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Dan Lubin

Abstract

The longwave radiation environment of the Antarctic Peninsula and Southern Ocean has been investigated using radiometric Fourier Transform Infrared (FTIR) measurements of atmospheric emission in conjunction with detailed radiative transfer theory. The California Space Institute FTIR Spectroradiometer was deployed at Palmer Station, Antarctica (64°46′S, 64°04′W), where it made zenith sky emission measurements several times daily between 25 August 1991 and 17 November 1991. Emission spectra covered the entire middle infrared (5–20 µm) with one inverse centimeter spectral resolution. For FTIR data obtained under cloudy skies, a least-squares algorithm is used to match the emission spectra with discrete-ordinate radiative transfer calculations that are based on marine cloud microphysics. This algorithm provides a determination of cloud emissivity, and useful estimates of cloud optical depth and equivalent radius of the droplet size distribution.

Temperatures in the lower troposphere between 259 K and 273 K diminish the radiative importance of water vapor and enhance the importance of clouds and C02 relative to midlatitudes. Springtime variability in stratospheric temperature and ozone abundance has a small but noticeable impact of about 1.0 W m−2 on surface longwave flux under clear skies. The mid-IR window emissivities of low stratiform clouds are most often between 0.90 and 0.98, with few as large as unity. Most low stratiform clouds appear to have moderate mid-IR optical depth (5–10), but relatively large equivalent radius (9–11 µm). However, clouds with base height between 1 and 2 km have noticeably smaller emissivities and optical depths. The emissivity of maritime antarctic clouds is determined to be smaller for a given liquid water path than the parameterization used in the NCAR Community Climate Model (CCM 1), and an appropriate mass absorption coefficient for antarctic clouds is 0.065 m2g−1 for the mid-IR window.

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Dan Lubin and Esther Morrow

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A cloud classification method that uses both multispectral and textural features with a maximum likelihood discriminator is applied to full-resolution AVHRR (Advanced Very High Resolution Radiometer) data from 100 NOAA polar-orbiter overpasses tracked from an icebreaker during the 1994 Arctic Ocean Section. The cloud classification method is applied to the 32 × 32 pixel cell centered about the ship’s position during each overpass. These overpasses have matching surface weather observations in the form of all-sky photographs or, during a period of heavy weather, an objective record that the sky was overcast with low water clouds. The cloud classifications from the maximum likelihood method are compared with the surface weather observations to determine if the automated satellite cloud classifier actually produces realistic descriptions of the scene. These comparisons are favorable in most cases, with the exception of a frequent error in which the classifier confuses Ci/Cc/Ac with extensive low water clouds over sea ice. This overall evaluation does not change appreciably if global area coverage resolution is used instead of full resolution or if the authors attempt to recalibrate the data to the NOAA-7 data for which the algorithm was originally developed. The authors find that the Ci/Cc/Ac cloud error can usually be avoided by 1) modifying the textural feature values for some cloud-over-ice categories and 2) applying a threshold value of 30% to the AVHRR channel 2 albedo averaged over the cell (and normalized by the cosine of the solar zenith angle). For a cell that the classifier identifies as containing Ci/Cc/Ac over sea ice, a cell-average channel 2 albedo greater than 30% usually indicates that the cell instead contains extensive low water clouds. When compared to the surface weather observations, the skill score of the satellite cloud classifier thus modified is 81%, which is very close to that claimed by its original author. This study suggests that satellite cloud detection and classification schemes based on both spectral signatures and texture recognition may indeed yield realistic results.

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Dan Lubin and John E. Frederick

Abstract

The National Science Foundation scanning spectroradiometer at Palmer Station, Antarctica (64°46′S, 64°04′W) provides hourly ground-based measurements of solar ultraviolet (UV) irradiance at the, earth's surface. These measurements define the UV radiation environment of the region and, in conjunction with a daily record of sky conditions and radiative transfer modeling, permit a quantitative understanding of the role of cloud cover in regulating UV radiation levels at the Antarctic surface, including the period of the springtime ozone depletion. The transmission properties of cloud types over the Antarctic Peninsula are quantified by taking the ratio of UV-A irradiances measured under them to UV-A irradiances calculated for clear skies and the same solar zenith angle, and the results are then generalized to the UV-B. Under the averse overcast sky in the region, UV irradiance at all wavelengths is slightly greater than half of the value for clear skies. Under the thickest overcast layers, UV irradiance at all wavelengths is roughly 20% what it would be if the sky were clear. In a seasonally averaged sense cloudiness has no effect on the percentage enhancement in UV-B surface irradiance that results from the springtime ozone depletion. However, when considering time scales of hours to several days, an increase in cloud cover can be discussed in terms of its ability to attenuate the solar irradiance; in some cases giving a surface UV-B level comparable to that found under an unperturbed ozone column and clear skies. Depending on the amount of ozone depletion and the type of cloud cover, there will always be a wavelength below which surface radiation levels are excessive during spring.

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Dan Lubin and D. A. Harper

Abstract

Over the Antarctic plateau, the radiances measured by the AVHRR middle infrared (11 and 12 μm) channels are shown to depend on effective cloud temperature, emissivity, ice water path, and effective radius of the particle size distribution. The usefulness of these dependencies is limited by radiometric uncertainties of up to 2 K in brightness temperature and by the fact that the radiative transfer solutions are not single valued over all possible ranges of temperature, effective radius, and ice water path. Despite these limitations. AVHRR imagery can be used to characterize cloud optical properties over the Antarctic continent if surface weather observations and/or radiosonde data can be collocated with the satellite overpasses. From AVHRR imagery covering the South Pole during 1992, the mean cloud emissivity is estimated at 0.43 during summer and 0.37 during winter, while the mean summer and winter effective radii are estimated at 12.3 and 5.6 μm, respectively. When a radiative transfer model is used to evaluate these results in comparison with surface pyrgeometer measurements, the comparison suggests that the AVHRR retrieval method captures the overall seasonal behavior in cloud properties. During months when the polar vortex persists, AVHRR infrared radiances may be noticeably influenced by polar stratospheric clouds.

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Dan Lubin and Paul G. Weber

Abstract

The bidirectional reflectance distribution function (BRDF) of an overcast atmosphere above an ocean surface has been calculated as a function of wavelength using a discrete-ordinates radiative transfer model. This plane-parallel BRDF appears qualitatively similar to the empirically derived angular dependence models from the Earth Radiation Budget Experiment. But when these two different BRDFs are used to estimate net shortwave flux at the ocean surface, discrepancies of 20–60 W m−2 can occur between the respective net surface flux estimations. When using either BRDF with Advanced Very High Resolution Radiometer data for surface radiation budget estimation, this uncertainty can be minimized by restricting the satellite viewing (polar) angle to between 30° and 50°. Accurate measurements of the planetary BRDF would help resolve these differences.

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Igor Podgorny, Dan Lubin, and Donald K. Perovich

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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.

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W. Han, K. Stamnes, and Dan Lubin

Abstract

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 μm) 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 μm) 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 μm) 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).

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Xiaozhen Xiong, Knut Stamnes, and Dan Lubin

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A method is presented for retrieving the broadband albedo over the Arctic Ocean using advanced very high resolution radiometer (AVHRR) data obtained from NOAA polar-orbiting satellites. Visible and near-infrared albedos over snow and ice surfaces are retrieved from AVHRR channels 1 and 2, respectively, and the broadband shortwave albedo is derived through narrow-to-broadband conversion (NTBC). It is found that field measurements taken under different conditions yield different NTBC coefficients. Model simulations over snow and ice surfaces based on rigorous radiative transfer theory support this finding. The lack of a universal set of NTBC coefficients implies a 5%–10% error in the retrieved broadband albedo. An empirical formula is derived for converting albedo values from AVHRR channels 1 and 2 into a broadband albedo under different snow and ice surface conditions. Uncertain calibration of AVHRR channels 1 and 2 is the largest source of uncertainty, and an error of 5% in satellite-measured radiance leads to an error of 5%–10% in the retrieved albedo. NOAA-14 AVHRR data obtained over the Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp are used to derive the seasonal variation of the surface albedo over the Arctic Ocean between April and August of 1998. Comparison with surface measurements of albedo by Perovich and others near the SHEBA ice camp shows very good agreement. On average, the retrieval error of albedo from AVHRR is 5%–10%.

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Remote Sensing of Polar Regions

Lessons and Resources for the International Polar Year

Dan Lubin, Gabrielle Ayres, and Steven Hart

Polar researchers have historically been innovative and adaptive users of satellite remote sensing data, and their experiences can suggest ways to enhance the use of remote sensing throughout the climate sciences. We performed a semistructured survey of the polar research community on the use of remote sensing at the beginning of the NASA Earth Observing System (EOS) era. For the most part, remote sensing plays a supporting but critical role in the research as described by the respondents. Data acquisition and analysis is mostly at the home institution, with field telemetry appearing in a small minority of responses. Most polar researchers have not had formal training in remote sensing, but they have adapted and trained themselves very thoroughly. Although a significant number of polar researchers are content with visual inspection of satellite images, a roughly equal number develop their own algorithms for derivation of geophysical products, and more have become adept at using high-level graphical programming languages to work with data. Given the self-sufficiency in remote sensing training that characterizes polar researchers, nontraditional satellite data users (e.g., life scientists) tend to view the "learning curve" as steep, as compared with physical scientists. Although up to a third of respondents report no significant obstacles in accessing satellite data, obstacles such as a) difficulty locating data centers for their needs, b) the cost of acquiring data, and c) insider or restricted access to data were each reported by about one-quarter of the respondents. The major ongoing challenges with remote sensing in polar research can be met with aspects of modern cyberinfrastructure involving data interoperability.

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Johannes Mülmenstädt, Dan Lubin, Lynn M. Russell, and Andrew M. Vogelmann

Abstract

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).

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