Chapter 22
Poster Summaries
1 Group A: Trace Gas Characteristics
An important goal of atmospheric remote sensing deals with the detection and modelling of
atmospheric trace gases. It is well known that they directly and indirectly influence the climate
of the earth due to their radiative and chemical properties. However, the net effect and the
global variability of these gases is not well known. The modelling of the earths climate requires
long term global datasets of trace gas concentrations.
Three studies related to the subject of trace gas characteristics were presented in student
posters. They dealt with new remote sensing technique of water vapor and carbon monoxide.
Water vapor is the most important greenhouse gas in the atmosphere. Also it is the origin for
atmospheric energy fluxes, clouds and precipitation. CO is an important trace gas as it is the
largest sink of tropospheric hydroxyl.
In the poster of Servazi et al. the measurements of the atmospheric water vapor using the
Raman lidar technique is described. A short technical description of the instrument and a
summary of how to derive the water vapor mixing ratio from the detected, incoherently backscattered light intensities is given. The comparison of the water vapor mixing ratio, measured
simultaneously from radiosonde and lidar, just at the start time of the radio-sonde, shows very
good correspondence between 1500 and 8000 m. Additionally, the strong variability of the
atmospheric water vapor during the transit time of the radiosonde (lh), inside of the vertical
column between 1500 and 8000 m above the lidar, is reported.
Bartsch et al. are involved in the development of a new algorithm for the column water vapor content land surfaces with relative error less than 10% using backscattered solar radiation.
This involved the calculation of more than 1600 high resolution spectra. These spectra were
calculated in the wavelength range between 880 and 1000 nm - a strong water vapor absorption band in the near IR - as a function of the following parameteres: various real measured
atmospheric profiles, 22 different surfaces, gaussian distributed aersosol parameters and 5 solar
zenith angles.
Using a principal component analysis, it can be concluded that only 3 to 4 wavelenght channels,
instead of the original 210 channels, are necessary to obtain the atmospheric column water vapor
content. Using the original 210 channels, to which a 1 % noise is added, the developed algorithm
yields a rms error of 5%.
The validation of the algorithm with the airborne high resolution spectrometer OVID will be
done in future. Also the reduction of channel number with respect to the MERIS spectrometer
onboard the European satellite ENVISAT is one further goal of her study.
Poster Summaries
1 Group A: Trace Gas Characteristics
An important goal of atmospheric remote sensing deals with the detection and modelling of
atmospheric trace gases. It is well known that they directly and indirectly influence the climate
of the earth due to their radiative and chemical properties. However, the net effect and the
global variability of these gases is not well known. The modelling of the earths climate requires
long term global datasets of trace gas concentrations.
Three studies related to the subject of trace gas characteristics were presented in student
posters. They dealt with new remote sensing technique of water vapor and carbon monoxide.
Water vapor is the most important greenhouse gas in the atmosphere. Also it is the origin for
atmospheric energy fluxes, clouds and precipitation. CO is an important trace gas as it is the
largest sink of tropospheric hydroxyl.
In the poster of Servazi et al. the measurements of the atmospheric water vapor using the
Raman lidar technique is described. A short technical description of the instrument and a
summary of how to derive the water vapor mixing ratio from the detected, incoherently backscattered light intensities is given. The comparison of the water vapor mixing ratio, measured
simultaneously from radiosonde and lidar, just at the start time of the radio-sonde, shows very
good correspondence between 1500 and 8000 m. Additionally, the strong variability of the
atmospheric water vapor during the transit time of the radiosonde (lh), inside of the vertical
column between 1500 and 8000 m above the lidar, is reported.
Bartsch et al. are involved in the development of a new algorithm for the column water vapor content land surfaces with relative error less than 10% using backscattered solar radiation.
This involved the calculation of more than 1600 high resolution spectra. These spectra were
calculated in the wavelength range between 880 and 1000 nm - a strong water vapor absorption band in the near IR - as a function of the following parameteres: various real measured
atmospheric profiles, 22 different surfaces, gaussian distributed aersosol parameters and 5 solar
zenith angles.
Using a principal component analysis, it can be concluded that only 3 to 4 wavelenght channels,
instead of the original 210 channels, are necessary to obtain the atmospheric column water vapor
content. Using the original 210 channels, to which a 1 % noise is added, the developed algorithm
yields a rms error of 5%.
The validation of the algorithm with the airborne high resolution spectrometer OVID will be
done in future. Also the reduction of channel number with respect to the MERIS spectrometer
onboard the European satellite ENVISAT is one further goal of her study.