Application of tomographic algorithms to Polar Mesospheric Cloud observations by Odin/OSIRIS

Abstract. Limb-scanning satellites can provide global information about the vertical structure of Polar Mesospheric Clouds. However, information about horizontal structures usually remains limited. This is due to both a long line of sight and a long scan duration. On eighteen days during the Northern Hemisphere summers 2010–2011 and the Southern Hemisphere summer 2011/2012, the Swedish-led Odin satellite was operated in a special mesospheric mode with short limb scans limited to the altitude range of Polar Mesospheric Clouds. For Odin's Optical Spectrograph and InfraRed Imager System (OSIRIS) this provides multiple views through a given cloud volume and, thus, a basis for tomographic analysis of the vertical/horizontal cloud structure. Here we present algorithms for tomographic analysis of mesospheric clouds based on maximum probability techniques. We also present results of simulating OSIRIS tomography and retrieved cloud structures from the special tomographic periods.

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Inversion of the Scattering Coefficient of Polar Mesospheric Clouds by Using One-Dimensional Maximum Probability Method

Laser & Optoelectronics Progress ◽

10.3788/lop54.120101 ◽

2017 ◽

Vol 54 (12) ◽

pp. 120101

Author(s):

郜海阳 Gao Haiyang ◽

卜令兵 Bu Lingbing ◽

王震 Wang Zhen ◽

朱红 Zhu Hong

Keyword(s):

Scattering Coefficient ◽

Probability Method ◽

Maximum Probability ◽

One Dimensional ◽

Polar Mesospheric Clouds ◽

Mesospheric Clouds

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Properties of polar mesospheric clouds measured by Odin/OSIRIS in the northern hemisphere in 2002–2005

Canadian Journal of Physics ◽

10.1139/p07-092 ◽

2007 ◽

Vol 85 (11) ◽

pp. 1143-1158 ◽

Cited By ~ 8

Author(s):

S V Petelina ◽

E J Llewellyn ◽

D A Degenstein

Keyword(s):

Interannual Variability ◽

Northern Hemisphere ◽

Occurrence Frequency ◽

High Latitudes ◽

Polar Mesospheric Clouds ◽

Infrared Imager ◽

The Mean ◽

Mesospheric Clouds

Interseasonal variations in the properties of Polar Mesospheric Clouds (PMC) measured by the Optical Spectrograph and InfraRed Imager System (OSIRIS) on the Odin satellite during the northern hemisphere (NH) summers of 2002–2005 are described in this work. The lowest PMC latitudes were about 50°N for every season with the number of detections smallest in 2002 and largest in 2004. In 2004 and 2005, the detection of PMCs at lower latitudes was asymmetric with the larger number of clouds observed during the first half and fewer at during the second half of the season. PMC occurrence frequency in 2002 was 25–30% lower than in 2003–2005, and the season duration was shortest in 2002 and longest in 2004. For all NH seasons except 2002, PMC occurrence frequency was systematically 20–50% higher than the Solar Mesosphere Explorer climatology. Similar to PMC occurrence frequency, cloud brightness was lowest in 2002 and highest in 2004 at all latitudes. The daily mean brightness maximum at 50°–60°N was less than 8% of that at highest latitudes. This contrasts with the maximum PMC occurrence frequency that reached nearly 30% at these latitudes in 2004 and 2005. PMC brightness showed no apparent seasonal asymmetry at lower latitudes in 2004 and 2005 that was seen in the occurrence frequency. Significant, by about a factor of 2, oscillations observed in the daily mean cloud brightness at high latitudes were also not seen in the corresponding occurrence frequency. These results suggest that the occurrence frequency alone does not provide detailed information on the cloud population and ice mass in the mesosphere. There is no significant interannual variability in the seasonal mean OSIRIS PMC altitude. Its value was very close to 8350 km for all seasons except 2004 when it was 83.42 km. The mean PMC altitudes for each season increased by 0.3–0.6 when the minimum altitude in the database was increased from 80 to 82 km. PACS Nos.: 92.05.Fg, 92.60.hc, 92.60.Jq, 92.60.Mt, 92.60.Nv, 92.60.Vb

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ATMOSPHERIC SCIENCE: Enhanced: An Iron Deficiency in Polar Mesospheric Clouds

Science ◽

10.1126/science.1097150 ◽

2004 ◽

Vol 304 (5669) ◽

pp. 395-396 ◽

Cited By ~ 1

Author(s):

D. M. Hunten

Keyword(s):

Iron Deficiency ◽

Atmospheric Science ◽

Polar Mesospheric Clouds ◽

Mesospheric Clouds

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Middle ultraviolet imager observations of the distribution of polar mesospheric clouds

Advances in Space Research ◽

10.1016/s0273-1177(01)00239-3 ◽

2001 ◽

Vol 27 (10) ◽

pp. 1703-1708 ◽

Cited By ~ 1

Author(s):

J.F. Carbary ◽

D. Morrison ◽

G.J. Romick ◽

L.J. Paxton ◽

C.-I. Meng

Keyword(s):

Polar Mesospheric Clouds ◽

Mesospheric Clouds

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A new Description of Probability Density Distributions of Polar Mesospheric Clouds (PMC)

10.5194/acp-2018-642 ◽

2018 ◽

Author(s):

Uwe Berger ◽

Gerd Baumgarten ◽

Jens Fiedler ◽

Franz-Josef Lübken

Keyword(s):

Probability Density ◽

Mass Density ◽

Particle Radius ◽

Number Density ◽

Data Set ◽

Statistical Probability ◽

Polar Mesospheric Clouds ◽

Density Distributions ◽

Mesospheric Clouds ◽

Probability Density Distributions

Abstract. In this paper we present a new description about statistical probability density distributions (pdfs) of Polar Mesospheric Clouds (PMC) and noctilucent clouds (NLC). The analysis is based on observations of maximum backscatter, ice mass density, ice particle radius, and number density of ice particles measured by the ALOMAR RMR-lidar for all NLC seasons from 2002 to 2016. From this data set we derive a new class of pdfs that describe the statistics of PMC/NLC events which is different from previously statistical methods using the approach of an exponential distribution commonly named g-distribution. The new analysis describes successfully the probability statistic of ALOMAR lidar data. It turns out that the former g-function description is a special case of our new approach. In general the new statistical function can be applied to many kinds of different PMC parameters, e.g. maximum backscatter, integrated backscatter, ice mass density, ice water content, ice particle radius, ice particle number density or albedo measured by satellites. As a main advantage the new method allows to connect different observational PMC distributions of lidar, and satellite data, and also to compare with distributions from ice model studies. In particular, the statistical distributions of different ice parameters can be compared with each other on the basis of a common assessment that facilitate, for example, trend analysis of PMC/NLC.

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