Repeatable high-resolution statistical downscaling through deep learning

This repository contains the source code necessary to reproduce the results of the manuscript "Repeatable high-resolution statistical downscaling through deep learning" to be submitted to Geoscientific Model Development. This work is built on top of the climate4R framework.

Code to train and validate the models

The folder train_scripts contains all the modifications to the base OEG_cnn_full.R file to produce all the needed validation data analysed in Results and discussion. The aforementioned script is based on the code by Baño-Medina et al. (2020, https://doi.org/10.5194/gmd-13-2109-2020). The OEG_cnn_full.R trains and validates 100 different deep learning models. The following variables can be changed to train a different set of models:

# Benchmark model CNN1 and variations of it
deepName <- c("CNN1", "CNN32-1", "CNN64-1", "CNN64_3-1")

u_model <- c("", "pp") # Type of u model, "" for U-Net, "pp" for U-Net++
u_layers <- c(3, 4, 5) # Depth of the u model
u_seeds <- c(16, 32, 64, 128) # Number of filters of the first layer
u_Flast <- c(1, 3) # Number of filters of the last ConvUnit
u_do <- c(TRUE) # Boolean for dropout within the u model
u_spdor <- c(0.25) # Fraction of the spatial dropout within the u model
u_dense <- c(FALSE) # Boolean for dense units after ConvUnit
u_dunits <- list(c(256, 128), c(128)) # Dense units, passed as a list, several layers supported
act_main <- c("lelu") # Activation function within u model and dense units
act_last <- c("lelu") # Activation function for the last ConvUnit
alpha1 <- c(0.3) # If leaky relu (lelu) used, which alpha. For u model and dense units
alpha2 <- c(0.3) # Same as above but for last ConvUnit
BN1 <- c(TRUE) # Batch normalization inside u model
BN2 <- c(FALSE, TRUE) # Batch normalization for the last ConvUnit

Parsing and auxiliary functions

In parse_aux can be found all the functions and code to parse the results and to generate the data shown in Results and discussion. results_plots.R is the main script to generate the tables and figures, it relies on aux_results.R for it. unet_def.R contains the functions to define the U-like architectures and other related needed functions, called from the OEG_cnn* files. Note that the U architectures are defined in a way that the depth is a variable, alternatively to their original definitions. Lastly, aux_funs_train.R holds further functions to create and to ease the training of the models, also needed by the OEG_cnn* files.

Job files

The bash files in the root folder run the R scripts and submit the job files to slurm. The predictors and predictand files necessary to run the code can be downloaded here: . These files need to be saved to the folder ./Data/precip, relative to where this repository is downloaded.

Note that to run the code it is necessary either an environment set-up in accordance to Section 3.5 of the manuscript or the singularity container in , available under request. The container should be in the root folder of this repository.

The models were trained in the Alpha Centauri sub-cluster of the Center for Information Services and High Performance Computing (ZIH) of the Technische Universität Dresden. Therefore, if the scripts will be run on an HPC system, the job_alphacentauri* files need to be changed accordingly. The submit_ALL.sh file submits all the jobs necessary to recreate the results shown in the manuscript. Each of the 100 models defined in OEG_cnn_full.R is trained once with 10 different random seed numbers, shown in the seeds file. All the other models (OEG_cnn_*) are trained 10 times with one seed.

Note the order of the arguments passed to the bash files are:

1. Name of the file to run (after OEG_cnn_), e.g., full or 4

2. Number of repetitions per each model, e.g., 1 or 10

3. Boolean to pass to the TF_DETERMINISTIC_OPS flag, to use the GPU deterministic algorithms

4. Random seed number

   • Note that in the case of the run_seeds.sh, the seeds file is already used, so only the first 3 arguments are needed

The submit_ALL.sh file shows all the arguments passed to the different files. The validation results and the history of the training process are then copied to the val_hist folder.

Additional notes

• The code was written for an HPC system, nevertheless, the contents of the job_alphacentauri* files can be run on any Linux system with the aforementioned environment or container, with minor modifications.

• The plots seen in the manuscript were generated from a PC with R 4.1.2. The container has R 3.6.3 built-in, so, small differences are seen in the plots generated with it, such as overlapping of text in figures 5 and 6. Still, the code can be modified to fix these minor issues.