Volcanic ash detection with infrared limb sounding: MIPAS observations and radiative transfer simulations

Grießbach, Sabine; Hoffmann, Lars; Spang, Reinhold; Riese, Martin

doi:10.5194/amt-7-1487-2014

Cited by 41 publications

(63 citation statements)

References 75 publications

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“…2) for the differentiation of ice from other aerosol types. This characteristic gradient has been exploited in several recent studies using MIPAS observations Grainger et al, 2013;Griessbach et al, 2014).…”

Section: Combined Cr-btd Classificationmentioning

confidence: 99%

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

et al. 2016

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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.

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Section: Combined Cr-btd Classificationmentioning

confidence: 99%

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

et al. 2016

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“…Both the location in the tropics, in regions of strong vertical motions and convective clouds, and the relatively sharp decrease at higher altitudes towards increasing temperatures suggest that it is connected to the influence of ice particles. The ice filter for MIPAS data by Griessbach et al (2016) is applied to all retrieved MIPAS aerosol profiles to reduce the effect of spectra influenced by ice in the present data set. Their method consists of two steps to detect whether MIPAS spectra are influenced by aerosols, ice, clouds, ashes, or a clear sky (Griessbach et al, 2014(Griessbach et al, , 2016.…”

Section: Chemical Transport Modelmentioning

confidence: 99%

“…The ice filter for MIPAS data by Griessbach et al (2016) is applied to all retrieved MIPAS aerosol profiles to reduce the effect of spectra influenced by ice in the present data set. Their method consists of two steps to detect whether MIPAS spectra are influenced by aerosols, ice, clouds, ashes, or a clear sky (Griessbach et al, 2014(Griessbach et al, , 2016. First, aerosols and clouds are identified, using a spectral window region that is sensitive to aerosols and clouds.…”

Section: Chemical Transport Modelmentioning

confidence: 99%

“…The impact of missing data is strongest in the lowermost altitude region presented here. For MIPAS this is mainly due to the presence of clouds and ash, which were filtered out using the cloud filter by Spang et al (2004) in the case of SO 2 and partly filtered out in the case of aerosol; additionally, the ice and ash filters by Griessbach et al (2016Griessbach et al ( , 2014 were used for the aerosol retrieval. The CTM has low data coverage at lower altitudes due to its isentropic vertical grid.…”

Section: Sulfur Mass In the Northern Hemisphere Mid-and High Latitudesmentioning

confidence: 99%

“…In our data set, we consider only retrieved values starting 4 km above the altitude of the uppermost spectrum that was flagged to have been influenced by ice. Further, the ash filter for MIPAS spectra by Griessbach et al (2014), based on an ash detection threshold function, is applied in the same way as the ice filter, to filter out volcanic ash and mineral dust.…”

Section: Chemical Transport Modelmentioning

confidence: 99%

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MIPAS observations of volcanic sulfate aerosol and sulfur dioxide in the stratosphere

et al. 2018

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Abstract. Volcanic eruptions can increase the stratospheric sulfur loading by orders of magnitude above the background level and are the most important source of variability in stratospheric sulfur. We present a set of vertical profiles of sulfate aerosol volume densities and derived liquidphase H 2 SO 4 (sulfuric acid) mole fractions for 2005-2012, retrieved from infrared limb emission measurements performed with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on board of the Environmental Satellite (Envisat). Relative to balloon-borne in situ measurements of aerosol at Laramie, Wyoming, the MIPAS aerosol data have a positive bias that has been corrected, based on the observed differences to the in situ data. We investigate the production of stratospheric sulfate aerosol from volcanically emitted SO 2 for two case studies: the eruptions of Kasatochi in 2008 and Sarychev in 2009, which both occurred in the Northern Hemisphere midlatitudes during boreal summer. With the help of chemical transport model (CTM) simulations for the two volcanic eruptions we show that the MIPAS sulfate aerosol and SO 2 data are qualitatively and quantitatively consistent with each other. Further, we demonstrate that the lifetime of SO 2 is explained well by its oxidation by hydroxyl radicals (OH). While the sedimentation of sulfate aerosol plays a role, we find that the long-term decay of stratospheric sulfur after these volcanic eruptions in midlatitudes is mainly controlled by transport via the BrewerDobson circulation. Sulfur emitted by the two midlatitude volcanoes resides mostly north of 30 • N at altitudes of ∼ 10-16 km, while at higher altitudes (∼ 18-22 km) part of the volcanic sulfur is transported towards the Equator where it is lifted into the stratospheric "overworld" and can further be transported into both hemispheres.

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Measurements of the complex refractive index of volcanic ash at 450, 546.7, and 650 nm

et al. 2015

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The detection and quantification of volcanic ash is extremely important to the aviation industry, civil defense organizations, and those in peril from volcanic ashfall. To exploit the remote sensing techniques that are used to monitor a volcanic cloud and return information on its properties, the effective complex refractive index of the volcanic ash is required. This paper presents the complex refractive index determined in the laboratory at 450.0 nm, 546.7 nm, and 650.0 nm for volcanic ash samples from eruptions of Aso (Japan), Grímsvötn (Iceland), Chaitén (Chile), Etna (Italy), Eyjafjallajökull (Iceland), Tongariro (New Zealand), Askja (Iceland), Nisyros (Greece), Okmok (Alaska), Augustine (Alaska), and Spurr (Alaska). The Becke line method was used to measure the real part of the refractive index with an accuracy of 0.01. The values measured differed between eruptions and were in the range 1.51-1.63 at 450.0 nm, 1.50-1.61 at 546.7 nm, and 1.50-1.59 at 650.0 nm. A novel method is introduced to derive the imaginary part of the refractive index from the attenuation of light by ash. The method has a precision in the range 10 −3 -10 −4 . The values for the ash imaginary refractive index ranged 0.22-1.70 × 10 −3 at 450.0 nm, 0.16-1.93 × 10 −3 at 546.7 nm, and 0.15-2.08 × 10 −3 at 650.0 nm. The accuracy of Becke and attenuation methods was assessed by measuring the complex refractive index of Hoya neutral density glass and found to have an accuracy of < 0.01 and < 2 × 10 −5 for the real and imaginary parts of the refractive index, respectively.

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Volcanic ash detection with infrared limb sounding: MIPAS observations and radiative transfer simulations

Cited by 41 publications

References 75 publications

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

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

MIPAS observations of volcanic sulfate aerosol and sulfur dioxide in the stratosphere

Measurements of the complex refractive index of volcanic ash at 450, 546.7, and 650 nm

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