Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

radfluxanal > Radiative Flux AnalysisVAP Type(s) > Baseline • Guest

sgpradflux1longc1-c2_opplot1-20140509-0000001The Radiative Flux Analysis VAP estimates clear-sky shortwave (SW) and longwave (LW) surface fluxes. Clear-sky detection and fitting techniques (Long and Ackerman 2000; Long and Turner 2008) are used to identify clear-sky periods from broadband radiometers and empirically fit functions.

The SW clear-sky detection technique uses hemispheric, broadband total- and diffuse-shortwave irradiance measurements to identify clear-sky periods using the known characteristics of typical clear-sky irradiance time series. An empirical fitting technique is used to estimate clear-sky shortwave fluxes (Long and Ackerman 2000). The LW clear-sky flux estimation technique uses the SW identified daylight clear-sky data to refine the properties needed for 24-hour-a-day identification of LW clear-sky periods. Once these refined parameters and limits are determined, then additional clear-sky periods are identified and an empirical fitting technique is again used to continuously estimate clear-sky longwave fluxes.

The VAP also uses a technique (Long et al. 2006) to infer average fractional sky cover from SW measurements for solar elevation angles 10° or greater, with an accuracy of about 10% compared to a total sky imager (TSI). Fractional sky cover is also estimated from LW fluxes using a method similar to that described in Durr and Philipona (2004). Other retrieved parameters include cloud optical depth for overcast skies (Barnard et al. 2008), cloud transmissivity, and cloud radiating temperature.


  • Fixed
  • AMF1
  • AMF2
  • AMF3

Data Details

Contact Chuck Long (deceased)
Resource(s) Data Directory
Content time range 7 January 1994 - 31 December 2022


Dong X, X Zheng, B Xi, and S Xie. 2023. "A Climatology of Midlatitude Maritime Cloud Fraction and Radiative Effect Derived from the ARM ENA Ground-Based Observations." Journal of Climate, 36(2), 10.1175/JCLI-D-22-0290.1.


Gonçalves L, S Coelho, P Kubota, and D Souza. 2022. "Interaction between cloud–radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a cloud-resolving model." Atmospheric Chemistry and Physics, 22(23), 10.5194/acp-22-15509-2022.

Liu L, J Ye, S Li, S Hu, and Q Wang. 2022. "A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data." Remote Sensing, 14(11), 10.3390/rs14112589.

Van Weverberg K and C Morcrette. 2022. "Sensitivity of Cloud‐Radiative Effects to Cloud Fraction Parametrizations in Tropical, Mid‐Latitude and Arctic Kilometre‐Scale Simulations." Quarterly Journal of the Royal Meteorological Society, 148(746), 10.1002/qj.4325.


Riihimaki L, X Li, Z Hou, and L Berg. 2021. "Improving prediction of surface solar irradiance variability by integrating observed cloud characteristics and machine learning." Solar Energy, 225, 10.1016/j.solener.2021.07.047.

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Damao Zhang
Pacific Northwest National Laboratory

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