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 A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation

2019 ◽  
Vol 11 (4) ◽  
pp. 1905-1915 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Bias Error ◽  
Surface Solar Radiation ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products; the water vapor, surface pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimates at 10 km scale were −11.5 W m−2, 113.5 W m−2 and 0.92, respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m−2 and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The dataset is available at  https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

Variability and Trends of Surface Solar Radiation in Europe based on satellite- and surface-based data

2020 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Jörg Trentmann
Keyword(s):  
Data Center ◽  
Water Cycle ◽  
Surface Radiation ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Global Energy ◽  
Radiation Data ◽  
Climate Monitoring ◽  
Data Record ◽  
Surface Measurements

<p>The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based  high-quality climate data records, with a focus on the global energy and water cycle. The new concept of Interim Climate Data Records (ICDRs) that extent the fixed-length Climate Data Records (CDRs) into 'near-realtime' in a consistent way, enables climate monitoring at a higher level of accuracy.</p><p>It has been found in recent studies based on surface and satellite data that on average SSR has been increasing in the last 3 decades in Europe (e.g. Sanchez-Lorenzo et al. 2017, Pfeifroth et al. 2018) - especially in spring and summer. Here we use the latest SARAH-2.1 TCDR (1983-2017), potentially together with its corresponding ICDR (2018 onwards), to analyze if the found positve trends in SSR are about to continue. In this respect, the satellite-based data record will be compared and validated with surface measurements given by the Baseline Surface Radiation Network (BSRN), the  World Radiation Data Center (WRDC) and the Global Energy Balance Archive (GEBA). A reasonable line of potential reasons for the found spring and summertime brightening in Europe is discussed.</p>

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.

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.

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 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 A 16-year dataset (2000–2015) of high-resolution (3 hour, 10 km) global surface solar radiation

2019 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset with an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products, the water vapor, surface pressure and ozone from ERA5 reanalysis data, and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data was evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimate at 10 km scale were −11.5 W m−2, 113.5 W m−2, and 0.92, respectively. The error was clearly reduced when the data were upscaled to 90 km; RMSE decreased to 93.4 W m−2 and R increased to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The data set is available at https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

Comparative analysis of the incoming surface and outgoing top of atmosphere solar radiation based on MERRA-2 & CERES data

2021 ◽  
Author(s):  
Michael Stamatis ◽  
Nikolaos Hatzianastassiou ◽  
Marios Bruno Korras Carraca ◽  
Christos Matsoukas ◽  
Martin Wild ◽  
...  
Keyword(s):  
Energy Balance ◽  
Solar Radiation ◽  
Spatial Resolution ◽  
Radiant Energy ◽  
Energy System ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Reference Station ◽  
Accurate Estimation ◽  
Surface Solar Radiation

<p>The incoming solar radiation at the top of the atmosphere (TOA), and especially at the Earth’s surface, determines the energy balance of our planet and regulates its climate. During the last decades, variations in the incoming surface solar radiation (SSR) have been observed, which depend on the atmosphere’s transparency. This phenomenon, known as global dimming and brightening (GDB), plays an important role in climate change and global warming. The present study examines the variability and changes of both SSR and the outgoing solar radiation at the TOA (OSR) based on long-term satellite data and ground truth measurements, but also reanalysis data, also with an aim to inter-compare and validate the changes of SSR (ΔSSR or GDB) and OSR (ΔOSR) in order to ensure the highest accuracy of the findings. For this analysis, mean monthly SSR and OSR fluxes are used at the global scale and over the last several decades. More specifically, SSR and OSR solar fluxes are used from the Modern-Era Retrospective Analysis for Research and Applications v.2 (MERRA-2) reanalysis data for the 40-year period 01/1980 - 12/2020 and from the satellite Clouds and the Earth's Radiant Energy System Energy Balanced and Filled (CERES-EBAF) database for the 20-year period 03/2000 - 07/2020. The spatial resolution of CERES-EBAF dataset is 1°×1° latitude and longitude. MERRA-2 data, originally provided on a 0.5°×0.625° horizontal grid, are regridded on the CERES-EBAF spatial resolution (1°×1°). The SSR and ΔSSR fluxes from MERRA-2 and CERES are compared to each other, and they are both assessed through comparisons against ground measurements from the two major reference station networks, namely the Global Energy Balance Archive (GEBA), and the Baseline Surface Radiation Network (BSRN). The OSR and ΔOSR fluxes from MERRA-2 are assessed through comparison against corresponding fluxes from the CERES satellite measurements. The data analysis examines the spatio-temporal distribution and the trends of SSR (ΔSSR or GDB) and OSR, using both radiation fluxes and their deseasonalized anomalies. Special emphasis is given to the accurate estimation of GDB and the associated uncertainty, while attempting to reduce this uncertainty using the results of the analysis at the top of the atmosphere.</p>

Global and Regional Satellite-based Surface Solar Radiation data sets provided by the CM SAF

2021 ◽  
Author(s):  
Jörg Trentmann ◽  
Uwe Pfeifroth ◽  
Jaqueline Drücke ◽  
Roswitha Cremer
Keyword(s):  
Solar Radiation ◽  
Global Climate ◽  
Surface Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Climate Monitoring ◽  
Data Record ◽  
Irradiance Data ◽  
Climate Data Record

<p>The incoming surface solar radiation has been defined as an essential climate variable by GCOS. Long term monitoring of this part of the earth’s energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models.</p><p>The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving climate data records (CDRs) from geostationary and polar-orbiting satellite instruments. Within the CM SAF these CDRs are accompanied by operational data at a short time latency to be used for climate monitoring. All data from the CM SAF are freely available via www.cmsaf.eu.</p><p>Here we present the regional and global climate data records of surface solar radiation from the CM SAF. The regional SARAH-2.1 climate data record (Surface Solar Radiation Dataset – Heliosat, doi: 10.5676/EUM_SAF_CM/SARAH/V002_01) is based on observations from the series of Meteosat satellites. SARAH-2.1 provides high resolution data (temporal and spatial) of the surface solar radiation (global and direct) and the sunshine duration from 1983 to 2017 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The global climate data record CLARA (CM SAF Clouds, Albedo and Radiation dataset from AVHRR data, doi: 10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01) is based on observations from the series of AVHRR instruments onboard polar-orbiting satellites. CLARA provides daily- and monthly-averaged global data of the solar irradiance (SIS) from January 1982 to June 2019 with a spatial resolution of 0.25°. In addition to the solar surface radiation, also the longwave surface radiation as well as surface albedo and numerous cloud properties are provided in CLARA. The high accuracy and stability of these data record allows the assessment of the spatial and temporal variability and trends as well as a number of other applications that require high-resolution surface irradiance data.</p><p>Both Thematic Climate Data Records (TCDR) are accompanied and temporally-extended by consistent data records, so-called Interim Climate Data Records (ICDR), which are provided with a latency of 5 days to support applications that require more recent surface irradiance data, e.g., operational climate monitoring.</p><p>In late 2021 / early 2022 new versions of both data records, SARAH and CLARA, will be provided by the CM SAF. The quality of these data records will be improved, e.g, by a better treatment of snow-covered surfaces, and temporally extended to cover the WMO climate reference period 1991 to 2020. Here, first results of the updated data records and their improvements will be presented.</p>

scholarly journals Is global dimming and brightening in Japan limited to urban areas?

2016 ◽  
Author(s):  
Katsumasa Tanaka ◽  
Atsumu Ohmura ◽  
Doris Folini ◽  
Martin Wild ◽  
Nozomu Ohkawara
Keyword(s):  
Solar Radiation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Temporal Trends ◽  
Surface Radiation ◽  
Surface Solar Radiation ◽  
Decadal Time Scale ◽  
Global Dimming ◽  
Urban And Rural Areas

Abstract. Observations worldwide indicate secular trends of all-sky surface solar radiation on decadal time scale, termed global dimming and brightening. Accordingly, the observed surface radiation in Japan generally shows a strong decline till the end of the 1980s and then a recovery toward around 2000. Because a substantial number of measurement stations are located within or proximate to populated areas, one may speculate that the observed trends are strongly influenced by local air pollution and are thus not of large-scale significance. This hypothesis poses a serious question as to what regional extent the global dimming and brightening are significant: Are the global dimming and brightening truly global phenomena, or regional or even only local? Our study focused on 14 meteorological observatories that measured all-sky surface solar radiation, zenith transmittance, and maximum transmittance. On the basis of municipality population time series, historical land use maps, recent satellite images, and actual site visits, we concluded that eight stations had been significantly influenced by urbanization, with the remaining six stations being left pristine. Between the urban and rural areas, no marked differences were identified in the temporal trends of the aforementioned meteorological parameters. Our finding suggests that global dimming and brightening in Japan occurred on a large scale, independently of urbanization.

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