Preprints
https://doi.org/10.5194/egusphere-2024-1823
https://doi.org/10.5194/egusphere-2024-1823
10 Jul 2024
 | 10 Jul 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements

Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova

Abstract. Among aerosol characterization methods, limb scattering measurements provide both near-global coverage and information about how aerosol is vertically distributed through the atmosphere. Near-real-time characterization of aerosols produced by volcanic eruptions is particularly important for aviation safety, but the radiative transfer modeling of scattering processes performed by traditional retrieval methods are too computationally expensive for near-real-time applications without simplifying assumptions. Here we present a near-real-time approach based on neural networks (NNs) for aerosol retrievals from the Ozone Mapping and Profiler Suite's Limb Profiler (OMPS LP) instrument aboard the Suomi National Polar-orbiting Partnership satellite. We find it is at least 60 times faster than the current operational code and on average achieves agreement within 20 % at most altitudes and latitudes with sensitivity and non-negligible aerosol abundances. We also apply our trained NNs to measurements of the recent Shiveluch and Ruang eruptions from NOAA-21's OMPS LP and find results consistent with the operational retrieval algorithm, indicating our methodology generalizes to future iterations of the same instrument without re-training the NNs.

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Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova

Status: open (until 27 Aug 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova

Data sets

Research compendium Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova https://doi.org/10.5281/zenodo.11477425

Video supplement

Animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5–21.5 km at 997 nm for the 2024 Ruang eruptions Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova https://doi.org/10.5281/zenodo.11641540

Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova

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Short summary
The Ozone Mapping and Profiler Suite's Limb Profiler (OMPS LP) provides near-global coverage and information about how aerosols from volcanic eruptions and major wildfires are vertically distributed through the atmosphere. We developed a machine learning method to characterize aerosols using OMPS LP measurements about 60 times faster than the current approach. This near-real-time characterization can be used to ensure aviation flight paths avoid dangerous conditions.