A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra

Abstract

Abstract. 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 MI-PAS 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.

A decadal satellite record of gravity wave activity in the lower stratosphere to study polar stratospheric cloud formation

“…The detection and classification results were compared with space-borne lidar observations. Spang et al (2016) showed that ice particles are classified most accurately with the new method. Our survey of gravity-wave-induced PSC formation events is based on daily maps of detrended and noise-corrected AIRS 15 µm brightness temperature variances and MIPAS PSC detections.…”

“…A particular advantage of MIPAS was its sensitivity to low particle concentrations due to the long integration paths of the limb observation geometry. Here, we make use of a new MIPAS PSC data product introduced by Spang et al (2016), which uses radiance measurements at 7.1, 8.2, 10.5, 12.0, and 12.7 µm to detect PSCs and to classify different particle types. The new method has been developed and tested with a database of radiative transfer model calculations of realistic PSC particle size distributions, geometries, and composition.…”

“…MIPAS PSC detections are shown for the altitude range of about 18-22 km (450-550 K potential temperature), which coincides with the range of polar vortex temperature minima (Randel et al, 2004) and PSC occurrence frequency maxima (Poole and Pitts, 1994;Spang et al, 2005;Pitts et al, 2009) during the course of the polar winter. To help link the PSC observations to gravity wave activity, further information was added to the maps following Spang et al (2016). PSC existence temperatures were estimated following Hanson and Mauersberger (1988) and Marti and Mauersberger (1993) based on ERA-Interim pressure and temperature data at the 500 K isentropic level as well as typical stratospheric values for HNO 3 (9 ppbv) and H 2 O (4 ppmv).…”

Infrared limb emission measurements of aerosol in the troposphere and stratosphere

“…A8b shows that the measurements above the separation lines disappear, except for a few PSC detections south of 60 • N over Siberia and some aerosol in the tropics. The fact that the BTD correlation used here for filtering out ice clouds also improves the discrimination between ice and non-ice PSCs has been investigated in a separate study by Spang et al (2016). …”

A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

“…Section 2 introduces MIPAS and other involved instruments. Section 3 provides a summary of the MIPAS detection and classification method -more extensively presented in Spang et al (2016) -is given in conjunction with a comparison to CALIPSO and plausibility tests for the results of the classification approach. Section 4 describes in detail various aspects of the climatology, like temporal and spatial evolution of the PSC type distribution.…”