DLWP: Deep Learning Weather Prediction

DLWP is a Python project containing data-processing and model-building tools for predicting the gridded atmosphere using deep convolutional neural networks.


If you use this code or find it useful please cite our publication!

Getting started

For now, DLWP is not a package that can be installed using pip or a setup.py file, so it works like most research code: download (or checkout) and run.

Required dependencies

It is assumed that the following are installed using Anaconda Python 3 (Python 2.7 is supported).

Optional dependencies

The following are required only for some of the DLWP features:

Quick overview

General framework

DLWP is built as a weather forecasting model that can, should performance improve greatly, "replace" and existing global weather or climate model. Essentially, this means that DLWP uses a deep convolutional neural network to map the state of the atmosphere at one time to the entire state of the atmophere at the next available time. A continuous forecast can then be made by feeding the model's predicted state back in as inputs, producing indefinite forecasts.

Data processing

The classes in DLWP.data provide tools for retrieving and processing raw data from the CFS reanalysis and reforecast and the ERA5 reanalysis. Meanwhile, the DLWP.model.preprocessing module provides tools for formatting the data for ingestion into the deep learning models. The following examples retrieve and process data from the CFS reanalysis:

The resulting file of predictor data can be ingested into the data generators for the models.

Keras models

The DLWP.model module contains classes for building and training Keras and PyTorch models. The DLWPNeuralNet class is essentially a wrapper for the simple Keras Sequential model, adding optional run-time scaling and imputing of data. It implements a few key methods:

An example of a model built and trained with the DLWP APIs using data generated by the DLWP processing methods, see examples/train.py.

DLWP also implements a DLWPFunctional class which implements the same methods as the DLWPNeuralNet class but takes as input to build_model a model assembled using the Keras functional API. For an example of training a functional model, see examples/train_functional.py.

PyTorch models

Currently, due to a focus on TensorFlow/Keras models, the PyTorch implementation in DLWP is more limited, although still robust. Like the Keras models, it implements a convenient build_model method to assemble a sequential-like model using the same API parameters as those for DLWPNeuralNet. Additionally, it also implements a fit method to automatically iterate through the data and optimizer, again, just like the Keras API.

The PyTorch example, train_torch.py, is somewhat outdated and uses the spherical convolution library s2cnn. This method has yet to produce good results.

Custom layers and functions

The DLWP.custom module contains many custom layers specifically for applying convolutional neural networks to the global weather prediction problem. For example, PeriodicPadding2D implements periodic boundary conditions for padding data in space prior to applying convolutions. These custom layers are worth a look.

Data generators

DLWP.model.generators contains several classes for generating data on-the-fly from a netCDF file produced by the DLWP preprocessing methods. These data generators can then be used in conjunction with a DWLP model instance's fit_generator method.

Advanced forecast tools

The DLWP.model module also contains a TimeSeriesEstimator class. This class can be used to make robust forward forecasts where the data input does not necessarily match the data output of a model. And example usage of this class is in examples/validate.py, which performs basic routines to validate the forecast skill of DLWP models.


The DLWP.util module contains useful utilities, including save_model and load_model for saving and loading DLWP models (and correctly dealing with multi-GPU models).