Application of a dielectric breakdown induced by high-power lasers for a laboratory simulation of meteor plasma
Extremely fast retrieval of volcanic SO2 layer heights from UV satellite data using inverse learning machines
Abstract:
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, however so far not available in operational near-real time UV satellite retrievals. The FP_ILM algorithm (Full-Physics Inverse Learning Machine) enables for the first time to extract the SO2 layer height information in a matter of seconds for current UV satellites and is thus applicable in NRT environments.
The FP_ILM combines a principal component analysis (PCA) and a neural network approach (NN) to extract the information about the volcanic SO2 layer height from high-resolution UV satellite backscatter measurements. So far, UV based SO2 layer height retrieval algorithms were 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.
In this presentation, we will present the latest FP_ILM algorithm improvements and show results of recent volcanic eruptions.
The SO2 layer height product for Sentinel-5p/TROPOMI is developed in the framework of the SO2Â Layer Height (S5P+I: SO2 LH)Â 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 LHÂ project is dedicated to the generation of an SO2Â LH product and its extensive verification with collocated ground- and space-born measurements.