EGU2020-19452
https://doi.org/10.5194/egusphere-egu2020-19452
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

FP_ILM: Extremely fast volcanic SO2 plume height retrieval based on S5P/TROPOMI data using inverse learning machines

Dmitry Efremenko1, Pascal Hedelt1, Diego Loyola1, and Robert Spurr2
Dmitry Efremenko et al.
  • 1Institut für Methodik der Fernerkundung (IMF), Deutsches Zentrum für Luft und Raumfahrt (DLR), Oberpfaffenhofen, Germany (dmitry.efremenko@dlr.de)
  • 2RT Solutions Inc., Cambridge, USA (rtsolutions@verizon.net)

We present here a novel method for the precise and extremely fast retrieval of volcanic SO2 layer height (LH) based on S5P/TROPOMI data. We have developed the Full-Physics Inverse Learning Machine (FP_ILM) algorithm using a combined principal components analysis (PCA) and neural network approach (NN) to extract the information about the volcanic SO2 LH from high-resolution UV backscatter measurement of TROPOMI aboard Sentinel-5 Precursor.

The SO2 LH is essential for accurate determination of SO2 emitted by volcanic eruptions. So far UV based SO2 plume height retrieval algorithms are very time-consuming and therefore not suitable for near-real-time applications. The FP_ILM approach however enables for the first time to extract the SO2 LH information in a matter of seconds for an entire S5P orbit and thus applicable in NRT application.

The FP_ILM SO2 LH product is developed as part of ESA’s ‘Sentinel-5p+ Innovation - SO2 Layer Height project’ (S5P+I: SO2 LH) project, dedicated to the generation of an SO2 LH product and its extensive verification with collocated ground- and space-born measurements.

How to cite: Efremenko, D., Hedelt, P., Loyola, D., and Spurr, R.: FP_ILM: Extremely fast volcanic SO2 plume height retrieval based on S5P/TROPOMI data using inverse learning machines, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19452, https://doi.org/10.5194/egusphere-egu2020-19452, 2020

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