• Bates, T. S., P. K. Quinn, J. E. Johnson, A. Corless, F. J. Brechtel, S. E. Stalin, C. Meinig, and J. F. Burkhart, 2013: Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway using unmanned aerial systems (UAS). Atmos. Meas. Tech., 6, 21152120, https://doi.org/10.5194/amt-6-2115-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blanchet, J.-P., and R. List, 1983: Estimation of optical properties of Arctic haze using a numerical model. Atmos.–Ocean, 21, 444465, https://doi.org/10.1080/07055900.1983.9649179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Briegleb, B. P., 1992: Delta-Eddington approximate for solar radiation in the NCAR community climate model. J. Geophys. Res., 97, 76037612, https://doi.org/10.1029/92JD00291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cassano, J. J., J. A. Maslanik, C. J. Zappa, A. L. Gordon, R. I. Cullather, and S. L. Knuth, 2010: Observations of Antarctic polynya with unmanned aircraft systems. Eos, Trans. Amer. Geophys. Union, 91, 245252, https://doi.org/10.1029/2010EO280001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chung, C. E., V. Ramanathan, D. Kim, and I. A. Podgorny, 2005: Global anthropogenic aerosol direct forcing derived from satellite and ground-based observations. J. Geophys. Res., 110, D24207, https://doi.org/10.1029/2005JD006356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crocker, R. I., J. A. Maslanik, J. J. Adler, S. E. Palo, U. C. Herzfeld, and W. J. Emery, 2012: A sensor package for ice surface observations using small unmanned aircraft systems. IEEE Trans. Geosci. Remote Sens., 50, 10331047, https://doi.org/10.1109/TGRS.2011.2167339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boer, G., and Coauthors, 2016: The Pilatus unmanned aircraft system for lower atmospheric research. Atmos. Meas. Tech., 9, 18451857, https://doi.org/10.5194/amt-9-1845-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, X., and G. G. Mace, 2003: Arctic stratus cloud properties and radiative forcing from ground-based data collected at Barrow, Alaska. J. Climate, 16, 445461, https://doi.org/10.1175/1520-0442(2003)016<0445:ASCPAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hess, M., P. Koepke, and I. Schult, 1998: Optical properties of aerosols and clouds: The software package OPAC. Bull. Amer. Meteor. Soc., 79, 831844, https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodson, A., and Coauthors, 2007: A glacier respires: Quantifying the distribution and respiration CO2 flux of cryoconite across an entire Arctic supraglacial ecosystem. J. Geophys. Res., 112, G04S36, https://doi.org/10.1029/2007JG000452.

    • Search Google Scholar
    • Export Citation
  • Inoue, J., J. A. Curry, and J. A. Maslanik, 2008: Application of Aerosondes to melt-pond observations over Arctic sea ice. J. Atmos. Oceanic Technol., 25, 327334, https://doi.org/10.1175/2007JTECHA955.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Intrieri, J. M., M. D. Shupe, T. Uttal, and B. J. McCarty, 2002: An annual cycle of Arctic cloud characteristics observed by radar and lidar at SHEBA. J. Geophys. Res., 107, 8039, https://doi.org/10.1029/2000JC000423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., and Coauthors, 2011: Deployment of a tethered-balloon system for microphysics and radiative measurements in mixed-phase clouds at Ny-Ålesund and South Pole. J. Atmos. Oceanic Technol., 28, 656669, https://doi.org/10.1175/2010JTECHA1439.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindsay, R. W., and D. A. Rothrock, 1994: Arctic sea ice albedo from AVHRR. J. Climate, 7, 17371749, https://doi.org/10.1175/1520-0442(1994)007<1737:ASIAFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lubin, D., and A. S. Simpson, 1997: Measurement of surface radiation fluxes and cloud optical properties during the 1994 Arctic Ocean Section. J. Geophys. Res., 102, 42754286, https://doi.org/10.1029/96JD03215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lubin, D., and A. M. Vogelmann, 2011: The influence of mixed-phase clouds on surface shortwave irradiance during the Arctic spring. J. Geophys. Res., 116, D00T05, https://doi.org/10.1029/2011JD015761.

    • Search Google Scholar
    • Export Citation
  • Lubin, D., P. Ricchiazzi, A. Payton, and C. Gautier, 2002: Significance of multidimensional radiative transfer effects measured in surface fluxes at an Antarctic coastline. J. Geophys. Res., 107, 4387, https://doi.org/10.1029/2001JD002030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshak, A., and A. B. Davis, Eds., 2005: 3D Radiative Transfer in Cloudy Atmospheres. Springer Verlag, 701 pp.

    • Crossref
    • Export Citation
  • Marshak, A., A. B. Davis, W. J. Wiscombe, and R. F. Cahalan, 1995: Radiative smoothing in fractal clouds. J. Geophys. Res., 100, 26 42726 261, https://doi.org/10.1029/95JD02895.

    • Search Google Scholar
    • Export Citation
  • Martino, L., and J. Míguez, 2011: A generalization of the adaptive rejection sampling algorithm. Stat. Comput., 21, 633647, https://doi.org/10.1007/s11222-010-9197-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McComiskey, A., P. Ricchiazzi, C. Gautier, and D. Lubin, 2006: Assessment of a three-dimensional model for atmospheric radiative transfer over heterogeneous land cover. Geophys. Res. Lett., 33, L10813, https://doi.org/10.1029/2005GL025356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., J. O. Pinto, J. A. Curry, and G. M. McFarquhar, 2008: Sensitivity of modeled Arctic mixed-phase stratocumulus to cloud condensation and ice nuclei over regionally varying surface conditions. J. Geophys. Res., 113, D05203, https://doi.org/10.1029/2007JD008729.

    • Search Google Scholar
    • Export Citation
  • Perovich, D. K., J. A. Richter-Menge, K. F. Jones, and B. Light, 2008: Sunlight, water, and ice: Extreme Arctic sea ice melt during the summer of 2007. Geophys. Res. Lett., 35, L11501, https://doi.org/10.1029/2008GL034007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Podgorny, I. A., and D. Lubin, 1998: Biologically active insolation over Antarctic waters: Effect of a highly reflecting coastline. J. Geophys. Res., 103, 29192928, https://doi.org/10.1029/97JC02763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reineman, B. D., L. Lenain, M. S. Statom, and W. K. Melville, 2013: Development and testing of instrumentation for UAV-based flux measurements within terrestrial and marine atmospheric boundary layers. J. Atmos. Oceanic Technol., 30, 12951319, https://doi.org/10.1175/JTECH-D-12-00176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, G. C., M. V. Ramana, C. Corrigan, D. Kim, and V. Ramanathan, 2008: Simultaneous observations of aerosol-cloud-albedo interactions with three stacked unmanned aerial vehicles. Proc. Natl. Acad. Sci. USA, 105, 73707375, https://doi.org/10.1073/pnas.0710308105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, W. M., and Coauthors, 2017: Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE): The Arctic radiant energy system during the critical seasonal ice transition. Bull. Amer. Meteor. Soc., 98, https://doi.org/10.1175/BAMS-D-14-00277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tomasi, C., A., and Coauthors, 2015: Aerosol remote sensing in polar regions. Earth Sci. Rev., 140, 108157, https://doi.org/10.1016/j.earscirev.2014.11.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tschudi, M. A., J. A. Maslanik, and D. K. Perovich, 2008: Derivation of melt pond coverage on Arctic sea ice using MODIS observations. Remote Sens. Environ., 112, 26052614, https://doi.org/10.1016/j.rse.2007.12.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uttal, T., and Coauthors, 2002: Surface Heat Budget of the Arctic Ocean. Bull. Amer. Meteor. Soc., 83, 255275, https://doi.org/10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verlinde, J., and Coauthors, 2007: The Mixed-Phase Arctic Cloud Experiment. Bull. Amer. Meteor. Soc., 88, 205221, https://doi.org/10.1175/BAMS-88-2-205.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 227 116 5
PDF Downloads 190 86 14

Monte Carlo Study of UAV-Measurable Albedo over Arctic Sea Ice

View More View Less
  • 1 Arctic Research and Consulting, San Diego, California
  • | 2 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
  • | 3 Dartmouth University, Hanover, New Hampshire
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

In anticipation that unmanned aerial vehicles (UAVs) will have a useful role in atmospheric energy budget studies over sea ice, a Monte Carlo model is used to investigate three-dimensional radiative transfer over a highly inhomogeneous surface albedo involving open water, sea ice, and melt ponds. The model simulates the spatial variability in 550-nm downwelling irradiance and albedo that a UAV would measure above this surface and underneath an optically thick, horizontally homogeneous cloud. At flight altitudes higher than 100 m above the surface, an airborne radiometer will sample irradiances that are greatly smoothed horizontally as a result of photon multiple reflection. If one is interested in sampling the local energy budget contrasts between specific surface types, then the UAV must fly at a low altitude, typically within 20 m of the surface. Spatial upwelling irradiance variability in larger open water features, on the order of 1000 m wide, will remain apparent as high as 500 m above the surface. To fully investigate the impact of surface feature variability on the energy budget of the lower troposphere ice–ocean system, a UAV needs to fly at a variety of altitudes to determine how individual features contribute to the area-average albedo.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dan Lubin, dlubin@ucsd.edu

Abstract

In anticipation that unmanned aerial vehicles (UAVs) will have a useful role in atmospheric energy budget studies over sea ice, a Monte Carlo model is used to investigate three-dimensional radiative transfer over a highly inhomogeneous surface albedo involving open water, sea ice, and melt ponds. The model simulates the spatial variability in 550-nm downwelling irradiance and albedo that a UAV would measure above this surface and underneath an optically thick, horizontally homogeneous cloud. At flight altitudes higher than 100 m above the surface, an airborne radiometer will sample irradiances that are greatly smoothed horizontally as a result of photon multiple reflection. If one is interested in sampling the local energy budget contrasts between specific surface types, then the UAV must fly at a low altitude, typically within 20 m of the surface. Spatial upwelling irradiance variability in larger open water features, on the order of 1000 m wide, will remain apparent as high as 500 m above the surface. To fully investigate the impact of surface feature variability on the energy budget of the lower troposphere ice–ocean system, a UAV needs to fly at a variety of altitudes to determine how individual features contribute to the area-average albedo.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dan Lubin, dlubin@ucsd.edu
Save