scholarly journals Geostatistical merging of ground-based and satellite-derived data of surface solar radiation

2011 ◽  
Vol 6 (1) ◽  
pp. 1-5 ◽  
Author(s):  
M. Journée ◽  
C. Bertrand
Keyword(s):  
Solar Radiation ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Data Types ◽  
Surface Solar Radiation ◽  
Geostatistical Methods ◽  
Radiation Data ◽  
Single Use ◽  
Derived Data

Abstract. In this paper, we demonstrate the benefit of using observations from Meteosat Second Generation (MSG) satellites in addition to in-situ measurements to improve the spatial resolution of solar radiation data over Belgium. This objective has been reached thanks to geostatistical methods able to merge heterogeneous data types. Two geostatistical merging methods are evaluated against the interpolation of ground-data only and the single use of satellite-derived information. It results from our analysis that merging both data sources provides the most accurate mapping of surface solar radiation over Belgium.

scholarly journals Quality Control of Solar Radiation Data within the South African Weather Service Solar Radiometric Network

2018 ◽  
Author(s):  
Lucky Ntsangwane ◽  
Venkataraman Sivakumar ◽  
Brighton Mabasa ◽  
Nosipho Zwane ◽  
Katlego Ncongwane ◽  
...  
Keyword(s):  
Quality Control ◽  
Solar Radiation ◽  
South African ◽  
Surface Radiation ◽  
The South ◽  
Absolute Deviation ◽  
Surface Solar Radiation ◽  
Practical Applications ◽  
Radiation Data ◽  
Weather Service

Quality control (QC) may be a lengthy and tedious process. As a result, most data users use data from meteorological services without performing data quality checks. The South African Weather Service (SAWS) re-established the national solar radiometric network comprising of 13 new stations within the six climatic zones of the country. This study reports on the performance results of the Baseline Surface Radiation Network (BSRN) QC procedures applied to the solar radiation data within the SAWS radiometric network. The overall percentage performance of the SAWS solar radiation network based on BSRN QC methodology is 97.79%, 93.64%, 91.6% and 92.23% for Long Wave Downward Irradiance (LWD), Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively with operational problems largely dominating the percentage of bad data. The overall average performance of the Surface Solar Radiation Dataset – Heliosat (SARAH) data records for the GHI estimation for all the stations showed a Mean Bias Deviation (MBD) of -8.28 Wm-2, a Mean Absolute Deviation (MAD) of 9.06 Wm-2 and the Root Mean Square Deviation (RMSD) of 11.02 Wm-2. The correlation (quantified by R2) between ground-based and SARAH-derived GHI time series was ~ 0.98. The established network has the potential of providing high quality minute solar radiation data sets (GHI, DHI, DNI and LWD) and auxiliary hourly meteorological parameters vital for scientific and practical applications in renewable energy technologies in South Africa.

Global validation of satellite-based and reanalysis surface solar radiation data sets

2020 ◽  
Author(s):  
Jörg Trentmann ◽  
Uwe Pfeifroth ◽  
Roswitha Cremer ◽  
Martin Stengel
Keyword(s):  
Solar Radiation ◽  
Current Knowledge ◽  
Global Scale ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Data Sets ◽  
Surface Solar Radiation ◽  
Radiation Data ◽  
Surface Measurements ◽  
Reference Measurements

<p>The solar radiation reaching the Earth’s surface determines our climate and is therefore important to be monitored as consistent and complete as possible. Even though surface reference measurements of surface solar radiation are available (e.g. from the Baseline Surface Radiation Network (BSRN)), their density remains low and large areas, like the oceans, remain poorly covered. To fill the gaps in space and time, satellite-based data records (like CLARA-A2 and SARAH-2.1 from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF)) or model-based reanalysis data records (like ERA-5) are used. They provide surface solar radiation data with regional and global coverage, which are needed to understand its distribution and variability from the regional to the global scale.</p><p>Here we present a validation and analysis of monthly mean surface solar irradiance from multiple satellite-based and reanalysis data sets on the regional and global scale with reference to a data base of hundreds of surface measurements over land and ocean, collected from different sources (incl. BSRN, GEBA, WRDC, and buoy networks). This study provides new insights about the quality and uncertainty of available state-of-the-art satellite-based and reanalysis data records for climate studies. Regions of agreement as well as areas where the gridded data records exhibit larger differences are identified, providing important information on our current knowledge of the surface solar radiation climatology and possible improvements for future developments.</p>

scholarly journals Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

2016 ◽  
Vol 16 (4) ◽  
pp. 2543-2557 ◽  
Author(s):  
Wenjun Tang ◽  
Jun Qin ◽  
Kun Yang ◽  
Shaomin Liu ◽  
Ning Lu ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Temporal Resolution ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Haihe River Basin ◽  
High Temporal Resolution ◽  
Spatiotemporal Resolution ◽  
Surface Solar Radiation ◽  
Radiation Data ◽  
Cloud Parameters

Abstract. Cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) are the important inputs in estimating surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy, but their temporal resolution is too low to obtain high-temporal-resolution SSR retrievals. In order to obtain hourly cloud parameters, an artificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m−2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone) are input to the model, we can derive SSR at high spatiotemporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m−2 (or 3.5 %) and 98.5 W m−2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m−2 (or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2 W m−2 (or 19.1 %) and 22.1 W m−2 (or 12.3 %), respectively. The accuracy is comparable to or even higher than two other radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

ETL Application in Telecom Industry Based on Teradata

2013 ◽  
Vol 444-445 ◽  
pp. 1702-1707 ◽  
Author(s):  
Jing Dan Shi ◽  
Min Li ◽  
Qin He Zhang ◽  
Xuan Wei
Keyword(s):  
Data Warehouse ◽  
Conversion Efficiency ◽  
Telecommunications Industry ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Data Conversion ◽  
Data Types ◽  
Business Analysis ◽  
Telecom Industry ◽  
Analysis System

In the telecommunications industry, because of the information from different data sources, there are many discrete, uncertainty and heterogeneous data types cannot be used directly for data warehouse, there is not common ETL model. In this paper, investigation for the ETL tools in the Teradata warehouse, design a general ETL model for telecoms industry. Take the telecom business analysis system for example, verify the ETL model, prove that ETL model improve data conversion efficiency and it is good generality.

scholarly journals Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

2015 ◽  
Vol 15 (23) ◽  
pp. 35201-35236
Author(s):  
W. Tang ◽  
J. Qin ◽  
K. Yang ◽  
S. Liu ◽  
N. Lu ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Temporal Resolution ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Haihe River Basin ◽  
Bias Error ◽  
High Temporal Resolution ◽  
Surface Solar Radiation ◽  
Radiation Data ◽  
Cloud Parameters

Abstract. Cloud parameters (cloud mask, effective particle radius and liquid/ice water path) are the important inputs in determining surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy but their temporal resolution is too low to obtain high temporal resolution SSR retrievals. In order to obtain hourly cloud parameters, the Artificial Neural Network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multi-functional Transport Satellite (MTSAT) geostationary satellite signals. Meanwhile, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m-2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone and so on) are input to the model, we can derive SSR at high spatio-temporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River Basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m-2 (or 3.5 %) and 98.5 W m-2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m-2 (5.4 %); the RMSEs in daily and monthly-mean SSR estimates are 34.2 W m-2 (19.1 %) and 22.1 W m-2 (12.3 %), respectively. The accuracy is comparable or even higher than other two radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

scholarly journals Causes of Dimming and Brightening in China Inferred from Homogenized Daily Clear-Sky and All-Sky in situ Surface Solar Radiation Records (1958–2016)

2019 ◽  
Vol 32 (18) ◽  
pp. 5901-5913 ◽  
Author(s):  
Su Yang ◽  
Xiaolan L. Wang ◽  
Martin Wild
Keyword(s):  
Water Vapor ◽  
Solar Radiation ◽  
Water Vapor Pressure ◽  
Historical Records ◽  
Surface Solar Radiation ◽  
Clear Sky ◽  
The Past ◽  
Sky Conditions

AbstractThis paper presents a study on long-term surface solar radiation (SSR) changes over China under clear- and all-sky conditions and analyzes the causes of the “dimming” and “brightening.” To eliminate the nonclimatic signals in the historical records, the daily SSR dataset was first homogenized using quantile-matching (QM) adjustment. The results reveal rapid dimming before 2000 not only under all-sky conditions, but also under clear-sky conditions, at a decline rate of −9.7 ± 0.4 W m−2 decade−1 (1958–99). This is slightly stronger than that under all-sky conditions at −7.4 ± 0.4 W m−2 decade−1, since the clear-sky dimming stopped 15 years later. A rapid “wettening” of about 40-Pa surface water vapor pressure (SWVP) from 1985 to 2000 was found over China. It contributed 2.2% to the SSR decline under clear-sky conditions during the whole dimming period (1958–99). Therefore, water vapor cannot be the main cause of the long-term dimming in China. After a stable decade (1999–2008), an intensive brightening appeared under the clear-sky conditions at a rate of 10.6 ± 2.0 W m−2 decade−1, whereas a much weaker brightening (−0.8 ± 3.1 W m−2 decade−1) has been observed under all-sky conditions between 2008 and 2016. The remarkable divergence between clear- and all-sky trends in recent decades indicates that the clouds played two opposite roles in the SSR changes during the past 30 years, by compensating for the declining SSR under the cloud-free conditions in 1985–99 and by counteracting the increasing SSR under cloud-free conditions in 2008–16. Aerosols remain as the main cause of dimming and brightening over China in the last 60 years, although the clouds counteract the effects of aerosols after 2000.

scholarly journals Quality control of solar radiation data within the South African Weather Service solar radiometric network

2019 ◽  
Vol 30 (4) ◽  
pp. 51-63
Author(s):  
Lucky Ntsangwane ◽  
Brighton Mabasa ◽  
Venkataraman Sivakumar ◽  
Nosipho Zwane ◽  
Katlego Ncongwane ◽  
...  
Keyword(s):  
Quality Control ◽  
Solar Radiation ◽  
South African ◽  
Surface Radiation ◽  
The South ◽  
Absolute Deviation ◽  
Surface Solar Radiation ◽  
Practical Applications ◽  
Radiation Data ◽  
Weather Service

This study reports on the performance results of the Baseline Surface Radiation Network (BSRN) quality control procedures applied to the solar radiation data, from September 2013 to December 2017, within the South African Weather Service radiometric network. The overall percentage performance of the SAWS solar radiation network based on BSRN quality control methodology was 97.79%, 93.64%, 91.60% and 92.23% for long wave downward irradiance (LWD), global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI), respectively, with operational problems largely dominating the percentage of bad data. The overall average performance of the surface solar radiation dataset – Heliosat data records for the GHI estimation for all stations showed a mean bias deviation of 8.28 Wm-2, a mean absolute deviation of 9.06 Wm-2 and the root mean square deviation of 11.02 Wm-2. The correlation, quantified by the square of correlation coefficient (R2), between ground-based and Heliosat-derived GHI time series was ~0.98. The established network has the potential to provide high quality minute solar radiation data sets (GHI, DHI, DNI and LWD) and auxiliary hourly meteorological parameters vital for scientific and practical applications in renewable energy technologies.

scholarly journals Calibration of Ångström-Prescott Equation to Estimate Daily Solar Radiation on Rio Grande do Norte State, Brazil

2017 ◽  
Vol 32 (3) ◽  
pp. 409-416 ◽  
Author(s):  
Felipe J. de Medeiros ◽  
Claudio M. Santos e Silva ◽  
Bergson G. Bezerra
Keyword(s):  
Solar Radiation ◽  
Sunshine Duration ◽  
Rio Grande ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Space Resolution ◽  
Radiation Data ◽  
Abstract Knowledge

Abstract Knowledge of solar radiation is required for many applications. However, this atmospheric variable is not measured with an adequate space resolution. In this sense, to sites where solar radiation data are not directly measure, estimative using Ångström-Prescott equation can be used in order to provide solar radiation data, with input of sunshine duration. Thus, the objective of present study was to calibrate the Ångström-Prescott equation for different sites in Rio Grande do Norte state, Brazil. The performance of the calibrated Ångström-Prescott equation was evaluated by comparing of daily global solar radiation observed in situ. The MBE (Mean Bias Error) was lower than 1.50 MJ m-2 day-1, the Pearson's correlation coefficient about 0.90 and Willmott's index of agreement higher than 0.90, which are considered satisfactory.

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