EGU2020-21555
https://doi.org/10.5194/egusphere-egu2020-21555
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Image Inpainting for Missing Values in Observational Climate Datasets Using Partial Convolutions in a cuDNN

Christopher Kadow1, David Hall2, and Uwe Ulbrich3
Christopher Kadow et al.
  • 1German Climate Computing Center (DKRZ), Climate Informatics and Technology Group, Hamburg, Germany (kadow@dkrz.de)
  • 2Nvidia, Santa Clara, CA, USA
  • 3Freie Universitaet Berlin, Institute of Meteorology, Berlin, Germany

Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that machine learning technology can be applied to refill these missing climate values in observational datasets. We found that the technology of image inpainting using partial convolutions in a CUDA accelerated deep neural network can be trained by large Earth system model experiments from NOAA reanalysis (20CR) and the Coupled Model Intercomparison Project phase 5 (CMIP5). The derived deep neural networks are capable to independently refill added missing values of these experiments. The analysis shows a very high degree of reconstruction even in the cross-reconstruction of the trained networks on the other dataset. The network reconstruction reaches a better evaluation than other typical methods in climate science. In the end we will show the new reconstructed observational dataset HadCRUT4 and discuss further investigations.

How to cite: Kadow, C., Hall, D., and Ulbrich, U.: Image Inpainting for Missing Values in Observational Climate Datasets Using Partial Convolutions in a cuDNN, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21555, https://doi.org/10.5194/egusphere-egu2020-21555, 2020

This abstract will not be presented.