Extremely Fast Retrieval of Volcanic SO2 Layer Heights from UV Satellite Data Using Inverse Learning Machines

Kurzfassung

Precise knowledge of the location and height of the volcanic sulfur dioxide (SO2) plume 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 like aviation control, although the SO2 LH is essential for accurate determination of SO2 emitted by volcanic eruptions. 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.