Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-2139-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-2139-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 May 2023
Data description paper |
| 25 May 2023
| 25 May 2023
Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Wouter H. Knap
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Chiel C. van Heerwaarden
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
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Cited articles
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434, https://doi.org/10.5194/essd-9-415-2017, 2017. a, b
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018. a
Durand, M., Murchie, E. H., Lindfors, A. V., Urban, O., Aphalo, P. J., and
Robson, T. M.: Diffuse solar radiation and canopy photosynthesis in a
changing environment, Agr. Forest Meteorol., 311, 108684,
https://doi.org/10.1016/j.agrformet.2021.108684, 2021. a
Ehrlich, A. and Wendisch, M.: Reconstruction of high-resolution time series from slow-response broadband terrestrial irradiance measurements by deconvolution, Atmos. Meas. Tech., 8, 3671–3684, https://doi.org/10.5194/amt-8-3671-2015, 2015. a
Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., and Werscheck, M.: CLAAS-2: CM SAF CLoud property dAtAset using SEVIRI – Edition 2, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002, 2016. a
Gristey, J. J., Feingold, G., Glenn, I. B., Schmidt, K. S., and Chen, H.:
Surface Solar Irradiance in Continental Shallow Cumulus Fields:
Observations and Large-Eddy Simulation, J. Atmos. Sci., 77,
1065–1080, https://doi.org/10.1175/JAS-D-19-0261.1, 2020. a, b
Gschwind, B., Wald, L., Blanc, P., Lefèvre, M., Schroedter-Homscheidt, M., and
Arola, A.: Improving the McClear model estimating the downwelling solar
radiation at ground level in cloud-free conditions – McClear-v3, metz,
28, 147–163, https://doi.org/10.1127/metz/2019/0946, 2019. a
Gueymard, C. A.: Cloud and albedo enhancement impacts on solar irradiance using
high-frequency measurements from thermopile and photodiode radiometers.
Part 1: Impacts on global horizontal irradiance, Sol. Energy, 153,
755–765, https://doi.org/10.1016/j.solener.2017.05.004, 2017. a, b, c, d
Helbig, M., Gerken, T., Beamesderfer, E. R., Baldocchi, D. D., Banerjee, T.,
Biraud, S. C., Brown, W. O. J., Brunsell, N. A., Burakowski, E. A., Burns,
S. P., Butterworth, B. J., Chan, W. S., Davis, K. J., Desai, A. R., Fuentes,
J. D., Hollinger, D. Y., Kljun, N., Mauder, M., Novick, K. A., Perkins,
J. M., Rahn, D. A., Rey-Sanchez, C., Santanello, J. A., Scott, R. L.,
Seyednasrollah, B., Stoy, P. C., Sullivan, R. C., de Arellano, J. V.-G.,
Wharton, S., Yi, C., and Richardson, A. D.: Integrating continuous
atmospheric boundary layer and tower-based flux measurements to advance
understanding of land-atmosphere interactions, Agr. Forest
Meteorol., 307, 108509, https://doi.org/10.1016/j.agrformet.2021.108509, 2021. a
Jakub, F. and Mayer, B.: A three-dimensional parallel radiative transfer model
for atmospheric heating rates for use in cloud resolving models – The
TenStream solver, J. Quant. Spectrosc. Ra., 163, 63–71, https://doi.org/10.1016/j.jqsrt.2015.05.003, 2015. a
Kipp & Zonen: CH1 Pyrheliometer Instruction Manual, https://www.kippzonen.com/Download/42/CH-1-Pyrheliometer-Manual-English (last access: 26 July 2022),
2001. a
Kipp & Zonen: CM22 precision pyranometer instruction manual, https://www.kippzonen.com/Download/55/CM-22-Pyranometer-Manual (last access: 26 July 2022), 2004. a
Kivalov, S. N. and Fitzjarrald, D. R.: Quantifying and Modelling the Effect
of Cloud Shadows on the Surface Irradiance at Tropical and
Midlatitude Forests, Bound.-Lay. Meteorol., 166, 165–198,
https://doi.org/10.1007/s10546-017-0301-y, 2018. a, b
Knap, W.: Basic and other measurements of radiation at station Cabauw (2005-02
et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940531, 2022. a, b
Knap, W. H. and Mol, W. B.: High resolution solar irradiance variability
climatology dataset part 1: direct, diffuse, and global irradiance, Zenodo [data set],
https://doi.org/10.5281/zenodo.7093164, 2022. a, b, c
KNMI: Cloud cover retrieved from infrared measurements at 10 minute intervals at CESAR observatory, KNMI [data set], https://dataplatform.knmi.nl/dataset/cesar-nubiscope-cldcov-la1-t10-v1-0, last access: 16 May 2023. a
Liang, X.: Emerging Power Quality Challenges Due to Integration of
Renewable Energy Sources, IEEE T. Ind. Appl.,
53, 855–866, https://doi.org/10.1109/TIA.2016.2626253, 2017. a
Lohmann, G. M.: Irradiance Variability Quantification and Small-Scale
Averaging in Space and Time: A Short Review, Atmosphere, 9, 264,
https://doi.org/10.3390/atmos9070264, 2018. a
Lohmann, G. M., Monahan, A. H., and Heinemann, D.: Local short-term variability in solar irradiance, Atmos. Chem. Phys., 16, 6365–6379, https://doi.org/10.5194/acp-16-6365-2016, 2016. a
Mercado, L. M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild,
M., and Cox, P. M.: Impact of changes in diffuse radiation on the global land
carbon sink, Nature, 458, 1014–1017, https://doi.org/10.1038/nature07949, 2009. a
Mol, W.: Code for 1 Hz solar irradiance processing and variability analyses, Zenodo [code], https://doi.org/10.5281/zenodo.7851741, 2023. a
Mol, W. and Heusinkveld, B.: Radiometer grid at Falkenberg and surroundings,
downwelling shortwave radiation, FESSTVaL campaign, Universität Hamburg [data set],
https://doi.org/10.25592/uhhfdm.10273, 2022. a
NOAA: Climate Algorithm Theoretical Basis Document, Tech. Rep.,
https://ncei.noaa.gov/pub/data/sds/cdr/CDRs/Cloud_Properties-ISCCP/AlgorithmDescription_01B-29.pdf (last access: 16 May 2023),
2022. a
Pearcy, R. W. and Way, D. A.: Two decades of sunfleck research: looking back to
move forward, Tree Physiol., 32, 1059–1061, https://doi.org/10.1093/treephys/tps084,
2012. a
Tabar, M. R. R., Anvari, M., Lohmann, G., Heinemann, D., Wächter, M., Milan,
P., Lorenz, E., and Peinke, J.: Kolmogorov spectrum of renewable wind and
solar power fluctuations, Eur. Phys. J. Spec. Top., 223, 2637–2644,
https://doi.org/10.1140/epjst/e2014-02217-8, 2014. a, b, c
Tijhuis, M., van Stratum, B., van Heerwaarden, C., and Veerman, M.: An
Efficient Parameterization for Surface Shortwave 3D Radiative
Effects in Large-Eddy Simulations of Shallow Cumulus Clouds, ESS Open Archive [preprint],
https://doi.org/10.1002/essoar.10511758.2, 2022.
a
van Heerwaarden, C. C., Mol, W. B., Veerman, M. A., Benedict, I., Heusinkveld,
B. G., Knap, W. H., Kazadzis, S., Kouremeti, N., and Fiedler, S.: Record high
solar irradiance in Western Europe during first COVID-19 lockdown
largely due to unusual weather, Communications Earth & Environment, 2, 37,
https://doi.org/10.1038/s43247-021-00110-0, 2021. a
van Stratum, B., van Heerwaarden, C. C., and Vilà-Guerau de Arellano, J.: The
Benefits and Challenges of Downscaling a Global Reanalysis with
Doubly-Periodic Large-Eddy Simulations, ESS Open Archive [preprint],
https://doi.org/10.22541/essoar.168167362.25141062/v1, 2023. a
Veerman, M. A., van Stratum, B. J. H., and van Heerwaarden, C. C.: A case study
of cumulus convection over land in cloud-resolving simulations with a coupled
ray tracer, Geophys. Res. Lett., 49, e2022GL100808,
https://doi.org/10.1029/2022GL100808, 2022. a, b, c
Wauben, W., Bosveld, F., and Baltink, H. K.: Laboratory and Field Evaluation of the NubiScope
Conference: WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation, TECO-2010, WMO, Helsinki, https://library.wmo.int/pmb_ged/wmo-td_1546_en/1_7_Waubenetal_Netherlands.pdf (last access: 16 May 2023), 2010. a, b, c
WMO: Chapter 8 – Measurement of sunshine duration,
https://library.wmo.int/index.php?lvl=notice_display&id=19664 (last access: 6 July 2022), 2014. a
Yang, D., Wang, W., Gueymard, C. A., Hong, T., Kleissl, J., Huang, J., Perez,
M. J., Perez, R., Bright, J. M., Xia, X., van der Meer, D., and Peters,
I. M.: A review of solar forecasting, its dependence on atmospheric sciences
and implications for grid integration: Towards carbon neutrality, Renew. Sust. Energ. Rev., 161, 112348,
https://doi.org/10.1016/j.rser.2022.112348, 2022. a
Yordanov, G. H.: A study of extreme overirradiance events for solar energy
applications using NASA’s I3RC Monte Carlo radiative transfer
model, Sol. Energy, 122, 954–965, https://doi.org/10.1016/j.solener.2015.10.014,
2015. a
Yordanov, G. H., Saetre, T. O., and Midtgård, O.: 100-millisecond Resolution
for Accurate Overirradiance Measurements, IEEE J.
Photovolt., 3, 1354–1360, https://doi.org/10.1109/JPHOTOV.2013.2264621, 2013. a
Yordanov, G. H., Saetre, T. O., and Midtgård, O.-M.: Extreme overirradiance
events in Norway: 1.6 suns measured close to 60∘ N, Sol. Energy, 115,
68–73, https://doi.org/10.1016/j.solener.2015.02.020, 2015. a
Short summary
We describe a dataset of detailed measurements of sunlight reaching the surface, recorded at a rate of one measurement per second for 10 years. The dataset includes detailed information on direct and scattered sunlight; classifications and statistics of variability; and observations of clouds, atmospheric composition, and wind. The dataset can be used to study how the atmosphere influences sunlight variability and to validate models that aim to predict this variability with greater accuracy.
We describe a dataset of detailed measurements of sunlight reaching the surface, recorded at a...
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