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.

Locations

  • Fixed
  • AMF1
  • AMF2
  • AMF3

Data Details

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

2023

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.

2022

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.

2021

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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Contact

Damao Zhang
Translator
Pacific Northwest National Laboratory

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