scholarly journals Performance of CAMS Radiation Service and HelioClim-3 databases of solar radiation at surface: evaluating the spatial variation in Germany

2020 ◽  
Vol 17 ◽  
pp. 143-152
Author(s):  
Mathilde Marchand ◽  
Yves-Marie Saint-Drenan ◽  
Laurent Saboret ◽  
Etienne Wey ◽  
Lucien Wald
Keyword(s):  
Standard Deviation ◽  
Solar Radiation ◽  
Correlation Coefficient ◽  
Satellite Images ◽  
Ground Level ◽  
Spatial Trend ◽  
The North ◽  
Spatial Consistency ◽  
The Mean ◽  
Sky Conditions

Abstract. The present work deals with the spatial consistency of two well-known databases of solar radiation received at ground level: the CAMS Radiation Service database version 3.2, abbreviated as CAMS-Rad and the HelioClim-3 database version 5, abbreviated as HC3v5. Both databases are derived from satellite images. They are validated against 10 min means of irradiance for the period 2010–2018 recorded in a network of 26 ground stations in Germany operated by the Deutscher Wetterdienst (DWD). For the CAMS-Rad database, the correlation coefficient between ground measurements and estimates ranges between 0.83 and 0.92 for all sky conditions. The bias ranges from −41 and 32 W m−2 (−11 % and 10 % of the mean irradiance). The standard deviation ranges between 89 and 129 W m−2 (25 % and 39 %). For the HC3v5 database, the correlation coefficient ranges between 0.90 and 0.95. The bias and the standard deviation are comprised between −22 and 16 W m−2 (−6 % and 5 %), and between respectively 70 and 104 W m−2 (20 % and 31 %). For the CAMS Rad database, overestimation is observed in the South, and underestimation in the North with a faint tendency of the bias to increase from East to West. For the HC3v5 database, the bias is fairly homogeneous across Germany. For both databases, there is no noticeable spatial trend in the standard deviation.

scholarly journals Validation of the McClear clear-sky model in desert conditions with three stations in Israel

2016 ◽  
Vol 13 ◽  
pp. 21-26 ◽  
Author(s):  
Mireille Lefèvre ◽  
Lucien Wald
Keyword(s):  
Correlation Coefficient ◽  
United Arab Emirates ◽  
Ground Level ◽  
Normal Incidence ◽  
Clear Sky ◽  
Global Irradiance ◽  
Desert Areas ◽  
The Mean ◽  
Monitoring Service ◽  
Sky Conditions

Abstract. The new McClear clear-sky model, a fast model based on a radiative transfer solver, exploits the atmospheric properties provided by the EU-funded Copernicus Atmosphere Monitoring Service (CAMS) to estimate the solar direct and global irradiances received at ground level in cloud-free conditions at any place any time. The work presented here focuses on desert conditions and compares the McClear irradiances to coincident 1 min measurements made in clear-sky conditions at three stations in Israel which are distant from less than 100 km. The bias for global irradiance is comprised between 2 and 32 W m−2, i.e. between 0 and 4 % of the mean observed irradiance (approximately 830 W m−2). The RMSE ranges from 30 to 41 W m−2 (4 %) and the squared correlation coefficient is greater than 0.976. The bias for the direct irradiance at normal incidence (DNI) is comprised between −68 and +13 W m−2, i.e. between −8 and 2 % of the mean observed DNI (approximately 840 W m−2). The RMSE ranges from 53 (7 %) to 83 W m−2 (10 %). The squared correlation coefficient is close to 0.6. The performances are similar for the three sites for the global irradiance and for the DNI to a lesser extent, demonstrating the robustness of the McClear model combined with CAMS products. These results are discussed in the light of those obtained by McClear for other desert areas in Egypt and United Arab Emirates.

scholarly journals Catalogue of Clusters that are Members of Superclusters

1983 ◽  
Vol 104 ◽  
pp. 185-186
Author(s):  
M. Kalinkov ◽  
K. Stavrev ◽  
I. Kuneva
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
The Other ◽  
Clusters Of Galaxies ◽  
Number Of Clusters ◽  
Abell Clusters ◽  
The Mean ◽  
Abell Cluster ◽  
Image Position

An attempt is made to establish the membership of Abell clusters in superclusters of galaxies. The relation is used to calibrate the distances to the clusters of galaxies with two redshift estimates. One is m10, the magnitude of the ten-ranked galaxy, and the other is the “mean population,” P, defined by: where p = 40, 65, 105 … galaxies for richness groups 0, 1, 2 …, and r is the apparent radius in degrees given by: The first iteration for redshift, z1, is obtained from m10 alone: The standard deviation for Eq. (1) is 0.105, the number of clusters with known velocities is 342 and the correlation coefficient between observed and fitted values is 0.921. With zi from Eq. (1), we define Cartesian galactic coordinates Xi = Rih−1 cosBi cosLi, Yi = Rih−1 cosBi sinLi, Zi = Rih−1 sinBi for each Abell cluster, i = 1, …, 2712, where Ri is the distance to the cluster (Mpc), and Ho = 100 h km s−1 Mpc−1.

scholarly journals Personality and Motivation: A Correlation study among Sports Person

2019 ◽  
Vol 6 (4) ◽  
pp. 5-13
Author(s):  
M Keerthika ◽  
S Punithavathi
Keyword(s):  
Standard Deviation ◽  
Gender Difference ◽  
Correlation Coefficient ◽  
Significant Relationship ◽  
Correlation Study ◽  
Motivation Factors ◽  
The Mean ◽  
The Individual ◽  
Age Range ◽  
The Relationship

In this competitive world, it is essential to grab the sportive nature of sports persons. For different personality type of the individual the motive to engage in sports also varies from person to person. The aim of the present study is to determine the relationship between personality and motivation among sports persons and to identify the gender difference of personality and motivation factors. The sample of this study was 120 sports persons out of which 60 were males and 60 were females belonging to the age range of 18 -30 years. The mean, standard deviation and Pearson’s correlation coefficient were used for analysing the data. Results indicate that there is no significant relationship between Personality and Motivation type of sports persons.

scholarly journals McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

2013 ◽  
Vol 6 (9) ◽  
pp. 2403-2418 ◽  
Author(s):  
M. Lefèvre ◽  
A. Oumbe ◽  
P. Blanc ◽  
B. Espinar ◽  
B. Gschwind ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Weather Prediction ◽  
Ground Level ◽  
Surface Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Sky Conditions ◽  
Aerosol Properties

Abstract. A new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. It is a fully physical model replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapour and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 105 times greater by adopting the abaci, or look-up table, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions at several stations within the Baseline Surface Radiation Network in various climates. The bias for global irradiance comprises between −6 and 25 W m−2. The RMSE ranges from 20 W m−2 (3% of the mean observed irradiance) to 36 W m−2 (5%) and the correlation coefficient ranges between 0.95 and 0.99. The bias for the direct irradiance comprises between −48 and +33 W m−2. The root mean square error (RMSE) ranges from 33 W m−2 (5%) to 64 W m−2 (10%). The correlation coefficient ranges between 0.84 and 0.98. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modelled by the MACC reanalysis.

A Comparison of Techniques for Particle Rebound Measurement in Gas Turbine Applications

2015 ◽  
Author(s):  
Jeffrey P. Bons ◽  
Rory Blunt ◽  
Steven Whitaker
Keyword(s):  
Standard Deviation ◽  
Velocity Distribution ◽  
Gas Turbine ◽  
Correlation Coefficient ◽  
Strong Dependence ◽  
Individual Particle ◽  
Aluminum Surface ◽  
Trajectory Data ◽  
Mean Velocity ◽  
The Mean

The rebound characteristics of 100–500μm quartz particles from an aluminum surface were imaged using the particle shadow velocimetry (PSV) technique. Particle trajectory data were acquired over a range of impact velocity (30–90 m/s) and impact angle (20°–90°) typical for gas turbine applications. The data were then analyzed to obtain coefficients of restitution (CoR) using four different techniques: (1) individual particle rebound velocity divided by the same particle’s inbound velocity (2) individual particle rebound velocity divided by inbound velocity taken from the mean of the inbound distribution of velocities from all particles (3) rebound velocity distribution divided by inbound velocity distribution related using distribution statistics and (4) the same process as (3) with additional precision provided by the correlation coefficient between the two distributions. It was found that the mean and standard deviation of the CoR prediction showed strong dependence on the standard deviation of the inbound velocity distribution. The two methods that employed statistical algorithms to account for the distribution shape [methods (3) and (4)] actually overpredicted mean CoR by up to 6% and CoR standard deviation by up to 100% relative to method (1). The error between the methods is shown to be a strong (and linear) function of correlation coefficient, which is typically 0.2–0.6 for experimental CoR data. Non-Gaussianity of the distributions only accounts for up to 1% of the error in mean CoR, and this largely from the non-zero skewness of the inbound velocity distribution. Particle rebound data acquired using field average techniques that do not provide an estimate of correlation coefficient are most accurately evaluated using method (2). Method (3) can be used with confidence if the standard deviation of the inbound velocity distribution is less than 10% of the mean velocity, or if a linear correction based on an assumed correlation coefficient is applied.

The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra

1994 ◽  
Vol 2 (1) ◽  
pp. 43-47 ◽  
Author(s):  
M.S. Dhanoa ◽  
S.J. Lister ◽  
R. Sanderson ◽  
R.J. Barnes
Keyword(s):  
Particle Size ◽  
Standard Deviation ◽  
Correlation Coefficient ◽  
Size Effects ◽  
Scatter Correction ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Standard Normal ◽  
Alternative Approaches ◽  
The Mean

We demonstrate that set-dependent multiplicative scatter correction and set-independent standard normal variate transformations of NIR spectra are linearly related as theoretically expected. It is shown that the mean and standard deviation of the set-mean-spectrum together with the correlation coefficient between each individual spectrum and set-mean-spectrum are required to link these two transformations. It is through these three quantities, that set-dependency is incorporated into spectra derived by application of multiplicative scatter correction. MSC and SNV are two alternative approaches to reduce particle size effects and they are interconvertible.

Study on Aerosol Optics Thickness Retrieval over Chuxiong Prefecture from MODIS Image

2013 ◽  
Vol 742 ◽  
pp. 414-419
Author(s):  
Wu Jun Xi ◽  
Ping He ◽  
Wang Zhou Yang
Keyword(s):  
Standard Deviation ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
The North ◽  
Modis Image ◽  
Thickness Standard ◽  
The Mean

The paper used 6S radiative transfer model to study aerosol optics thickness retrieval in Chuxiong prefecture with MODIS image on January 12th, 2008, the results showed that: the mean, the minimum, the maximum and standard deviation aerosol optics thickness were 0.115644, 0.075, 0.337 and 0.099680 in Chuxiong prefecture. Aerosol optics thickness of the east and south was higher, that of the north and central are low, especially west. Lufeng county, Shuangbai county and Wuding county were in the top three of the maximum aerosol optics thickness. Lufeng county, Shuangbai county and Yongren county were in the top three of mean aerosol optics thickness, Lufeng county, Shuangbai county and Yuanmou county were in the top three of aerosol optics thickness standard deviation.

scholarly journals CLIGEN Parameter Regionalization for Mainland China

2021 ◽  
Author(s):  
Wenting Wang ◽  
Shuiqing Yin ◽  
Bofu Yu ◽  
Shaodong Wang
Keyword(s):  
Standard Deviation ◽  
Solar Radiation ◽  
Mainland China ◽  
Cumulative Distribution ◽  
Annual Rainfall ◽  
Stochastic Weather Generator ◽  
Critical Set ◽  
The Mean ◽  
Parameter Values

Abstract. Stochastic weather generator CLIGEN can simulate long-term weather sequences as input to WEPP for erosion predictions. Its use, however, has been somewhat restricted by limited observations at high spatial-temporal resolutions. Long-term daily temperature, daily and hourly precipitation data from 2405 stations and daily solar radiation from 130 stations distributed across mainland China were collected to develop the most critical set of site-specific parameter values for CLIGEN. Universal Kriging (UK) with auxiliary covariables, longitude, latitude, elevation, and the mean annual rainfall was used to interpolate parameter values into a 10 km × 10 km grid and parameter accuracy was evaluated based on leave-one-out cross-validation. The results demonstrated that Nash-Sutcliffe efficiency coefficients (NSEs) between UK interpolated and observed parameters were greater than 0.85 for all parameters apart from the standard deviation of solar radiation, skewness coefficient of daily precipitation, and cumulative distribution of relative time to peak intensity, with relatively lower interpolation accuracy (NSE > 0.66). In addition, CLIGEN simulated daily weather sequences using UK-interpolated and observed inputs showed consistent statistics and frequency distributions. The mean absolute discrepancy between the two sequences in the average and standard deviation of the temperature was less than 0.51 °C. The mean absolute relative discrepancy for the same statistics for solar radiation, precipitation amount, duration and maximum intensity in 30-min were less than 5 %. CLIGEN parameters at the 10 km resolution would meet the minimum WEPP climate requirements throughout in mainland China. The dataset is availability at http://clicia.bnu.edu.cn/data/cligen.html and http://doi.org/10.12275/bnu.clicia.CLIGEN.CN.gridinput.001 (Wang et al., 2020).

scholarly journals Study of Seasonal Surface Refractivity over North-Central Nigeria

2021 ◽  
pp. 5-13
Author(s):  
Muhammad Ladan ◽  
Oyedum David ◽  
Jibrin Yabagi ◽  
Ndanusa Babakacha ◽  
Mohammed Kimpa ◽  
...  
Keyword(s):  
High Frequency ◽  
Meteorological Data ◽  
Central Zone ◽  
Ground Level ◽  
Weather Conditions ◽  
Very High Frequency ◽  
International Telecommunication Union ◽  
North Central ◽  
The North ◽  
The Mean

Tropospheric radio wave signals experience loss due to multipath effect, scattering and other forms of attenuation through the atmospheric medium, primarily due to variations in weather conditions with time. The knowledge of surface refractivity profile is important for optimal planning of Very High Frequency/Ultra High Frequency (VHF/UHF) terrestrial radio links in a region. The study of surface refractivity (Ns) over the North-Central Nigeria was carried out using meteorological data from seven locations in North-Central zone of Nigeria. The seasonal variations of Ns were also derived using the monthly summaries of surface data obtained from Nigerian Meteorological Agency (NIMET) over seven stations of Abuja, Lafia, Lokoja, Makurdi, Jos, Minna and Ilorin between 2005 and 2010.The results indicated that the monthly averages of radio refractivity during the rainy season months (April to October) are greater than the Ns values during the dry season months (November to March) for all the locations throughout the years of the study. The computed of mean monthly Ns over all the seven stations in the first 1 km above the ground level is 348 N-units, which gives mean refractivity gradient (dN/dh) of -49 N/k, these shows that the region is characterised by low scale super-refraction. The mean k-factor over the entire region in the first 1 km above the ground level is 1.4; the mean Field Strength Variability (FSV) in first 1 km of height in the region was calculated to be 14 dB. The mean Radio Horizon distance within 1 km height for a transmitter height of 100 m over the stations is 42 km. The results provide useful information needed by radio engineers to set up new terrestrial radio propagation links or to improve on the existing ones especially at VHF, UHF in the North-Central region of Nigeria, as recommended by International Telecommunication Union Recommendations (ITU-R P.453, 2013), which observed the need for local reference data on refractivity and refractivity gradients all over the world.

scholarly journals Comparison of GOME tropospheric NO<sub>2</sub> columns with NO<sub>2</sub> profiles deduced from ground-based in situ measurements

2006 ◽  
Vol 6 (11) ◽  
pp. 3211-3229 ◽  
Author(s):  
D. Schaub ◽  
K. F. Boersma ◽  
J. W. Kaiser ◽  
A. K. Weiss ◽  
D. Folini ◽  
...  
Keyword(s):  
Standard Deviation ◽  
A Priori ◽  
Relative Difference ◽  
In Situ Measurements ◽  
Data Sets ◽  
Clear Sky ◽  
Tropospheric No2 ◽  
The Mean ◽  
Sky Conditions

Abstract. Nitrogen dioxide (NO2) vertical tropospheric column densities (VTCs) retrieved from the Global Ozone Monitoring Experiment (GOME) are compared to coincident ground-based tropospheric NO2 columns. The ground-based columns are deduced from in situ measurements at different altitudes in the Alps for 1997 to June 2003, yielding a unique long-term comparison of GOME NO2 VTC data retrieved by a collaboration of KNMI (Royal Netherlands Meteorological Institute) and BIRA/IASB (Belgian Institute for Space Aeronomy) with independently derived tropospheric NO2 profiles. A first comparison relates the GOME retrieved tropospheric columns to the tropospheric columns obtained by integrating the ground-based NO2 measurements. For a second comparison, the tropospheric profiles constructed from the ground-based measurements are first multiplied with the averaging kernel (AK) of the GOME retrieval. The second approach makes the comparison independent from the a priori NO2 profile used in the GOME retrieval. This allows splitting the total difference between the column data sets into two contributions: one that is due to differences between the a priori and the ground-based NO2 profile shapes, and another that can be attributed to uncertainties in both the remaining retrieval parameters (such as, e.g., surface albedo or aerosol concentration) and the ground-based in situ NO2 profiles. For anticyclonic clear sky conditions the comparison indicates a good agreement between the columns (n=157, R=0.70/0.74 for the first/second comparison approach, respectively). The mean relative difference (with respect to the ground-based columns) is −7% with a standard deviation of 40% and GOME on average slightly underestimating the ground-based columns. Both data sets show a similar seasonal behaviour with a distinct maximum of spring NO2 VTCs. Further analysis indicates small GOME columns being systematically smaller than the ground-based ones. The influence of different shapes in the a priori and the ground-based NO2 profile is analysed by considering AK information. It is moderate and indicates similar shapes of the profiles for clear sky conditions. Only for large GOME columns, differences between the profile shapes explain the larger part of the relative difference. In contrast, the other error sources give rise to the larger relative differences found towards smaller columns. Further, for the clear sky cases, errors from different sources are found to compensate each other partially. The comparison for cloudy cases indicates a poorer agreement between the columns (n=60, R=0.61). The mean relative difference between the columns is 60% with a standard deviation of 118% and GOME on average overestimating the ground-based columns. The clear improvement after inclusion of AK information (n=60, R=0.87) suggests larger errors in the a priori NO2 profiles under cloudy conditions and demonstrates the importance of using accurate profile information for (partially) clouded scenes.

Sign in / Sign up

Export Citation Format

Share Document