Monthly reference distribution of daily relative sunshine values

In this paper it is shown that for a given location the monthly distributions of the daily relative sunshine depend only, within statistical errors, on their monthly averages and are independent of the month of the year. Furthermore it is put in evidence that a reference distribution of the daily relative sunshine values for every month of the year can be found by the knowledge of the average deduced from data of some years.     

The reference frequency distributions of daily relative sunshine from different locations

In some applications the relative sunshine distribution curves are very usefull. The present work tends to provide the above frequency distributions for the Italian area, using the daily data of eleven stations. A preliminary compatibility test is applied in order to verify if a dependence on location exist and it is deduced that the area referring to the various locations is statistically compatible with a few exceptions. Therefore, it is possible to give generalized frequency distributions curves which can be considered reference curves for all Italian locations. The empirical distributions are also compared with the calculated ones by the method of Bendt et al. and a good agreement is found.     

Correlation between relative sunshine and state of the sky

It is possible to construct a sunshine map referring to a large zone, lacking in a widespread network of recording stations, correlating the relative sunshine with the “state of the sky”, defined by the monthly mean number of the clear, mixed and overcast days, whose data are more largely available. In the correlation here proposed climatological and meteorological factors are introduced in order to express the helioclimatic characteristics of every location. The application to Italy, using the data of the period 1973–1977 provided by the 31 stations of ITAV, has suggested the classification of these stations in seven “helioclimatic groups” characterized by different values of the above factors, mainly representative of the position and typology of the surrounding surfaces and of the atmospheric transparence in a cloudy or cloudless sky. The positive results of the comparison between the experimental and computed relative sunshine values are shown in the tables.     

Estimation of daily total and diffuse insolation in India from weather data

Insolation and weather data for a large number of cities in India is analysed and correlated. Correlations based on a citywise regression analysis indicate that daily total insolation correlates best with sunshine duration, all clouds and precipitation. However these relations are not useful for predicting insolation at locations where this data is not measured. Monthwise correlations which are valid over a region are more useful. Hence such correlations have been developed for Indian conditions. In order to increase the accuracy of prediction of these correlations, India is divided into two regions on the basis of the climatic characteristics of the winter monsoon.Finally the Liu and Jordan model for predicting daily diffuse radiation from daily total radiation has been tested and found to be applicable for Indian conditions. However the numerical values obtained are very different from those obtained for conditions in the United States.     

A simplification of weather data to evaluate daily and monthly energy needs of residential buildings

Hourly, daily, monthly and annual heating and cooling requirements of a residential building located in Ottawa, Ontario, Canada were estimated, employing ENERPASS as the energy simulation tool, and performing hour-by-hour energy analysis. The following weather data were employed:

1. (i) Ten years (1967–1976) of weather data. The ten-year average of the results is identified as TYA.

2. (ii) A typical meteorological year (TMY) generated using the same ten years of data.

3. (iii) Two different hourly ambient air temperature distributions (T1 and T2) for a typical day in each month. The solar radiation on each surface was estimated using the mean monthly clearness index.

The house use patterns, including heat generation and the thermostat setting, were taken the same when using TYA, TMY, T1 or T2. The analysis was carried out for the house as it is (well insulated and airtight), and for two modifications: one with larger infiltration rate and lower wall thermal resistance, and the other with larger south-facing window area and using super-windows. The results of this study show that the long-range hourly, daily, monthly and annual heating and cooling requirements of a residential building located in a cold climate can be predicted by employing mean daily maximum and minimum temperatures and the mean monthly clearness index for each month. This amounts to substantial savings in computational costs, in either using many years of weather data or generating a TMY for the site. For locations lacking detailed hourly weather data, the use of data and the procedure outlined in this study may be employed to predict the long-range thermal performance of simple residential buildings.     

Effect of sunshine and solar declination on the computation of monthly mean daily diffuse solar radiation

Several years of measured data for 17 European locations have been used to develop models for estimating monthly mean daily values of diffuse radiation (Hd) from combinations of the following: clearness index, sunshine fraction, and solar declination. Two models giving the highest correlation coefficients and the lowest standard errors of estimation are tested with data for 10 European locations not used in their development. From consideration of the MBE and RMSE values, a model which estimates Hd values from clearness index, relative sunshine duration and solar declination is found to be the most accurate. Comparison with Hd values predicted with the European Community solar radiation model (ECM) confirms this conclusion.     

Correlation of monthly-average daily global, diffuse and beam radiation with bright sunshine hours

Correlations between monthly-average daily radiation and bright sunshine hours for 11 Indian stations have been studied. It is found that the global radiation can be predicted to an accuracy of 0.5% by using the linear relation, G/G_ext = a + b (n/N). For diffuse radiation, three different correlations have been tried. For individual applications, any of the three correlations discussed in this paper can be used, but for collective applications, the correlation D/G_ext = a + b (n/N) gives better results. Beam radiation is found to be highly dependent upon the local conditions. To develop a single formula for 11 stations involves a large percentage of error. The Hay method [9] is comparable to the equation G/G_ext = a + b(n/N) for global radiation, but for diffuse radiation, the equation D/G_ext = a + b (n/N) is superior to the Hay method. The correlations for global and diffuse radiation are G/G_ext = 0.3156 + 0.4520(n/N) and D/G_ext = 0.3616 − 0.2123 (n/N), respectively.     

Spatial estimation of monthly mean daily sunshine hours and solar radiation across mainland China

Spatial databases of climate data in digital format are required for many agricultural and eco-environmental systems. This study compared 7 approaches for interpolating monthly mean daily sunshine hours and solar radiation over mainland China. The approaches included simple geostatistical approaches to incorporation of Universal Transverse Mercator (UTM) coordinates and elevation. Performance indicators (root mean square error, mean absolute percentage error, and modeling efficiency) showed thin plate smoothing spline with UTM coordinates and elevation (TPS) outperformed other models. Besides, multiple linear regression equations for estimating solar radiation using geographical parameters (UTM coordinates and elevation) and sunshine hours predicted by TPS performed well for the study site. Spatial datasets of annual and monthly mean daily sunshine hours and solar radiation with 1 km resolution were then obtained by the best performance models. Spatial and temporal variability was clearly observed in sunshine hours and solar radiation. For both annual and seasonal scenarios, higher values of sunshine hours and solar radiation existed in north and Tibetan Plateau and lower values were observed in the middle and southern China. Lower values of annual solar radiation were also found in northeastern China. Sunshine hours and solar radiation varied with time, especially from spring to summer and from summer to autumn. The accurate gridded datasets are expected to provide significant information on more efficient use of natural resources.     

Calculation of monthly average global solar radiation on horizontal surfaces using daily hours of bright sunshine

Several statistical models calculating the monthly average global solar radiation on horizontal surfaces using the daily hours of bright sunshine have been extensively evaluated and compared for Canadian regions. Results show that Gariépy's model provides the best overall performance and Hay's model is rated next best, whereas Rietveld's model has been found to be the least accurate. Local performances of models have also been established throughout Canada that show that both Gariépy's and Hay's models perform quite well. Recommended models have been suggested for the studied regions.     

采用"典型日"法计算日均浓度的方法探讨

“典型日”法是建设项目环境影响评价中计算日均浓度常用的方法，但由于工作经费和时间的限制，选择的典型日通常代表性较差。为此，提出了一种既减少工作量，又不失代表性的典型日确定方法，并以某建设项目环境影响评价为例进行说明。     

Estimation of daily and monthly direct, diffuse and global solar radiation from sunshine duration measurements

An accurate 200 W/m² threshold pyreheliometer instrument for measuring the duration of bright sunshine has been used to derive daily and monthly regressions for direct, diffuse, and global solar radiation component vs sunshine duration. Daily regression for diffuse/global are linear in sunshine duration, while quadratic regression forms are employed for direct normal, direct horizontal, and global/extraterrestrial components. Only the daily direct normal component had regression values which depend on season while all of the monthly regressions depend on season. Linear regression relations for monthly direct normal, diffuse/global and global/extraterrestrial are employed, with a quadratic form being used for direct horizontal. Effects of rainfall, especially in overcast conditions, and of atmospheric turbidity and precipitable water, especially under clear-sky conditions, are observed and documented.     

Analysis of monthly average daily global radiation and monthly average sunshine duration at two tropical locations

The diffuse radiation falling on a horizontal surface at Madras, India has been analysed and the results presented. Five years of data have been used for the analysis and a correlation has been established between the daily average diffuse radiation with the monthly average clearness index. It has also been shown that there is an appreciable difference in the ratio of monthly average hourly diffuse to the monthly average daily diffuse radiation with respect to day length.     

Coupling satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface

Satellite images are heavily used for the estimation of solar irradiation at the Earth's surface. The accuracy yet should be improved to attain more reliable input values for the use of all types of solar energy systems. This paper presents two new alternative approaches to increase the estimation accuracy of daily solar irradiation by coupling the satellite images with surface bright sunshine hour measurements. Two different approaches are described for the estimation of global solar irradiation on daily base, by using the data for some locations in Turkey and Germany. These approaches are compared with the estimation of a satellite model (HELIOSAT), Angstrom models and ground measured daily global solar irradiation by using regressions and error analyses. For nine out of ten stations the relative RMSE values of the proposed models slightly decrease in the range of only 2% in comparison with the direct satellite model for the daily global solar irradiation. The results obtained for the new approaches did not considerably improve the performance of the satellite model. However, it is possible to recommend new coupled approaches to estimate daily global solar irradiation because of their simpler calculation procedure. The results are encouraging for the future works to use long and short-term satellite image data together with the surface measured data to estimate the solar irradiation values.     

Prediction of global solar radiation from bright sunshine hours and other meteorological data

Three existing empirical relations which predict global radiation from bright sunshine hours and meteorological parameters, were tried for 14 Indian stations where all relevant data was available. A large amount of error (±50%) was found. So a new empirical relation was established between global radiation and meteorological parameters. The new relation predicted the insolation within a ±10% error limit in most cases. Global radiation dependence on ambient temperature and relative humidity was introduced through atmosperic water content per unit volume. The relation is WAT = RH(4.7923+0.3647×T+0.0055×T²+0.0003×T³)G = G_ext(0.414+0.400×SS-0.0055×WAT)$\text{SS}\text{= S}\text{Z}\text{?}$     

Correlations between monthly average global solar radiation on horizontal surfaces and relative duration of sunshine in Yunnan Province, China

With measured data of global solar radiation and the relevant data of geographic and meteorological parameters at 7 meteorological stations in Yunnan Province, the correlations between monthly average global solar radiation on horizontal surfaces and relative duration of sunshine are developed in this special region which has significantly varying climates. It is believed that the two correlations developed in this work are applicable for estimating monthly global solar radiation on horizontal surfaces at any site in Yunnan Province, China.     

Bin weather data for the provinces of the Eastern Anatolia in Turkey

This paper presents information about bin weather data that have been generated from weather histories and recorded over a period of many years for 14 cities located in the Eastern Anatolia region in Turkey. The analysis covers 14 provinces: A?r?, Ardahan, Bingöl, Bitlis, Elaz??, Erzincan, Erzurum, Hakkari, I?d?r, Kars, Malatya, Mu?, Tunceli, and Van. The bin data for dry-bulb temperature from ?36 °C to 45 °C with 3 °C increments are calculated in six daily 4-h shifts (1–4, 5–8, 9–12, 13–16, 17–20 and 21–24 h). In the calculations, the hourly dry-bulb temperatures recorded by the Turkish State Meteorological Affairs between the years 1990 and 2005 (16 years) are used. Monthly and yearly bin data are presented in tables and on graphs. The information and data presented in this paper hope to fill a gap in information needed by building designers and engineers for simplified energy calculations of buildings in the Eastern Anatolia region.     

Predicting the energy output of wind farms based on weather data: Important variables and their correlation

Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data.