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Finding Surface Reflectivity in a Coastal Area
Published: July 12, 2017
Posted: January 17, 2018

A new method effectively estimated surface reflectivity for challenging coastal conditions by using clouds as a mirror.

The Science
Surface reflectivity substantially affects the variability of the Earth’s radiation balance and is therefore a key input to earth system models.  Conventional approaches for determining surface reflectivity couple the broader spatial coverage of satellite observations with the detailed information from tower-mounted instruments.  However, accurate measurements of surface reflectivity in coastal regions with complex mixtures of distinctive surface types (land and ocean) are particularly challenging for these conventional approaches.   Tower-mounted instruments may be limited or unavailable in these regions and coarser-resolution satellite methods have difficulty with the complex surface type. Researchers from the U.S. Department of Energy (DOE) Pacific Northwest National Laboratory helped demonstrate feasibility of a new ground-based method—using clouds as a mirror—for measuring surface reflectivity in a highly heterogeneous coastal region. Such measurements are based on sunlight “bounced up” from a surface and then “bounced down” by a cloud deck.   

The Impact
Compared with conventional satellite-based and composite methods, results showed that the new ground-based method produced similar spectral signatures of surface reflectivity. Because the ground-based data required for the new method can be obtained at locations around the globe with various degrees of surface heterogeneity, the new method can provide spectral surface reflectivity at climatically important regions and is a valuable complementary tool to conventional approaches.

Surface reflectivity substantially affects the variability of the Earth’s radiation balance. This variability is sensitive to multiple natural and man-made factors, and accurate measurements from the ground, air and space are important for data sets used in atmospheric models. Assessing surface variability, however, is extremely challenging in regions with complex landscapes and different types of surface cover. In this study, researchers used an integrated data set collected during a 19-month period (June 2009-December 2010) at the DOE Atmospheric Radiation Measurement (ARM) Facility observatory on Graciosa Island in the Azores, a small archipelago off the coast of Portugal. This data set was developed using both ground-based and satellite observations. Scientists compared the areal-averaged surface reflectivity obtained by the new method to those derived from satellite observations and estimated by a conventional composite approach combining satellite- and ground-based data. Comparisons showed that the new method can effectively estimate the spectral surface reflectivity in a coastal area at spatial scales needed to validate and improve model simulations of surface reflectivity.

Contacts (BER PMs)
Ashley Williamson
Atmospheric System Research

Shaima Nasiri  
Atmospheric System Research

Sally McFarlane
Atmospheric Radiation Measurement (ARM) Facility

(PI Contact)
Evgueni Kassianov
Pacific Northwest National Laboratory

This work is supported by the Office of Biological and Environmental Research (OBER) of the US Department of Energy (DOE) as part of the Atmospheric Radiation Measurement (ARM) and Atmospheric System Research (ASR) programs. The Pacific Northwest National Laboratory (PNNL) is operated by Battelle for the DOE under contract DE-A06-76RLO 1830. The MODIS MCD43C2 v5 surface albedo BRDF data were acquired from the Land Processes (LP) Distributed Active Archive Center (DAAC), (

E. Kassianov, J. Barnard, C. Flynn, L. Riihimaki, L.K. Berg, D.A. Rutan. “Areal-Averaged Spectral Surface Albedo in an Atlantic Coastal Area: Estimation from Ground-based Transmission,” Atmosphere 8, 123 (2017). [DOI: 10.3390/atmos8070123]

Related Links
Reference Link

Topic Areas:

  • Research Area: Atmospheric System Research
  • Facility: DOE ARM User Facility

Division: SC-33.1 Earth and Environmental Sciences Division, BER


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