EGU23-7281
https://doi.org/10.5194/egusphere-egu23-7281
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Neural network surrogate models for multiple scattering: Application to OMPS LP simulations

Michael Himes1, Natalya Kramarova2, Tong Zhu3, Jungbin Mok2,3, Matthew Bandel3, Zachary Fasnacht3, and Robert Loughman4
Michael Himes et al.
  • 1NASA Postdoctoral Program Fellow, Goddard Space Flight Center, Greenbelt, Maryland, USA (michael.d.himes@nasa.gov)
  • 2NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 3Science Systems and Applications Inc., Lanham, Maryland, USA
  • 4Department of Atmospheric and Planetary Sciences, Hampton University, Hampton, Virginia, USA

Retrieving ozone from limb measurements necessitates the modeling of scattered light through the atmosphere.  However, accurately modeling multiple scattering (MS) during retrieval requires excessive computational resources; consequently, operational retrieval models employ approximations in lieu of the full MS calculation.  Here we consider an alternative MS approximation method, where we use radiative transfer (RT) simulations to train neural network models to predict the MS radiances.  We present our findings regarding the best-performing network hyperparameters, normalization schemes, and input/output data structures.  Using RT calculations based on measurements by the Ozone Mapping and Profiling Suite's Limb Profiler (OMPS/LP), we compare the accuracy of these neural-network models with both the full MS calculation as well as the current MS approximation methods utilized during OMPS/LP retrievals.

How to cite: Himes, M., Kramarova, N., Zhu, T., Mok, J., Bandel, M., Fasnacht, Z., and Loughman, R.: Neural network surrogate models for multiple scattering: Application to OMPS LP simulations, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7281, https://doi.org/10.5194/egusphere-egu23-7281, 2023.