This repository contains code for super-resolving GOES-16 ABI channels to 0.5 km resolution.

Assuming you have anaconda installed, you can build the environment (abisr_test) using the included environment.yml file. Currently, these scripts require a GPU. Depending on your GPU, you may need to install a different version of the CUDA libraries. This was tested on an NVIDIA RTX A6000 and uses Tensorflow 2.15.0

Run this once:

conda env create -f environment.yml 

This will create an environment named 'abisr_test'. If you would like a different name, then you can change the first line of the environment.yml file.

Now, activate the environment. Run this in each new terminal session before running the script:

conda activate abisr_test

TF/Keras model weights used in the paper can be obtained at this link: https://drive.google.com/drive/folders/1F0MfzEg64oIuCls5knAPwh6Vky9l2pSi?usp=sharing

Make a new directory (ideally named 'models') and extract this file there.

Example command:

python predict.py --model_path='models/PansharpeningCNN_12_layers_256_filters/' \
                   --input_path='' \
                   --output_path='.' \
                   --timestring='s20221251520205' \
                   --gpu_index=0 \
                   --chunk_size=1024 \
                   --overlap=32 \
                   --domain='FD_Example4' \
                   --n_parallel_out=8

The predict.py script will run the model by tiliing across the entire full-disk image (or a portion of it). It will produce 0.5-km resolution files for all 16 ABI channels. The predict.py script can be executed from the command line with the following arguments:

--model_path: Path to the model directory.

--input_path: Path to the input data directory. This directory should contain all 16 ABI channels. It can contain multiple times because we specify exactly which time to use with the 'timestring' argument. The script requires that all 16 L1b files (one for each channel) are present in the --input directory.

--output_path: Path to the output directory (optional, default is current directory). A directory will be created here named 'ABISR_{timestring}' and 15 super-resolved files will be saved here with a suffix: '_05km.nc'

--timestring: This is the file start time that you can find in the name of the ABI L1b files beginning with 'sYYYYDDD....' This is unique for each timestamp, but should be same for each set of 16 channels. Example: 's20221251520205'

--gpu_index: Integer that tells the script which GPU to use. Use nvidia-smi to figure out which ID to supply.

--chunk_size: Size of the chunks to process one-at-a-time on the GPU. Larger is faster, but uses more VRAM. If you get an OOM error from tensorflow, then decrease.

--overlap: Number of pixels to overlap between chunks. Reduces issues with edge artifacts between chunks. 16-32 is best.

--domain: The name of the domain for which you wish to run the script. 'FD_Full' will run an entire full disk image. You must specify the indicies of your domain in domain.py. The script, by default crops the output L1b file to this domain.

--n_parallel_out: Number of processes to run in parallel when saving out each of the 15 bands. The best number depends on I/O bandwidth and compression level.

Currently only full-disk images from GOES-16 ABI have been tested. The script performs the following steps:

• Loads the specified model.
• Reads ABI data from the input directory.
• Applies the super-resolution model to make all channels at 0.5-km resolution.
• Saves the super-resolved images to the output directory while attempting to mimic the format of the original ABI L1b files.

• Various planned speed optimizations
• Add support for CONUS/Meso sectors
• Train models for a few channel subsets
• Add ability to save only selected channels to disk