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Aerosol distribution above Munich using remote sensing techniques
Aerosol distribution above Munich using remote sensing techniques
Aerosols are an important part of our atmosphere. As they are very inhomogenously distributed both in time and space the estimation of their influence on the climate is very complex. So it is important to improve the knowledge about the aerosol distribution. In this study the distribution of aerosols above the region around Munich, Germany in the time period 2007 to 2010 is studied with measurements from remote sensing instruments. Thereby the main focus is set on the lidar data from the ground based lidar system MULIS of the Meteorological Institute Munich and the space lidar CALIOP onboard the satellite CALIPSO which both deliver aerosol information height resolved. As an addition and for a better comparison with previous studies, aerosol information from the AERONET Sunphotometer in Munich and the satellite spectroradiometer MODIS are used. With help of these four datasets several aerosol parameters could be studied: on average the aerosol optical depth (AOD) above the Munich region is at 1064 nm about 0.05 to 0.06, at 532 nm about 0.12 to 0.17, and at 355 nm about 0.22 to 0.28. The height of the boundary layer top decreases from 1.68 km in spring to 0.73 km in winter, while the thickness of the elevated layers is more stable (spring: 1.43 km, winter: 1.02 km). The occurrence of ELs is highest in spring (in 75 % of all measurements), and lowest in winter (36 %). Measurements of the particle linear depolarization ratio and the Ångström exponent show that the aerosols in elevated layers clearly differ from the aerosols in the PBL. Especially in spring the average EL depolarization is large (25 %) indicating transportation of strongly depolarizing aerosols like Saharan dust in the free troposphere. The dominant aerosol type in the Munich region is smoke (also called biomass burning), polluted continental can occur in high concentrations especially during summer time. Dust occurs only in rare occasions, mainly mixed with other aerosol types (polluted dust). One important finding from the comparison of the four datasets is that CALIPSO strongly underestimates the AOD. To study the significances of different causes for this, the CALIPSO extinction coefficient profiles are compared with coincidentally performed measurements of MULIS. The underestimation of the AOD above Munich by CALIPSO is mainly found to be due to the failure of the layer detection: its effect on the AOD underestimation is about 36 %. Also the wrong assumption of the lidar ratio contributes to the underestimation, though on a smaller account of about 5 %. The influence of clouds in the surroundings on the AOD is not quantifiable, but the analysis shows that clouds lead to an overestimation of the AOD. To compensate these reasons and to get detailed profiles from CALIPSO, it could be shown that -for case studies- it is very efficient to calculate the extinction coefficients from CALIPSO raw data (L1B) manually.
aerosol, remote sensing, CALIPSO, AOD distribution, region Munich
Schnell, Franziska Ingrid Josefine
2014
Englisch
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Schnell, Franziska Ingrid Josefine (2014): Aerosol distribution above Munich using remote sensing techniques. Dissertation, LMU München: Fakultät für Physik
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Abstract

Aerosols are an important part of our atmosphere. As they are very inhomogenously distributed both in time and space the estimation of their influence on the climate is very complex. So it is important to improve the knowledge about the aerosol distribution. In this study the distribution of aerosols above the region around Munich, Germany in the time period 2007 to 2010 is studied with measurements from remote sensing instruments. Thereby the main focus is set on the lidar data from the ground based lidar system MULIS of the Meteorological Institute Munich and the space lidar CALIOP onboard the satellite CALIPSO which both deliver aerosol information height resolved. As an addition and for a better comparison with previous studies, aerosol information from the AERONET Sunphotometer in Munich and the satellite spectroradiometer MODIS are used. With help of these four datasets several aerosol parameters could be studied: on average the aerosol optical depth (AOD) above the Munich region is at 1064 nm about 0.05 to 0.06, at 532 nm about 0.12 to 0.17, and at 355 nm about 0.22 to 0.28. The height of the boundary layer top decreases from 1.68 km in spring to 0.73 km in winter, while the thickness of the elevated layers is more stable (spring: 1.43 km, winter: 1.02 km). The occurrence of ELs is highest in spring (in 75 % of all measurements), and lowest in winter (36 %). Measurements of the particle linear depolarization ratio and the Ångström exponent show that the aerosols in elevated layers clearly differ from the aerosols in the PBL. Especially in spring the average EL depolarization is large (25 %) indicating transportation of strongly depolarizing aerosols like Saharan dust in the free troposphere. The dominant aerosol type in the Munich region is smoke (also called biomass burning), polluted continental can occur in high concentrations especially during summer time. Dust occurs only in rare occasions, mainly mixed with other aerosol types (polluted dust). One important finding from the comparison of the four datasets is that CALIPSO strongly underestimates the AOD. To study the significances of different causes for this, the CALIPSO extinction coefficient profiles are compared with coincidentally performed measurements of MULIS. The underestimation of the AOD above Munich by CALIPSO is mainly found to be due to the failure of the layer detection: its effect on the AOD underestimation is about 36 %. Also the wrong assumption of the lidar ratio contributes to the underestimation, though on a smaller account of about 5 %. The influence of clouds in the surroundings on the AOD is not quantifiable, but the analysis shows that clouds lead to an overestimation of the AOD. To compensate these reasons and to get detailed profiles from CALIPSO, it could be shown that -for case studies- it is very efficient to calculate the extinction coefficients from CALIPSO raw data (L1B) manually.