Frequency Distributions for Hourly and Daily Clearness Indices

2001 ◽  
Vol 124 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Manuel Iban˜ez ◽  
William A. Beckman ◽  
Sanford A. Klein

The clearness index for hourly and daily radiation is an important parameter in describing solar radiation. Liu and Jordan demonstrated that the monthly average daily clearness index could be used to predict the long-term distribution of daily solar radiation in a month. This paper reviews recent literature on the prediction of hourly and daily frequency distributions and cumulative frequency distributions of clearness indices. Ten years of measured weather data for six cities in the U.S. are used to investigate the nature of the hourly and daily frequency distributions. A second set of ten years of data for six cities is used to verify the predictions. A bi-exponential probability density function is proposed that fits the observed bimodal nature of the data better than existing models. A case is made for the function being universal.

Author(s):  
Manuel Ibañez ◽  
William A. Beckman ◽  
Sanford A. Klein

Abstract The clearness index for hourly and daily radiation is an important parameter in describing solar radiation. Liu and Jordan demonstrated that the monthly average daily clearness index could be used to predict the long-term distribution of daily solar radiation in a month. This paper reviews recent literature on the prediction of hourly and daily frequency distributions and cumulative frequency distributions of clearness indices. Ten years of measured weather data for six cities in the US are used to investigate the nature of the hourly and daily frequency distributions. A second set of ten years of data for six cities is used to verify the predictions. A bi-exponential probability density function is proposed that fits the observed bimodal nature of the data better than existing models. A case is made for the function being universal.


2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Haixiang Zang ◽  
Qingshan Xu ◽  
Pengwei Du ◽  
Katsuhiro Ichiyanagi

A modified typical meteorological year (TMY) method is proposed for generating TMY from practical measured weather data. A total of eleven weather indices and novel assigned weighting factors are applied in the processing of forming the TMY database. TMYs of 35 cities in China are generated based on the latest and accurate measured weather data (dry bulb temperature, relative humidity, wind velocity, atmospheric pressure, and daily global solar radiation) in the period of 1994–2010. The TMY data and typical solar radiation data are also investigated and analyzed in this paper, which are important in the utilizations of solar energy systems.


Solar Energy ◽  
2004 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. RaviKumar

The present article describes the development of cumulative frequency distributions (similar to clearness index distributions) for daily global horizontal illuminance based on the modeled data of 56 US locations. Normalized variables have been employed to account for seasonal and location dependencies. The normalized variables contain the maximum and minimum illuminance values in the array of daily values in addition to the daily illuminance (the variable to be related to cumulative frequency) and the monthly average daily illuminance. The distributions thus developed discrete in terms of the normalized monthly average daily global illuminance have been presented in a convenient linear form which accommodates continuous values for the parameter. When data are not available, the maximum and minimum of the daily values needed in estimating the parameter first and eventually the daily illuminance values have been correlated to other more readily available predictors. Present distributions have been validated by comparing against the daily illuminance values available in the large data base employed. Daily global illuminance has been predicted with a standard deviation of 23.44 klux and rms difference of 4.64%. The present correlations can be conveniently applied to obtain the 30 or 31 daily illuminance values in a month from the monthly average value.


2021 ◽  
Author(s):  
Bao The Nguyen

According to the natural geographical distribution, developing countries are concentrated in tropical climates, where radiation is abundant. So the use of solar energy is a sustainable solution for developing countries. However, daily or hourly measured solar irradiance data for designing or running simulations for solar systems in these countries is not always available. Therefore, this chapter presents a model to calculate the daily and hourly radiation data from the monthly average daily radiation. First, the chapter describes the application of Aguiar’s model to the calculation of daily radiation from average daily radiation data. Next, the chapter presents an improved Graham model to generate hourly radiation data series from monthly radiation. The above two models were used to generate daily and hourly radiation data series for Ho Chi Minh City and Da Nang, two cities representing two different tropical climates. The generated data series are tested by comparing the statistical parameters with the measured data series. Statistical comparison results show that the generated data series have acceptable statistical accuracy. After that, the generated radiation data series continue to be used to run the simulation program to calculate the solar water distillation system and compare the simulation results with the radiation data. Measuring radiation. The comparison results once again confirm the accuracy of the solar irradiance data generation model in this study. Especially, the model to generate the sequences of hourly solar radiation values proposed in this study is much simpler in comparison to the original model of Graham. In addition, a model to generate hourly ambient tempearure date from monthly average daily ambient temperature is also presented and tested. Then, both generated hourly solar radiation and ambient temperature sequences are used to run a solar dsitillation simulation program to give the outputs as monthly average daily distillate productivities. Finally, the outputs of the simulation program running with the generated solar radiation and ambient temperature data are compared with those running with measured data. The errors of predicted monthly average daily distillate productivities between measured and generated weather data for all cases are acceptably low. Therefore, it can be concluded that the model to generate artificial weather data sequences in this study can be used to run any solar distillation simulation programs with acceptable accuracy.


1983 ◽  
Vol 105 (3) ◽  
pp. 281-287 ◽  
Author(s):  
D. R. Clark ◽  
S. A. Klein ◽  
W. A. Beckman

A computationally simple algorithm is presented for evaluating the hourly utilizability function, φ, defined as the fraction of the long-term, monthly-average, hourly solar radiation incident on a surface which exceeds a specified threshold intensity. The algorithm was developed by correlating values of φ obtained by numerical integration of hourly radiation for three locations. The algorithm is shown to compare well both with a more complex analytical expression for φ developed recently and with results obtained numerically using many years of hourly horizontal radiation measurements in nine U.S. locations. In addition, the algorithm is shown to be applicable for surfaces of any orientation.


2003 ◽  
Vol 44 (5) ◽  
pp. 691-705 ◽  
Author(s):  
Ali Al-Lawati ◽  
Atsu S.S Dorvlo ◽  
Joseph A Jervase

2021 ◽  
Author(s):  
Basil Psiloglou ◽  
Harry D. Kambezidis ◽  
Konstantinos V. Varotsos ◽  
Dimitris G. Kaskaoutis ◽  
Dimiitris Karagiannis ◽  
...  

<p>It is generally accepted that a climatic data set of meteorological measurements with true sequences and real interdependencies between meteorological variables is needed for a representative climate simulation. In the late 1970s the Typical Meteorological Year (TMY) concept was introduced in USA as a design tool for approximating expected climate conditions at specific locations, at a time when computers were much slower and had less memory than today. A TMY is a collation of selected weather data for a specific location, listing usually hourly values of meteorological and solar radiation elements for one-year period. The values are generated from a data bank much longer than a year in duration, at least 10 years. It is specially selected so that it presents the range of weather phenomena for the location in question, while still giving annual averages that are consistent with the long-term averages for the specific location. Each TMY data file consists of 12 months chosen as most “typical“ among the years present in the long-term data set. Although TMYs do not provide information about extreme events and do not necessarily represent actual conditions at any given time, they still reflect all the climatic information of the location. TMY sets remain in popular use until today providing a relatively concise data set from which system performance estimates can be developed, without the need of incorporating large amounts of data into simulation models. </p><p>TMY sets for 33 locations in Greece distributed all over the country were developed, covering for the first time all climatic zones, for both historical and future periods. Historical TMY sets generation was based on meteorological data collected from the Hellenic National Meteorological Service (HNMS) network in Greece in the period 1985-2014, while the corresponding total solar radiation values have been derived through the Meteorological Radiation Model (MRM).</p><p>Moreover, the generation of future TMY sets for Greece was also performed, for all 33 locations. To this aim, bias adjusted daily data for the closest grid point to the HNMS station’s location were employed from the RCA4 Regional Climate Model of the Swedish Meteorological and Hydrological Institute (SMHI) driven by the Earth system model of the Max Planck Institute for Meteorology (MPI-M). Simulations were carried out in the framework of the EURO-CORDEX modeling experiment, with a horizontal RCA4 model resolution of 0.11<sup>o</sup> (~12 x 12 km). We used daily data for four periods: the 1985-2014 used as reference period and the 2021-2050, 2046-2070 and 2071-2100 future periods under RCP4.5 and RCP8.5 scenarios. </p><p>This work was carried out in the framework of the “Development of synergistic and integrated methods and tools for monitoring, management and forecasting of environmental parameters and pressures” (KRIPIS-THESPIA-II) Greek national funded project.</p>


2017 ◽  
Vol 21 (3) ◽  
pp. 1379-1387
Author(s):  
Shanmugham Ravichandran ◽  
Jebamani Rathnaraj

An attempt has been made to analyze and evaluate the daily average clearness index (Hg/H0) in terms of daily average cloudiness index (Hd/H0) for three tropical locations in South India (Chennai, Trivandrum, and Visakapatnam). Long term data (15 years, 1993-2007) of measured daily average global and diffuse solar radiation for the locations have been used for this study. Two correlation equations (linear and polynomial) for each location have been developed for clearness index in terms of cloudiness index and found its validity. Performance statistics of the model has been done and applicability of the model is done by comparing the performance statistics with the existing models. It has been found that the proposed model has least error compared with the existing models.


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