Skip to content

Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data

License

Notifications You must be signed in to change notification settings

carlos-gg/dl4ds

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Tensorflow - Version Python - Version Open in Colab

Deep Learning for empirical DownScaling

DL4DS (Deep Learning for empirical DownScaling) is a Python package that implements state-of-the-art and novel deep learning algorithms for empirical downscaling of gridded Earth science data.

The general architecture of DL4DS is shown on the image below. A low-resolution gridded dataset can be downscaled, with the help of (an arbitrary number of) auxiliary predictor and static variables, and a high-resolution reference dataset. The mapping between the low- and high-resolution data is learned with either a supervised or a conditional generative adversarial DL model.

drawing

The training can be done from explicit pairs of high- and low-resolution samples (MOS-style, e.g., high-res observations and low-res numerical weather prediction model output) or only with a HR dataset (PerfectProg-style, e.g., high-res observations or high-res model output).

A wide variety of network architectures have been implemented in DL4DS. The main modelling approaches can be combined into many different architectures:

Downscaling type Training (loss type) Sample type Backbone section Upsampling method
MOS (explicit pairs of HR and LR data) Supervised (non-adversarial) Spatial Plain convolutional Pre-upsampling via interpolation
PerfectProg (implicit pairs, only HR data) Conditional Adversarial Spatio-temporal Residual Post-upsampling via sub-pixel convolution
Dense Post-upsampling via resize convolution
Unet (PIN, Spatial samples) Post-upsampling via deconvolution
Convnext (Spatial samples)

In DL4DS, we implement a channel attention mechanism to exploit inter-channel relationship of features by providing a weight for each channel in order to enhance those that contribute the most to the optimizaiton and learning process. Aditionally, a Localized Convolutional Block (LCB) is located in the output module of the networks in DL4DS. With the LCB we learn location-specific information via a locally connected layer with biases.

DL4DS is built on top of Tensorflow/Keras and supports distributed GPU training (data parallelism) thanks to Horovod.

API documentation

Check out the API documentation here.

Installation

pip install dl4ds

Example notebooks

A first Colab notebook can be found in the notebooks folder. Click the badge at the top to open the notebook on Google Colab.

About

Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published