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DINCAE (Data-Interpolating Convolutional Auto-Encoder) is a neural network to reconstruct missing data in satellite observations

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DINCAE (Data-Interpolating Convolutional Auto-Encoder) is a neural network to reconstruct missing data in satellite observations.


Python 3.6 with the modules:

Tested versions:

  • Python 3.6.8
  • netcdf4 1.4.2
  • numpy 1.15.4
  • Tensorflow version 1.15

You can install those packages either with pip3 or with conda.

Input format

The input data should be in netCDF with the variables:

  • lon: longitude (degrees East)
  • lat: latitude (degrees North)
  • time: time (days since 1900-01-01 00:00:00)
  • mask: boolean mask where true means the data location is valid
  • SST (or any other varbiable name): the data
netcdf avhrr_sub_add_clouds {
	time = UNLIMITED ; // (5266 currently)
	lat = 112 ;
	lon = 112 ;
	double lon(lon) ;
	double lat(lat) ;
	double time(time) ;
		time:units = "days since 1900-01-01 00:00:00" ;
	int mask(lat, lon) ;
	float SST(time, lat, lon) ;
		SST:_FillValue = -9999.f ;

Running DINCAE

Copy the template file and adapt the filename, variable name and the output directory and possibly optional arguments for the reconstruction method as mentioned in the documentation. The code can be run as follows:

export PYTHONPATH=/path/to/module

/path/to/module should be replaced by the directory name containing the file

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Files for DINCAE, version 1.1.0
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