Publications

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2017
Smith, WL, Hansen C, Bucholtz A, Anderson BE, Beckley M, Corbett JG, Cullather RI, Hines KM, Hofton M, Kato S, Lubin D, Moore RH, Rosenhaimer MS, Redemann J, Schmidt S, Scott R, Song S, Barrick JD, Blair JB, Bromwich DH, Brooks C, Chen G, Cornejo H, Corr CA, Ham SH, Kittelman AS, Knappmiller S, LeBlanc S, Loeb NG, Miller C, Nguyen L, Palikonda R, Rabine D, Reid EA, Richter-Menge JA, Pilewswskie P, Shinozuka Y, Spangenberg D, Stackhouse P, Taylor P, Thornhill KL, Van Gilst D, Winstead E.  2017.  ARCTIC RADIATION-ICEBRIDGE SEA AND ICE EXPERIMENT The Arctic Radiant Energy System during the Critical Seasonal Ice Transition. Bulletin of the American Meteorological Society. 98:1399-1426.   10.1175/bams-d-14-00277.1   AbstractWebsite

Through ARISE, NASA acquired unique aircraft data on clouds, atmospheric radiation and sea ice properties during the critical period between the sea ice minimum in late summer and autumn and the commencement of refreezing.

2012
Mulmenstadt, J, Lubin D, Russell LM, Vogelmann AM.  2012.  Cloud properties over the North Slope of Alaska: Identifying the prevailing meteorological regimes. Journal of Climate. 25:8238-8258.   10.1175/jcli-d-11-00636.1   AbstractWebsite

Long time series of Arctic atmospheric measurements are assembled into meteorological categories that can serve as test cases for climate model evaluation. The meteorological categories are established by applying an objective k-means clustering algorithm to 11 years of standard surface-meteorological observations collected from 1 January 2000 to 31 December 2010 at the North Slope of Alaska (NSA) site of the U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM). Four meteorological categories emerge. These meteorological categories constitute the first classification by meteorological regime of a long time series of Arctic meteorological conditions. The synoptic-scale patterns associated with each category, which include well-known synoptic features such as the Aleutian low and Beaufort Sea high, are used to explain the conditions at the NSA site. Cloud properties, which are not used as inputs to the k-means clustering, are found to differ significantly between the regimes and are also well explained by the synoptic-scale influences in each regime. Since the data available at the ARM NSA site include a wealth of cloud observations, this classification is well suited for model observation comparison studies. Each category comprises an ensemble of test cases covering a representative range in variables describing atmospheric structure, moisture content, and cloud properties. This classification is offered as a complement to standard case-study evaluation of climate model parameterizations, in which models are compared against limited realizations of the Earth atmosphere system (e.g., from detailed aircraft measurements).

1999
Han, W, Stamnes K, Lubin D.  1999.  Remote sensing of surface and cloud properties in the Arctic from AVHRR measurements. Journal of Applied Meteorology. 38:989-1012.   10.1175/1520-0450(1999)038<0989:rsosac>2.0.co;2   AbstractWebsite

Algorithms to retrieve cloud optical depth and effective radius in the Arctic using Advanced Very High Resolution Radiometer (AVHRR) data are developed, using a comprehensive radiative transfer model in which the atmosphere is coupled to the snowpack. For dark surfaces AVHRR channel 1 is used to derive visible cloud optical depth, while for bright surfaces AVHRR channel 2 is used. Independent inference of cloud effective radius from AVHRR channel 3 (3.75 mu m) allows for derivation cloud liquid water path (proportional to the product of optical depth and effective radius). which is a fundamental parameter of the climate system. The algorithms are based on the recognition that the reflection function of clouds at a nonabsorbing wavelength (such as AVHRR channel 1) in the solar spectrum is primarily a function of cloud optical thickness, whereas the reflection function at a liquid water absorbing wavelength (such as AVHRR channel 3) is primarily a function of cloud particle size. For water clouds over highly reflecting surfaces (snow and ice), the reflectance in AVHRR channel 1 is insensitive to cloud optical depth due to the multiple reflections between cloud base and the underlying surface; channel 2 (0.85 mu m) must be used instead for optical depth retrieval. Water clouds over tundra or ocean are more straightforward cases similar to those found at lower latitudes, and in these cases a comprehensive atmospheric radiative transfer model with a Lambertian surface under cloud is used. Thus, for water cloud over tundra and ocean, channel 1 is used for cloud optical depth retrieval. In all cases, channel 3 is used for independent retrieval of cloud droplet effective radius. The thermal component of channel 3 is estimated by making use of channel 4 (11 mu m) and is subtracted from the total channel 3 radiance. Over clear-sky scenes, the bidirectional reflectance properties of snow are calculated directly by the coupled snowpack-atmosphere model. This results in greater overall accuracy in retrieved surface properties as compared with the simplified approach that uses a Lambertian approximation for the surface albedo. To test the physical soundness of the algorithms the authors have applied them to AVHRR data over Barrow, Alaska, from April to August 1992. Downwelling irradiances at the surface calculated using the retrieved cloud optical depth and effective radius are compared with field irradiance measurements, and encouraging agreement is found. The algorithms are also applied to three areas of about 100-km dimension around Barrow, each having a different underlying surface (ocean, tundra, snow).