Jochen Landgraf
Jochen Landgraf is a senior scientist at SRON Netherlands Institute for Space Research and a guest professor at the Institute for Environmental Physics, University Heidelberg. At SRON he is head of a research group focusing on atmospheric remote sensing of trace gases from TANGO, TROPOMI, S5, and GOSAT observations. His group developed the algorithm and SW for the operational CO and CH4 data processing of the Sentinel-5P and Sentinel 5 mission. In several studies, his team demonstrated higher-level data applications of S5P CO and CH4 data. He is a member of the S4/S5 and CO2M mission advisory group and has led and contributed to many projects, including several for ESA and EUMETSAT. His scientific focus is on atmospheric radiative transfer and measurement inversion techniques but also includes new measurement concepts and instrument specifications.
Sessions
Radiative Transfer Theory provides a robust framework for understanding how matter interacts with light, enabling us to interpret radiometric observations of planetary atmospheres. This theory allows us to explore photochemical processes and calculate heating rates in atmospheres, both of which are crucial for studying atmospheric chemistry and modeling the climate.
The absorption of telluric greenhouse gases in the Earth's atmosphere and the
methodology for determining the concentrations of CO 2 and CH 4 through satellite
observations are the focal points of this tutorial. For this tutorial, relevant aspects are
discussed in the lecture on Radiative Transfer. The tutorial will guide participants in
simulating the solar spectrum that is reflected by the Earth's atmosphere and
subsequently measured by satellites. Emphasis will be placed on the 1.6 µm spectral
range that encompasses the absorption bands of CO 2 , CH 4 , and water vapor. It is
used by several satellite missions to infer greenhouse gas abundances from
radiometric observations. The model that we will use will incorporate fundamental
instrument characteristics such as spectral resolution and sampling, facilitating a
comparison between our simulation and actual observations obtained from the
GOSAT satellite. By adjusting the input parameters of the model, we strive to
enhance the concordance between the model's predictions and observed data,
thereby allowing us to ascertain the total quantities of CO 2 and CH 4 present in the
monitored atmosphere.