V030-08
Extremely Fast Retrieval of Volcanic SO2 Layer Heights from UV Satellite Data Using Inverse Learning Machines
Abstract:
We have therefore 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 FP_ILM approach enables for the first time to extract the SO2 LH information in a matter of seconds for an entire S5P orbit and is thus applicable in NRT applications.
In this presentation, we will present the FP-ILM algorithm and show results of recent volcanic eruptions.
The SO2 layer height product is developed in the framework of the SO2 Layer Height (S5P+I: SO2 LH) project, which is part of ESA Sentinel-5p+ Innovation project (S5P+I). The S5P+I project aims to develop novel scientific and operational products to exploit the potential of the S5P/TROPOMI capabilities. The S5P+I: SO2 LH project is dedicated to the generation of an SO2 LH product and its extensive verification with collocated ground- and space-born measurements.