Baseline Surface Radiation Network (BSRN): structure and data
description (1992–2017)

Driemel, Amelie; Augustine, John; Behrens, Klaus; Colle, Sérgio; Cox, Christopher; Cuevas, E.; Denn, Fred M; Duprat, Thierry; Fukuda, Masato; Grobe, Hannes; Haeffelin, Martial; Hyett, Nicole; Ijima, Osamu; Kallis, Ain; Knap, Wouter; Kustov, Vasilii; Long, Charles; Longenecker, David; Lupi, Angelo; Maturilli, Marion; Mimouni, Mohamed; Ntsangwane, Lucky; Ogihara, Hiroyuki; Olano, Xabier; Olefs, Marc; Omori, Masao; Passamani, Lance; Pereira, Ênio Bueno; Schmithüsen, Holger; Schumacher, Stefanie; Sieger, Rainer; Tamlyn, Jonathan; Vogt, Roland; Vuilleumier, Laurent; Xia, Xiangao; Ohmura, Atsumu; König-Langlo, Gert

doi:10.5194/essd-2018-8

Cited by 45 publications

(62 citation statements)

References 9 publications

(11 reference statements)

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“…Three sites showed lower negative nighttime values than the extremely rare minimum limits (SBO, SOV, TAM). This issue which is related to the thermal offset due to longwave loss of the instrument has been often reported (Driemel et al, ; Dutton et al, ). To cope with these issues, we set negative values <−10 to missing.…”

Section: Datamentioning

confidence: 88%

“…The BSRN provides high‐quality downwelling radiation observations at 1‐min resolution for more than 50 sites across the globe (Driemel et al, ; Ohmura et al, ). Here we use the majority of sites (54) for which monthly clear‐sky fluxes are available from the standard method as used by Wild et al ().…”

Section: Datamentioning

confidence: 99%

“…Their comparison was based on 54 Baseline Surface Radiation Network (BSRN) stations across the globe. These measurements follow a strict protocol and include direct radiation measured with pyrheliometers, as well as diffuse radiation and the total incoming solar radiation (or global radiation) measured with pyranometers at high temporal resolution (minute data; Driemel et al, ; Ohmura et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…In order to test and validate the proposed methodology, we use data from the BSRN network with a sparse but global coverage from the tropics to the poles. The BSRN network provides 1‐min data of diffuse and global shortwave radiation (Driemel et al, ). Reference clear‐sky fluxes are computed by the standard method of Long and Ackerman ().…”

Section: Introductionmentioning

confidence: 99%

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Estimating Shortwave Clear‐Sky Fluxes From Hourly Global Radiation Records by Quantile Regression

Renner

Wild

Schwarz

et al. 2019

Earth and Space Science

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Estimates of radiative fluxes under cloud‐free conditions (“clear‐sky”) are required in many fields, from climatic analyses of solar transmission to estimates of solar energy potential for electricity generation. Ideally, these fluxes can be obtained directly from measurements of solar fluxes at the surface. However, common standard methods to identify clear‐sky conditions require observations of both the total and the diffuse radiative fluxes at very high temporal resolution of minutes, which restricts these methods to a few, well‐equipped sites. Here we propose a simple method to estimate clear‐sky fluxes only from typically available global radiation measurements (Rsd) at (half‐)hourly resolution. Plotting a monthly sample of observed Rsd against the corresponding incoming solar radiation at the top of atmosphere (potential solar radiation) reveals a typical triangle shape with clear‐sky conditions forming a distinct, linear slope in the upper range of observations. This upper slope can be understood as the fractional transmission of solar radiation representative for cloud‐free conditions of the sample period. We estimate this upper slope through quantile regression. We employ data of 42 stations of the worldwide Baseline Surface Radiation Network to compare our monthly estimates with the standard clear‐sky identification method developed by Long and Ackerman (2000, https://doi.org/10.1029/2000JD900077). We find very good agreement of the derived fractional solar transmission (R2 = 0.73) across sites. These results thus provide confidence in applying the proposed method to the larger set of global radiation measurements to obtain further observational constraints on clear‐sky fluxes and cloud radiative effects.

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Section: Datamentioning

confidence: 88%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Shortwave Clear‐Sky Fluxes From Hourly Global Radiation Records by Quantile Regression

Renner

Wild

Schwarz

et al. 2019

Earth and Space Science

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“…The Cloud_cci AVHRR-PMv3 BOA radiative fluxes SWF down BOA and LWF down BOA were compared against ground-based reference stations of the World Radiation Monitoring Center (WRMC) Baseline Surface Radiation Network (BSRN, Driemel et al, 2018). For this, monthly mean BSRN SWF down BOA and LWF down BOA values were calculated per station from all available observations and then compared to the nearest neighbouring Cloud_cci grid box.…”

Section: Boa Radiative Fluxes 25mentioning

confidence: 99%

Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

Stengel

Stapelberg

Sus

et al. 2019

Preprint

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Abstract. We present version 3 of the Cloud_cci AVHRR-PM dataset which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from Advanced Very High Resolution Radiometer (AVHRR) measurements recorded by the afternoon (post meridiem, PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling, shortwave and longwave broadband fluxes at the surface (bottom-of-atmosphere - BOA) and top-of-atmosphere (TOA). All fluxes were determined at AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) show a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at BOA and TOA. The Cloud_cci AVHRR-PM version 3 dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a Digital Object Identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

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Newly collected data across Alaska reveal remarkable biases in solar radiation products

Wang

Clow

2020

Intl Journal of Climatology

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Data on surface solar radiation are scarce in high-latitude regions, and few studies have evaluated the performance of reanalysis products in estimating solar radiation in those regions. Here, an extensive solar radiation dataset is compiled from 98 stations across Alaska to evaluate 11 different surface solar radiation products (seven reanalysis and four observation-derived). No product can capture all aspects of the ground-based observations, and there is ample room for improvement; root mean square errors (RMSEs) of daily, monthly, and annual average comparisons of the products against observations are 38-65, 19-39, and 11-17 WÁm −2 , respectively. ERA5, MERRA2, and ERA-Interim performed the best in Alaska. Daily records from all products show large RMSEs of 60-108 WÁm −2 during May-July, equivalent to 30-55% of the observed solar radiation during this season. The sparseness of Alaskan observations, cloud cover, and algorithm issues may be potential sources of bias.

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Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017)

Cited by 45 publications

References 9 publications

Estimating Shortwave Clear‐Sky Fluxes From Hourly Global Radiation Records by Quantile Regression

Estimating Shortwave Clear‐Sky Fluxes From Hourly Global Radiation Records by Quantile Regression

Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

Newly collected data across Alaska reveal remarkable biases in solar radiation products

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