Prediction of diffuse solar radiation using satellite data

In the project of the solar systems, the values of the solar radiation of a region must be known. The global solar radiation measurements are performed by the Turkish Meteorological Service in Turkey, while the diffuse solar radiation measurements are unavailable. In this study, some new models for predicting the monthly average daily diffuse solar radiation on a horizontal surface for Erzincan, Turkey are developed using satellite data. The evaluation of the models is carried out with the statistical analysis methods of mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (r). The results are proved that the correlation equations obtained in here can be used to forecast diffuse solar radiation reasonably well.     

A simple method for partitioning total solar radiation into diffuse/direct components in the United States

Solar radiation is a major sustainable and clean energy resource, and use of solar radiation is expected to increase. The utilization efficiency of solar energy varies with the relative proportions of the direct and diffuse components that compose total solar radiation and with the slope and aspect of the irradiated surface. The purpose of this paper is to develop a simple method for estimating diffuse and direct solar radiation at sites with observation of only total solar radiation. An existing model for estimating diffuse radiation, i.e., a linear relationship between the diffuse fraction (the ratio of diffuse radiation to total solar radiation) and the clearness index (the ratio of total solar radiation to extraterrestrial radiation), is applied to 7 sites across the continental United States with observations of diffuse and total radiation. The linear model shows good monthly performance. The model parameters (slope and interception) show a strong seasonal pattern that exhibits small variation across the 7 sites; therefore, the average values of the two monthly parameters may be used for estimating diffuse radiation for other locations with observations of total radiation.     

The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye,Turkey

This paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m² respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m²/day by June to 19.19 MJ/m²/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m²/day by October to 7.20 MJ/m²/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m²/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.     

Evaluation of various global solar radiation models for Nigeria

This study assessed the performance of six solar radiation models. The objective was to determine the most accurate model for estimating global solar radiation on a horizontal surface in Nigeria. Twenty-two years meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration for the three regions, covering the entire climatic zones in Nigeria were utilized for calibrating and validating the selected models for Nigeria. The accuracy and applicability of various models were determined for three locations (Abuja, Benin City, and Sokoto), which spread across Nigeria using seven viable statistical indices. This study found that the estimation results of considered models are statistically significant at the 95% confidence level, but their accuracy varies from one location to another. However, the multivariable regression relationship deduced in terms of sunshine ratio, air temperature ratio, maximum air temperature, and cloudiness performs better than other relationships. The multivariable relationship has the least root mean square error and mean absolute bias error, not exceeding 1.0854 and 0.8160 MJ m^?2 day^?1, respectively, and monthly relative percentage error in the range of ± 12% for the study areas.     

Comparisons and critical assessment of global and diffuse solar irradiation estimation methodologies

Defining better methodologies of accurate predictions of the amount of monthly mean daily global and diffuse solar irradiation exposed is of utmost importance in order to determine the potential for utilizing the solar energy. This study compares and discusses the main methodologies, databases, and software that are used in estimating the solar irradiation to be used for the short- and long-term performances and feasibilities of solar energy systems, especially photovoltaic power plants in Turkey, and addresses the best one to be used to make the most accurate estimations. The comparisons are carried out between the recent methodologies developed by the authors, some models taken from the related literature that are concluded to be better, a widely used database, namely Meteonorm, and a widely used software, namely EU PVGIS. The reference data to develop the methodologies and to make comparison are provided from the State Meteorological Service of Turkey, which is the responsible body in Turkey to make measurements of solar irradiation. The comparisons are based on monthly mean daily values of global and diffuse solar irradiation and are carried out by statistical errors: mean bias error and root mean square error. The results showed that the methodology developed by the authors has shown better performances in estimating the monthly mean daily global and diffuse solar irradiation amount for Turkey.     

Global solar radiation and energy yield estimation from photovoltaic power plants for small loads

Daily global solar radiation on a horizontal surface and duration of sunshine hours have been determined experimentally for five meteorological stations in Saudi Arabia, namely, Abha, Al-Ahsa, Al-Jouf, Al-Qaisumah, and Wadi Al-Dawaser sites. Five-years of data covering 1998–2002 period have been used. Suitable Angstrom models have been developed for the global solar radiation estimation as a function of the sunshine duration for each respective sites. Daily averages of monthly solar PV power outputs have been determined using the Angstrom models developed. The effect of the PV cell temperature on the PV efficiency has been considered in calculating the PV power output. The annual average PV output energy has been discussed in all five sites for small loads. The minimum and maximum monthly average values of the daily global solar radiation are found to be 12.09 MJ/m²/d and 30.42 MJ/m²/d for Al-Qaisumah and Al-Jouf in the months of December June, respectively. Minimum monthly average sunshine hours of 5.89 hr were observed in Al-Qaisumah in December while a maximum of 12.92 hr in Al-Jouf in the month of June. Shortest range of sunshine hours of 7.33–10.12 hr was recorded at Abha station. Minimum monthly average Solar PV power of 1.59 MJ/m²/day was obtained at Al-Qaisumah in the month of December and a maximum of 3.39 MJ/m²/day at Al-Jouf in June. The annual PV energy output was found to be 276.04 kWh/m², 257.36 kWh/m², 256.75 kWh/m², 245.44 kWh/m², and 270.95 kWh/m² at Abha, Al-Ahsa, Al-Jouf, Al-Qaisumah, and Wadi Al-Dawaser stations, respectively. It is found that the Abha site yields the highest solar PV energy among the five sites considered.     

Comparison of the daily global solar radiation from different data sources in Northwest China climate

The solar radiation data are of high importance to the solar energy systems. Conventional methods to obtain the solar radiation data are from weather stations, solar radiation models, commercial software databases, and field measurements. In the present study, a new daily global solar radiation model is proposed, by combining the quadratic function of sunshine fraction and sine function of the day of the year. The solar radiation model calculated data are then compared with China Meteorological Data Sharing System (CMDSS) data, TRNSYS data, and field-measured data in Northwest China climate. It is found that the newly proposed solar radiation model has better performance than the other nine solar radiation models in the literature. The solar radiation model calculated data fit well with the CMDSS annually average data. The TRNSYS data are a bit larger than the CMDSS annually average data in summer half year and a little smaller than those in winter half year. The solar radiation model and the CMDSS annually average data have the best correlation, whereas the TRNSYS data and the field-measured data have the worst correlation. The solar radiation model calculated data have the best correlation with the other three data sources.     

A METHOD FOR ESTIMATING SNOWFALL AMOUNTS1

ABSTRACT: Long-term climatic data from National Weather Service stations in six areas of the United States were utilized to develop regression equations relating a “snowfall index” parameter and air temperature. Nearly 200 stations, representing a wide range in physiographic and climatic conditions, were selected for analysis. Snowfall index is defined as the ratio of snowfall depth to precipitation. Using annual data, regression analysis indicated snowfall index to be significantly (α= 0.01) related to temperature for five of the six areas studied. Differences between equations were attributed to the effects of precipitation and temperature during non-snowfall periods. Mean monthly data were also considered and significant equations (α= 0.011, representing a family of similarly shaped curves, were obtained. The illustrated equations can be used for estimating mean monthly and annual snowfall amounts for stations having only temperature and precipitation measurements.     

ESTIMATING REFERENCE EVAPORATION IN THE CENTRAL VALLEY OF CALIFORNIA USING THE LINACRE MODEL1

ABSTRACT: The Linacre (1988) model for calculating evaporation from open water or well-watered surfaces only requires inputs of air temperature, latitude and elevation, and windspeed if it is available. The model was developed using data collected at a large number of sites in different climatic regions of the world, while independent tests of the model have shown it to be suitable for estimating evaporation in a variety of locations. This study was intended to contribute to the broad goal of evaluating temperature-based evaporation models for use in California by testing the Linacre model in the agriculturally intensive Central Valley. Observed monthly mean reference evaporation (E_o) and meteorological data for periods ranging up to 72 months were obtained from 25 California Irrigation and Management Information System (CIMIS) stations distributed throughout the Central Valley. Uncalibrated and calibrated Linacre models were used to estimate monthly mean reference evaporation, and the performance of each model was evaluated using indices that quantified the random and systematic errors and overall model performance. The accuracy of the radiation and ventilation components of the model were evaluated separately. The uncalibrated model was found to systematically overestimate E_o with most of the model error being attributed to the ventilation component. Calibration of the radiation and ventilation components removed most of the systematic model errors, and the root mean square error for monthly mean E_o was 0.676 mm day^?1 (16.8 percent of the mean observed value). (KEY TERMS: reference evaporation; Linacre model; irrigation scheduling.)     

Prediction of global solar radiation using support vector machines

This article utilizes Support Vector Machines (SVM) for predicting global solar radiation (GSR) for Sharurha, a city in the southwest of Saudi Arabia. The SVM model was trained using measured air temperature and relative humidity. Measured data of 1812 values for the period from 1998–2002 were obtained. The measurement data of 1600 were used for training the SVM, and the remaining 212 were used for comparison between the measured and predicted values of GSR. The GSR values were predicted using the following four combinations of data sets: (i) Daily mean air temperature and day of the year as inputs, and global solar radiation as output; (ii) daily maximum air temperature and day of the year as inputs, and GSR as output; (iii) daily mean air temperature and relative humidity and day of the year as inputs, and GSR as output; and (iv) daily mean air temperature, day of the year, relative humidity, and previous day’s GSR as inputs, and GSR as output. The mean square error was found to be 0.0027, 0.0023, 0.0021, and 7.65 × 10^?4 for case (i), (ii,), (iii), and (iv) respectively, while the corresponding absolute mean percentage errors were 5.64, 5.08, 4.48, and 2.8%. Obtained results show that the SVM method is capable of predicting GSR from measured values of temperature and relative humidity.     

GENERATION OF SYNTHETIC AND MISSING CLIMATIC DATA FOR PUERTO RICO1

ABSTRACT: Climatic data such as temperature, solar radiation, relative humidity, and wind speed have been widely used to estimate evapotranspiration. Moat of the solar radiation data and portions of the relative humidity data are either not available or missing from the records in Puerto Rico. Depending upon the availability and data characteristics of records, three methods (including a regression technique, an averaging of historical data, and a regional average) were used to generate missing data, and a time series analysis was used to synthesize a series of climatic data. The limitations and applicability of each method are discussed. The results showed that the time series analysis method can be successfully used to synthesize a series of monthly solar radiations for several stations. The regression technique and the regional average can be successfully applied to generate missing monthly solar radiation data. The regression technique and the averaging of historical data have been satisfactorily used to interpolate missing monthly relative humidity. The explained variance (R²) varied from 0.68 to 0.88, which are both significant at the 0.05 level of significance.     

Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model1

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.x Abstract: Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.     

STEP WISE,MULTIPLE OBJECTIVE CALIBRATION OF A HYDROLOGIC MODEL FOR A SNOWMELT DOMINATED BASIN1

ABSTRACT: The ability to apply a hydrologic model to large numbers of basins for forecasting purposes requires a quick and effective calibration strategy. This paper presents a step wise, multiple objective, automated procedure for hydrologic model calibration. This procedure includes the sequential calibration of a model's simulation of solar radiation (SR), potential evapotranspiration (PET), water balance, and daily runoff. The procedure uses the Shuffled Complex Evolution global search algorithm to calibrate the U.S. Geological Survey's Precipitation Runoff Modeling System in the Yampa River basin of Colorado. This process assures that intermediate states of the model (SR and PET on a monthly mean basis), as well as the water balance and components of the daily hydrograph are simulated consistently with measured values.     

WATERSHED CONFIGURATION AND GEOGRAPHIC INFORMATION SYSTEM PARAMETERIZATION FOR SPUR MODEL HYDROLOGIC SIMULATIONS1

ABSTRACT: A grid cell geographic information system (GIS) is used to parameterize SPUR, a quasi-physically based surface runoff model in which a watershed is configured as a set of stream segments and contributing areas. GIS analysis techniques produce various watershed configurations by progressive simplification of a stream network delineated from digital elevation models (DEM). We used three watershed configurations: ≥ 2nd, ≥ 4th, and ≥ 13th Shreve order networks, where the watershed contains 28, 15, and 1 channel segments with 66, 37, and 3 contributing areas, respectively. Watershed configuration controls simulated daily and monthly sums of runoff volumes. For the climatic and topographic setting in southeastern Arizona the ≥ 4th order configuration of the stream network and contributing areas produces results that are typically as good as the ≥ 2nd order network. However both are consistently better than the ≥ 13th order configuration. Due to the degree of parameterization in SPUR, model simulations cannot be significantly improved by increasing watershed configuration beyond the ≥ 4th order network. However, a range of Soil Conservation Service curve numbers derived from rainfall/runoff data can affect model simulations. Higher curve numbers yield better results for the ≥ 2nd order network while lower curve numbers yield better results for the ≥ 4th order network.     

SPATIAL VARIABILITY IN WATER‐BALANCE MODEL PERFORMANCE IN THE CONTERMINOUS UNITED STATES1

ABSTRACT: A monthly water‐balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil‐moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R‐square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long‐term mean and standard deviation of annual precipitation; temperature and runoff; and low‐flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R‐square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest. Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.     

油田放射性测井对环境和个人辐射剂量分析

放射性测井对环境和个人辐射剂量监测数据表明:个人受辐射伤害最大的是保管员,其次是仪器修理人员,然后是农民轮换工、司机、绞车工、操作工、主管领导、井口工人。保管员、仪器修理工、司机遭受辐射的剂量比较大,而在一线现场操作人员的个人辐射剂量水平反而都比较低,说明现场的个人防护措施得当。经过分析,确定了放射性测井施工中各岗位的安全操作时间,并针对个人防护存在的问题,提出了相应的改进措施和建议。     

吉东山区总辐射资源的地理空间分布特征

本文以地理纬度、经度和海拔高度为因子,采用二次趋势面函数,建立了描述吉林省东部长白山区年总辐射、5～9月总辐射和日平气温稳定通过10℃期间的总辐射资源的地理空间分布特征的数学模式。采用地理细网格场的分析方法,揭示了该山区总辐射资源的空间分布情况。     

The risks of radiation

Summary The risks of radioactivity are a really complicated matter, yet they are much better known than are the risks relating to thousands of chemical poisons that occur in our environment. The greatest mistakes are probably made in the definition of safety margins. Except for the bombs dropped in Japan and one other case in the Marshall Islands, there has always—luckily—been a wide safety margin between fallout radiation and doses dangerous to health; the margin has actually been about 1000-fold. The Chernobyl dose of 0.5 mGy/year that we received is only 1/1000 of the acute dose of 0.5 Gy which would cause a slight and nonpermanent change in the blood picture.There is no such safety margin with respect to many air pollutants. The safety standards for sulfuric or nitric oxides, ozone and so on, have been set only just below the level that already causes a health hazard, and these standards are exceeded once in a while. Otherwise, traffic would have to be forbidden and many industrial plants, especially power stations using coal, would have to stop working whenever a low-temperature inversion occurred. Environmental radioactivity does not represent a likely health risk in Finland unless a nuclear war breaks out. Air pollutants, on the contrary, are a real and almost daily health risk that should be carefully considered when decisions about our energy production are being made.In spite of what happened at Chernobyl, global consumption of nuclear power will double by the year 2000, since there are about 140 nuclear power plants presently under construction. It is not likely that another catastrophe like Chernobyl will happen, yet nuclear plant accidents are of course possible, even if their likelihood is diminished by improving reactor safety and even if any eventual damage could be expected to be smaller. If a reactor is hooded by a containment structure, no significant release of radioactive materials should be possible even in case of an accident. However, we must be prepared to protect the population with carefully planned civil defense and radiation protection measures.I would like to sum up all of this as follows: The risks of radiation are a fact, but the fear they arouse is often disproportionate. There are risks related to all other methods of energy production as well; nuclear power is no exception in that respect. It is good that we aim at greater safety, but I hope that false presumptions will not lead us to mistaking a less safe alternative for a safer one.     

REGIONAL FLOOD EQUATIONS FOR THE PROVINCES OF ONTARIO AND QUEBEC1

ABSTRACT: When nonparametric frequency analysis was performed on 183 stations from Ontario and Quebec, unimodal and multimodal maximum annual flood density functions were discovered. In order to determine generating mechanisms, a monthly partitioning of the annual maximum floods was undertaken. The timing of the floods revealed that the unimodal distributions reflected a single flood generating mechanism while the multi-modal densities reflected two or more mechanisms. Based on the division of the flood series by mechanisms, nine homogeneous regions were delineated. L-moment distributional homogeneity tests along with smaller standard errors for the regional equations supported the delineation.     

Load Estimation Method Using Distributions with Covariates: A Comparison with Commonly Used Estimation Methods

Load estimates obtained using an approach based on statistical distributions with parameters expressed as a function of covariates (e.g., streamflow) (distribution with covariates hereafter called DC method) were compared to four load estimation methods: (1) flow‐weighted mean concentration; (2) integral regression; (3) segmented regression (the last two with Ferguson's correction factor); and (4) hydrograph separation methods. A total of 25 datasets (from 19 stations) of daily concentrations of total dissolved solids, nutrients, or suspended particulate matter were used. The selected stations represented a wide range of hydrological conditions. Annual flux errors were determined by randomly generating 50 monthly sample series from daily series. Annual and interannual biases and dispersions were evaluated and compared. The impact of sampling frequency was investigated through the generation of bimonthly and weekly surveys. Interannual uncertainty analysis showed that the performance of the DC method was comparable with those of the other methods, except for stations showing high hydrological variability. In this case, the DC method performed better, with annual biases lower than those characterizing the other methods. Results show that the DC method generated the smallest pollutant load errors when considering a monthly sampling frequency for rivers showing high variability in hydrological conditions and contaminant concentrations.