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| 001 | 811918 | ||
| 005 | 20240712100848.0 | ||
| 024 | 7 | _ | |a 10.5194/amt-9-3619-2016 |2 doi |
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| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Spang, Reinhold |0 P:(DE-Juel1)129154 |b 0 |e Corresponding author |
| 245 | _ | _ | |a A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2016 |b Copernicus |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the ESA Envisat satellite operated from July 2002 until April 2012. The infrared limb emission measurements represent a unique dataset of daytime and night-time observations of polar stratospheric clouds (PSCs) up to both poles. Cloud detection sensitivity is comparable to space-borne lidars, and it is possible to classify different cloud types from the spectral measurements in different atmospheric windows regions.Here we present a new infrared PSC classification scheme based on the combination of a well-established two-colour ratio method and multiple 2-D brightness temperature difference probability density functions. The method is a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption. The method has been tested in conjunction with a database of radiative transfer model calculations of realistic PSC particle size distributions, geometries, and composition. The Bayesian classifier distinguishes between solid particles of ice and nitric acid trihydrate (NAT), as well as liquid droplets of super-cooled ternary solution (STS).The classification results are compared to coincident measurements from the space-borne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument over the temporal overlap of both satellite missions (June 2006–March 2012). Both datasets show a good agreement for the specific PSC classes, although the viewing geometries and the vertical and horizontal resolution are quite different. Discrepancies are observed between the CALIOP and the MIPAS ice class. The Bayesian classifier for MIPAS identifies substantially more ice clouds in the Southern Hemisphere polar vortex than CALIOP. This disagreement is attributed in part to the difference in the sensitivity on mixed-type clouds. Ice seems to dominate the spectral behaviour in the limb infrared spectra and may cause an overestimation in ice occurrence compared to the real fraction of ice within the PSC area in the polar vortex.The entire MIPAS measurement period was processed with the new classification approach. Examples like the detection of the Antarctic NAT belt during early winter, and its possible link to mountain wave events over the Antarctic Peninsula, which are observed by the Atmospheric Infrared Sounder (AIRS) instrument, highlight the importance of a climatology of 9 Southern Hemisphere and 10 Northern Hemisphere winters in total. The new dataset is valuable both for detailed process studies, and for comparisons with and improvements of the PSC parameterizations used in chemistry transport and climate models. |
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| 700 | 1 | _ | |a Hoffmann, Lars |0 P:(DE-Juel1)129125 |b 1 |
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| 700 | 1 | _ | |a Riese, Martin |0 P:(DE-Juel1)129145 |b 7 |
| 773 | _ | _ | |a 10.5194/amt-9-3619-2016 |g Vol. 9, no. 8, p. 3619 - 3639 |0 PERI:(DE-600)2505596-3 |n 8 |p 3619 - 3639 |t Atmospheric measurement techniques |v 9 |y 2016 |x 1867-8548 |
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