slightly lower bias and scatter than the empirical models. When used with their original model coefficients, the physically based model had a higher bias than the measurement error of the net radiation instruments used. The performance of the empirical models was nearly identical at all sites. Since the empirical models were easier to use and simpler to calibrate than the physically based models, the results indicate that the empirical models can be used as a good substitute for the physically based ones when available meteorological input data is limited. Model predictions were found to have a higher bias and scatter when using summed calculated hourly time steps compared to using daily input data.