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Precise knowledge of the location and height of the volcanic sulphur dioxide (SO2) plume is essential for accurate determination of SO2 emitted by volcanic eruptions. Current SO2 plume height retrieval algorithms based on ultraviolet (UV) satellite measurements are very time-consuming and therefore not suitable for near-real-time applications. In this work we present a novel method called the full-physics inverse learning machine (FP-ILM) algorithm for extremely fast and accurate retrieval of the SO2 plume height. FP-ILM creates a mapping between the spectral radiance and the geophysical parameters of interest using supervised learning methods. The FP-ILM combines smart sampling methods, dimensionality reduction techniques, and various linear and non-linear regression analysis schemes based on principal component analysis and neural networks. The computationally expensive operations in FP-ILM are the radiative transfer model computations of a training dataset and the determination of the inversion operator – these operations are performed off-line. The application of the resulting inversion operator to real measurements is extremely fast since it is based on calculations of simple regression functions. Retrieval of the SO2 plume height is demonstrated for the volcanic eruptions of Mt. Kasatochi (in 2008) and Eyjafjallajökull (in 2010), measured by the GOME-2 (Global Ozone Monitoring Instrument – 2) UV instrument on-board MetOp-A. [ABSTRACT FROM AUTHOR]

Precise knowledge of the location and height of the volcanic SO2 plume is essential foraccurate determination of SO2 emitted by volcanic eruptions for aviation control applications,but so far very time-consuming to retrieve from UV satellite data. We have developed anextremely fast yet accurate SO2 layer height retrieval algorithm using the Full-PhysicsInverse Learning Machine (FP-ILM) algorithm, which, for the first time, is applied toTROPOMI aboard Sentinel-5 Precursor. Conceptually, the FP-ILM consists of a training phase, in which the inversion operator isobtained using synthetic data generated using a radiative transfer (RT) model, and anoperational phase, in which the inversion operator is applied to satellite measurements. Themain advantage of the FP-ILM over classical direct fitting approaches is that thetime-consuming training phase involving complex RT modeling is performed offline; theinverse operator itself is robust and computationally simple and therefore extremelyfast. In this presentation we demonstrate the ability of the FP-ILM algorithm to retrievelayer heights in near-real time applications with an accuracy of better than 2 kmand show SO2 layer height results for selected volcanic eruptions measured bySentinel-5 Precursor/TROPOMI, including the Krakatau and Etna eruption end of2018. [ABSTRACT FROM AUTHOR]

Abstract: Despite a fainter Sun, the surface of the early Earth was mostly ice-free. Proposed solutions to this so-called “faint young Sun problem” have usually involved higher amounts of greenhouse gases than present in the modern-day atmosphere. However, geological evidence seemed to indicate that the atmospheric CO2 concentrations during the Archaean and Proterozoic were far too low to keep the surface from freezing. With a radiative–convective model including new, updated thermal absorption coefficients, we found that the amount of CO2 necessary to obtain 273K at the surface is reduced up to an order of magnitude compared to previous studies. For the late Archaean and early Proterozoic period of the Earth, we calculate that CO2 partial pressures of only about 2.9mb are required to keep its surface from freezing which is compatible with the amount inferred from sediment studies. This conclusion was not significantly changed when we varied model parameters such as relative humidity or surface albedo, obtaining CO2 partial pressures for the late Archaean between 1.5 and 5.5mb. Thus, the contradiction between sediment data and model results disappears for the late Archaean and early proterozoic. [Copyright &y& Elsevier]