Volcanic SO<sub>2</sub> plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm

Efremenko, Dmitry; Loyola, G R Diego; Hedelt, Pascal; Spurr, Robert

doi:10.1080/01431161.2017.1348644

Cited by 31 publications

(24 citation statements)

References 63 publications

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“…Global satellite observations allow for the timely detection and monitoring of SO 2 emitted from volcanic eruptions, even in remote regions, where no ground-based instruments are installed (see, e.g., Fioletov et al, 2013). Satellite measurements of UV earthshine spectra in the wavelength range between 305 and 335 nm provide the highest sensitivity to SO 2 in the Earth's atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM

et al. 2019

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Abstract. The accurate determination of the location, height, and loading of sulfur dioxide (SO2) plumes emitted by volcanic eruptions is essential for aviation safety. The SO2 layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO2 plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO2 layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO2 plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU. We present SO2 layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI.

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Section: Introductionmentioning

confidence: 99%

Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM

et al. 2019

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“…Machine learning can be used not only for forward problems (like the parameterization of RTM simulations), but also for solving inverse problems, see for example (Loyola et al, 2016). During the last years we developed an approached called full-physics inverse learning machine (FP_ILM) technique that was successfully applied for retrieving profile shapes from GOME-2 (Xu et al, 2017) and retrieving SO 2 layer height from GOME-2 (Efremenko et al, 2017) and TROPOMI (Hedelt et al, 2019). Figure 1 shows a flow diagram of the different steps of the FP_ILM algorithm and the following subsections describe in more detail how FP_ILM is applied for the retrieval of GE_LER.…”

Section: The Fp_ilm Algorithm For the Ge_ler Retrievalmentioning

confidence: 99%

Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) to account for BRDF effects on UVN satellite measurements of trace gases, clouds and aerosols

Loyola

Heue

et al. 2019

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Abstract. The retrieval of trace gas, cloud and aerosol measurements from ultraviolet, visible and near-infrared (UVN) sensors requires precise information on the surface properties that are traditionally obtained from Lambertian equivalent reflectivity (LER) climatologies. The main drawbacks of using such LER climatologies for new satellite missions are (a) climatologies are typically based on previous missions with a significant lower spatial resolution, (b) they usually do not fully take into account the satellite viewing dependencies characterized by the bidirectional reflectance distribution function (BRDF) effects, and (c) climatologies may differ considerably from the actual surface conditions especially under snow/ice situations. In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors based on the full-physics inverse learning machine (FP_ILM) retrieval. The radiances are simulated using a radiative transfer model that takes into account the satellite viewing geometry and the inverse problem is solved using machine learning techniques to obtain the GE_LER from satellite measurements. The GE_LER retrieval is optimized for the trace gas retrievals using the DOAS algorithm and the large amount of data of the new atmospheric Sentinel satellite missions. The GE_LER can either be used directly for the computation of AMFs using the effective scene approximation or a global gapless geometry-dependent LER (G3_LER) daily map can be easily created from the GE_LER under clear-sky conditions for the computation of AMFs using the independent pixel approximation. The FP_ILM GE_LER algorithm is applied to measurements of TROPOMI launched in October 2017 on board the EU/ESA Sentinel-5 Precursor (S5P) mission. The TROPOMI GE_LER/G3_LER results are compared with climatological OMI LER data and the advantages of using GE_LER/G3_LER are demonstrated for the retrieval of total ozone from TROPOMI.

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“…The spectrum at full spectral resolution can be reproduced by computing the radiances only at certain wavelengths. Such an approach is used in several fast RTMs (e.g., PCRTM [11], RTTOV [12] and others [13,14]) and retrieval algorithms [15,16]. To select the most representative spectral subsets, the spectral sampling methods are used (see, e.g., [11,14] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

et al. 2019

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The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency of two acceleration techniques based on the principal component analysis (PCA) for simulating the Hartley-Huggins band spectra. In the first one, the PCA is used to map the data set of optical properties of the atmosphere to a lower-dimensional subspace, in which the correction function for an approximate but fast RTM is derived. The second technique is based on the dimensionality reduction of the data set of spectral radiances. Once the empirical orthogonal functions are found, the whole spectrum can be reconstructed by performing radiative transfer computations only for a specific subset of spectral points. We considered a clear-sky atmosphere where the optical properties are defined by Rayleigh scattering and trace gas absorption. Clouds can be integrated into the model as Lambertian reflectors. High computational performance is achieved by combining both techniques without losing accuracy. We found that for the Hartley-Huggins band, the combined use of these techniques yields an accuracy better than 0.05% while the speedup factor is about 20. This innovative combination of both PCA-based techniques can be applied in future works as an efficient approach for simulating the spectral radiances in other spectral regions.

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Volcanic SO₂ plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm

Cited by 31 publications

References 63 publications

Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM

Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM

Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) to account for BRDF effects on UVN satellite measurements of trace gases, clouds and aerosols

Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

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Volcanic SO2 plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm

Cited by 31 publications

References 63 publications

Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM

Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM

Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) to account for BRDF effects on UVN satellite measurements of trace gases, clouds and aerosols

Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

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Volcanic SO₂ plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm