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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Jun 2019

Research article | 12 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Toward autonomous surface-based infrared remote sensing of polar clouds: Retrievals of cloud microphysical properties

Penny M. Rowe1,2, Christopher J. Cox3,4, Steven Neshyba5, and Von P. Walden6 Penny M. Rowe et al.
  • 1NorthWest Research Associates, Redmond, WA, USA
  • 2Department of Physics, Universidad de Santiago de Chile, Santiago, Chile
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
  • 4NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, Colorado, CO, USA
  • 5Chemistry Department, University of Puget Sound, Tacoma, WA, USA
  • 6Department of Civil and Environmental Engineering, Washington State University, Pullman, WA, USA

Abstract. Improvements to climate model results in polar regions require improved knowledge of cloud microphysical properties. Surface-based infrared radiance spectrometers have been used to retrieve cloud microphysical properties in polar regions, but measurements are sparse. Reductions in cost and power requirements to allow more widespread measurements could be aided by reducing instrument resolution. Here we explore the effect of errors and instrument resolution on cloud microphysical property retrievals from downwelling infrared radiances for resolutions of 0.1 to 8 cm−1. Retrievals are tested on 331 radiance simulations characteristic of the Arctic, including mixed-phase, vertically inhomogeneous, and liquid-topped clouds and a variety of ice habits. Results indicate that measurement biases lead to biases in retrieved properties that are not represented by the retrieval error covariance matrix. Retrieval errors are high if mixed-phase is assumed throughout liquid-topped ice clouds. Errors due to assuming ice habit is spherical are progressively larger for solid columns, plates, and hollow bullet rosettes. Using retrieved cloud heights, particularly when errors are imposed, increases retrieval errors but decreases sensitivity to incorrect ice habits and vertical variation. Results indicate that retrieval accuracy is unaffected by resolution from 0.1 to 2 cm−1, after which it decreases only slightly. At a resolution of 4 cm−1, for typical errors expected in temperature (0.2 K) and water vapour (3 %), and assuming radiation bias and noise of 0.2 mW/(m2 sr cm−1), using retrieved cloud heights, error estimates are 0.1 ± 0.6 for optical depth, 0.0 ± 0.3 for ice fraction, 0 ±l 2 μm for effective radius of liquid, and 2 ± 2 μm for effective radius of ice. These results indicate that a moderately low resolution, portable, surface-based infrared spectrometer could provide microphysical properties to help constrain climate models.

Penny M. Rowe et al.
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Penny M. Rowe et al.
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Publications Copernicus
Short summary
A better understanding of polar clouds is needed for predicting climate change, including cloud thickness and the sizes and amounts of liquid droplets and ice crystals. These properties can be estimated from an instrument (an infrared spectrometer) that sits on the surface and measures how much infrared radiation is emitted by the cloud. In this work we use model data to investigate how well such an instrument could retrieve cloud properties for different instrument and error characteristics.
A better understanding of polar clouds is needed for predicting climate change, including cloud...