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Abstract. Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO2 slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than ∼ 0.3, cloud-height retrievals from simulated spectra using CO2 slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5cm−1, mean biases are found to be ∼ 0.2km for clouds with bases below 2 and −0.2km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors in the atmospheric state as well as both instrument noise and bias of 0.2mW/(m2srcm−1), at a resolution of 4cm−1, average retrieval errors are found to be less than ∼ 0.5km for cloud bases within 1km of the surface, increasing to ∼ 1.5km at 4km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.
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Polarization enhancement of passive SWIR cloud thermodynamic phase remote sensing
Determining whether a cloud is composed of spherical water droplets of polyhedral ice crystals (i.e., the thermodynamic phase) from a passive remote sensing instrument is very difficult because of the immense variety of clouds and their highly variable microphysical properties. To improve upon the popular method of radiance ratios, we enhance the classification ability by adding polarimetric sensitivity to an instrument that measures radiance in three short-wave infrared bands. Clouds typically induce a polarization signature on the order of a percent, and so sensitive optics are required for accurate classification. In this paper, we present the combination of spectral and polarimetric sensitivity for cloud thermodynamic phase classification using data from a ground-based, 3-band, short-wave infrared polarimeter and cloud-phase validation from a dual-polarization lidar. We then analyze the classification quality of various methods using surface-fitting techniques to show that the addition of polarimetry is advantageous for cloud classification.
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- Award ID(s):
- 1757351
- PAR ID:
- 10159558
- Date Published:
- Journal Name:
- Proc. SPIE 11132
- Volume:
- 1113206
- Page Range / eLocation ID:
- 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2530128
