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Advances in Atmospheric Sciences

, Volume 34, Issue 11, pp 1301–1315 | Cite as

Modification of the SUNFLUX solar radiation scheme with a new aerosol parameterization and its validation using observation network data

  • Yongjian He
  • Zhi’an Sun
  • Guoping Shi
  • Jingmiao Liu
  • Jiandong Li
Original Paper
  • 85 Downloads

Abstract

SUNFLUX is a fast parameterization scheme for determination of the solar radiation at the Earth’s surface. In this paper, SUNFLUX is further modified in the treatment of aerosols. A new aerosol parameterization scheme is developed for five aerosol species. Observational data from Baseline Surface Radiation Network (BSRN), Surface Radiation Budget Network (SURFRAD) and Aerosol Robotic Network (AERONET) stations are used to evaluate the accuracy of the original and modified SUNFLUX schemes. General meteorological data are available at SURFRAD stations, but not at BSRN stations. Therefore, the total precipitable water content and aerosol data are obtained from AERONET stations. Fourteen stations are selected from both BSRN and AERONET. Cloud fraction data from MODIS are further used to screen the cloud. Ten-year average aerosol mixing ratios simulated by the CAM-chem system are used to calculate the fractions of aerosol optical depth for each aerosol species, and these fractions are further used to convert the observed total aerosol optical depth into the components of individual species for use in the evaluations. The proper treatment of multiple aerosol types in the model is discussed. The evaluation results using SUNFLUX with the new aerosol scheme, in terms of the BSRN dataset, are better than those using the original aerosol scheme under clear-sky conditions. However, the results using the SURFRAD dataset are slightly worse, attributable to the differences in the input water vapor and aerosol optical depth. Sensitivity tests are conducted to investigate the error response of the SUNFLUX scheme to the errors in the input variables.

Key words

global solar radiation SURFRAD BSRN AERONET 

摘 要

SUNFLUX 是地面太阳辐射参数化快速计算方案. 本文对 SUNFLUX 在气溶胶方面做了改进, 开发了针对 5 种气溶胶的参数化方案. 并使用了Baseline Surface Radiation Network (BSRN), Surface Radiation Budget Network (SURFRAD) 和 Aerosol Robotic Network (AERONET) 数据对改进前和改进后 SUNFLUX 方案的精度进行了评估. 由于, 常规气象数据无法在BSRN网站上获取, 需要从SURFRAD网站上获取, 而大气可降水量及气溶胶数据可从AERONET网站上获取. 本文选取了 BSRN 和 AERONET 共有的 14 个站的数据进行评估, 使用 modis 云量数据剔除云的影响, 保证数据质量. 在评估中, 使用 CAM—chem 系统模拟的 10 年平均气雾混合比例, 用于计算每种气溶胶的光学厚度比值, 并使用这些比值将观测的气溶胶光学厚度转化为需要的气溶胶光学厚度. 本文讨论了模型中各种类型的气溶胶处理方法. 评估的结果表明, 晴空条件下, 使用新的气溶胶方案的 SUNFLUX 计算的 BSRN 数据要好于原先的方案. 但是, 计算的 SURFRAD 数据结果稍差, 主要因为输入水汽和气溶胶光学厚度的差异. 同时, 我们也进行了灵敏度测试, 分析SUNFLUX误差相对于输入变量误差的响应.

关键词

太阳总辐射 SURFRAD BSRN AERONE 

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Notes

Acknowledgements

Jingmiao LIU is supported by the joint research projects entitled “Observing and Modelling Study on Spatial and Temporal Variation of Radiation Budget and PAR in Regional Scale” and “Evaluation on Detailed Agro-climatic Potential Productivity and Effects of Climate Change in Northeast China” (Grant No. CCSF201313). The BSRN data were downloaded from http://www.bsrn.awi.de/. The AERONET data were downloaded from http://aeronet.gsfc.nasa.gov/. The MODIS data were downloaded from http://modis.gsfc.nasa.gov/. The SURFRAD data were downloaded from http://www.esrl.noaa.gov/gmd/grad/surfrad/. The CAM-chem aerosol data were downloaded from ftp://ftp-ipcc.fzjuelich. de/pub/emissions/gridded netcdf/.

References

  1. Augustine, J. A., C. R. Cornwall, G. B. Hodges, C. N. Long, C. I. Medina, and J. J. DeLuisi, 2003: An automated method of MFRSR calibration for aerosol optical depth analysis with application to an Asian dust outbreak over the United States. J. Appl. Meteor., 42, 266–278, doi: 10.1175/1520-0450(2003) 042<0266:AAMOMC>2.0.CO;2.CrossRefGoogle Scholar
  2. Barnard, J. C., C. N. Long, E. I. Kassianov, S. A. McFarlane, J. M. Comstock, M. Freer, and G. M. McFarquhar, 2008: Development and evaluation of a simple algorithm to find cloud optical depth with emphasis on thin ice clouds. The Open Atmospheric Science Journal, 2, 46–55, doi: 10.2174/1874282300802010046.CrossRefGoogle Scholar
  3. Beyer, H. G., and Coauthors, 2009: D 1.1.3 report on benchmarking of radiation products. Report under contract No. 038665 of MESoR. [Available online from http://www.mesor.net/deliverables.html]Google Scholar
  4. Cusack, S., A. Slingo, J. M. Edwards, and M. Wild, 1998: The radiative impact of a simple aerosol climatology on the Hadley Centre atmospheric GCM. Quart. J. Roy. Meteor. Soc., 124, 2517–2526, doi: 10.1002/qj.49712455117.Google Scholar
  5. Dubovik, O., and Coauthors, 2006: Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. J. Geophys. Res., 111, doi: 10.1029/2005JD006619.Google Scholar
  6. Kokhanovsky A. A, B. Mayer, V. V. Rozanov, 2005: A parameterization of the diffuse transmittance and reflectance for aerosol remote sensing problems. Atmos. Res., 73, 37–43, doi: 10.1016/j.atmosres.2004.07.004CrossRefGoogle Scholar
  7. Kowalczyk, E. A., Y. P. Wang, R. M. Law, H. L. Davies, J. L. Mc-Gregor, and G. Abramowitz, 2006: The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model. CSIRO Marine and Atmospheric Research Paper 013, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, 43 pp.Google Scholar
  8. Lamarque, J.-F., and Coauthors, 2012: CAM-chem: Description and evaluation of interactive atmospheric chemistry in the Community Earth System Model. Geoscientific Model Development, 5, 369–411, doi: 10.5194/gmd-5-369-2012.CrossRefGoogle Scholar
  9. Liang, H., R. H. Zhang, J. M. Liu, Z. A. Sun, and X. H. Cheng, 2012: Estimation of hourly solar radiation at the surface under cloudless conditions on the Tibetan Plateau using a simple radiation model. Adv. Atmos. Sci., 29(4), 675–689, doi: 10.1007/s00376-012-1157-1.CrossRefGoogle Scholar
  10. Long, C. N., and K. L. Gaustad, 2004: The shortwave (SW) clearsky detection and fitting algorithm: Algorithm operational details and explanations. Atmospheric Radiation Measurement Program Technical Report., DOE/SC-ARM/TR-004.1. [Available online from http://www.arm.gov]CrossRefGoogle Scholar
  11. Lorenz, E., J. Hurka, D. Heinemann, and H. G. Beyer, 2009: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1), 2–10, doi: 10.1109/JSTARS.2009.2020300.CrossRefGoogle Scholar
  12. Manners, J., J.-C. Thelen, J. Petch, P. Hill, and J. M. Edwards, 2009: Two fast radiative transfer methods to improve the temporal sampling of clouds in numerical weather prediction and climate models. Quart. J. Roy. Meteor. Soc., 135, 457–468, doi: 10.1002/qj.385.CrossRefGoogle Scholar
  13. Mathiesen, P., and J. Kleissl, 2011: Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Solar Energy, 85(5), 967–977, doi: 10.1016/j.solener.2011.02.013.CrossRefGoogle Scholar
  14. Ohmura, A., and Coauthors, 1998: Baseline surface radiation network (BSRN/WCRP): New precision radiometry for climate research. Bull. Amer. Meteor. Soc., 79, 2115–2136, doi: 10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2.CrossRefGoogle Scholar
  15. Pérez-Ramírez, D., D. N. Whiteman, A. Smirnov, H. Lyamani, B. N. Holben, R. Pinker, M. Andrade, and L. Alados-Arboledas, 2014: Evaluation of AERONET precipitable water vapor versus microwave radiometry, GPS, and radiosondes at ARM sites. J. Geophys. Res., 119, 9596–9613, doi: 10.1002/2014 JD021730.Google Scholar
  16. Pinker, R. T., and I. Laszlo, 1992: Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteor., 31, 194–211, doi: 10.1175/1520-0450(1992)031<0194: MSSIFS>2.0.CO;2.CrossRefGoogle Scholar
  17. Platnick, S., S. A. Ackerman, M. D. King, K. Meyer, W. P. Menzel, R. E. Holz, B. A. Baum, and P. Yang, 2015: MODIS atmosphere L2 cloud product (06 L2). NASA MODIS Adaptive Processing System, Goddard Space Flight Center.Google Scholar
  18. Riahi, K., and Coauthors, 2011: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109, 33–57, doi: 10.1007/s10584-011-0149-y.CrossRefGoogle Scholar
  19. Schmithüsen, H., R. Sieger, and G. König-Langlo, 2012: BSRN toolbox V2.0—A tool to create quality checked output files from BSRN datasets and station-to-archive files. Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven, doi: 10.1594/PANGAEA.774827.Google Scholar
  20. Stackhouse, P. W., and S. K. Gupta, 2013: ISLSCP II surface radiation budget (SRB) radiation data. ORNL DAAC, Oak Ridge, Tennessee, USA, http://dx.doi.org/10.3334/ORNLDAAC/1201.Google Scholar
  21. Sun, Z. A., 1987: Climatological calculation and distributive features of mean atmospheric water content over China. Journal of Nanjing Institute of Meteorology, 10, 74–80. (in Chinese)Google Scholar
  22. Sun, Z. A., 2011: Improving transmission calculations for the Edwards-Slingo radiation scheme using a correlated kdistribution method. Quart. J. Roy. Meteor. Soc., 137, 2138–2148, doi: 10.1002/qj.880.CrossRefGoogle Scholar
  23. Sun, Z. A., and A. X. Liu, 2013: Fast scheme for estimation of instantaneous direct solar irradiance at the Earth’s surface. Solar Energy, 98, 125–137, doi: 10.1016/j.solener.2012.12.013.CrossRefGoogle Scholar
  24. Sun, Z., J. Liu, X. Zeng, and H. Liang, 2012: Parameterization of instantaneous global horizontal irradiance: Cloudy-sky component. J. Geophys. Res., 117, D14202, doi: 10.1029/2012 JD017557.CrossRefGoogle Scholar
  25. Sun, Z. A., X. N. Zeng, J. M. Liu, H. Liang, and J. Li, 2014a: Parametrization of instantaneous global horizontal irradiance: Clear-sky component. Quart. J. Roy. Meteor. Soc., 140, 267–280, doi: 10.1002/qj.2126.CrossRefGoogle Scholar
  26. Sun, Z. A., J. M. Liu, X. N. Zeng, and H. Liang, 2014b: Estimation of global and net solar radiation at the Earth surface under cloudy-sky condition. Journal of Meteorology and Environment, 30(3), 1–9. (in Chinese with English abstract)Google Scholar
  27. Takemura, T., M. Egashira, K. Matsuzawa, H. Ichijo, R. O’Ishi, and A. Abe-Ouchi, 2009: A simulation of the global distribution and radiative forcing of soil dust aerosols at the Last Glacial Maximum. Atmos. Chem. and Phys., 9, 3061–3073, doi: 10.5194/acp-9-3061-2009.CrossRefGoogle Scholar
  28. Troccoli, A., and J.-J. Morcrette, 2014: Skill of direct solar radiation predicted by the ECMWF global atmospheric model over Australia. Journal of Applied Meteorology and Climatology, 53, 2571–2588, doi: 10.1175/JAMC-D-14-0074.1.CrossRefGoogle Scholar
  29. Wang, B. Z., and Y. B. Shen, 2012: Atmospheric vapor content over China and its climatological evaluation method. Journal of Applied Meteorological Science, 23, 763–768, doi: 10.3969/j.issn.1001-7313.2012.06.014. (in Chinese)Google Scholar
  30. Ward, J., 2016: Australian solar energy forecasting system final report: Project results and lessons learned. Australian Renewable Energy Agency (ARENA). [Available online from http://arena.gov.au/project/australian-solar-energyforecasting-system-asefs-phase-1]Google Scholar
  31. WMO, 1983: Radiation commission of IAMAP meeting of experts on aerosol and their climatic effects. WMO Rep. WCP55, Geneva, Switzerland.Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yongjian He
    • 1
  • Zhi’an Sun
    • 2
  • Guoping Shi
    • 1
  • Jingmiao Liu
    • 3
    • 4
  • Jiandong Li
    • 5
  1. 1.Institute of Geographic and Remote SensingNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Environment & Research DivisionAustralian Bureau of MeteorologyMelbourneAustralia
  3. 3.Institute of Atmospheric EnvironmentChina Meteorological AdministrationShenyangChina
  4. 4.Chinese Academy of Meteorological SciencesBeijingChina
  5. 5.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

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