EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Fast interpolation of satellite altimetry data with probabilistic machine learning and GPU

Ronald MacEachern1, Michel Tsamados1, William Gregory2, Isobel Lawrence3, and So Takao4
Ronald MacEachern et al.
  • 1Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, UK
  • 2Princeton, NOAA affiliate
  • 3ESRIN, European Space Agency, Italy/Centre for Polar Observation and Modelling, University of Leeds, UK
  • 4Centre for Artificial Intelligence, University College London, UK

Recent work has demonstrated how Gaussian Process Regression (GPR) can be used to interpolate Pan-Arctic radar freeboard of sea ice as measured by satellites. Sea ice freeboard is crucial to measuring sea ice thickness, and thus sea ice volume, which can play an important role in climate models. Similarly sea surface heights from altimetry are essential to determine the geostrophic currents from space. Using GPR can be computationally burdensome for modest dataset sizes and prohibitive for large datasets. To avoid having to deal with a large dataset the raw satellite observations were binned (averaged) onto a regularly spaced grid. We look at how these calculations can be reduced in terms of run time by utilising a Graphical Processing Unit (GPU), a dedicated GPR python package and by making practical adjustments to the methodology. We find by adopting these changes the overall run time of a single day’s interpolation can be greatly reduced by a factor of over 60, making it practical to run such calculations on an environment with a GPU. We then extend the method to use raw satellite observation data (no binning), which greatly increases the number of training points, requiring the use a sparse method for GPR. We conclude with recommendations for further work on this subject as it has the potential for widespread use in remote sensing applications.

How to cite: MacEachern, R., Tsamados, M., Gregory, W., Lawrence, I., and Takao, S.: Fast interpolation of satellite altimetry data with probabilistic machine learning and GPU, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17323,, 2023.