DLWP: Deep Learning Weather Prediction


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).

  • TensorFlow (GPU capable version highly recommended). The conda package, while not the recommended installation method, is easy and also installs the required CUDA dependencies. For best performance, follow the instructions for installing from source.
    conda install tensorflow-gpu
  • Keras
    pip install keras
  • netCDF4
    conda install netCDF4
  • xarray
    conda install dask xarray

Optional dependencies

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

  • PyTorch: for torch-based deep learning models. Again the GPU-ready version is recommended.
    pip install torch torchvision
  • scikit-learn: for machine learning pre-processing tools such as Scalers and Imputers
    conda install scikit-learn
  • scipy: for CFS data interpolation
  • pygrib: for raw CFS data processing
    pip install pygrib
  • cdsapi: for retrieval of ERA5 data
    pip install cdsapi
  • pyspharm: spherical harmonics transforms for the barotropic model
    conda install -c conda-forge pyspharm

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:

  • examples/write_cfs.py
  • examples/write_cfs_predictors.py

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:

  • build_model: use a custom API to assemble layers in a Sequential model. Also implements models running on multiple GPUs.
  • fit: scale the data and fit the model
  • fit_generator: use the Keras fit_generator method along with a custom data generator (see section below)
  • predict: predict with the model
  • predict_timeseries: predict a continuous time series forecast, where the output of one prediction iteration is used as the input for the next

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.

  • The DataGenerator class is the simplest generator class. It merely returns batches of data from a file containing "predictors" and "targets" variables already formatted for use in the DLWP model. Due to this simplicity, this is the optimal way to generate data directly from the disk when system memory is not sufficient to load the entire dataset. However, this comes at the cost of generating very large files on disk with redundant data (since the targets are merely a different time shift of the predictors).
  • The SeriesDataGenerator class is much more robust and memory efficient. It expects only a single "predictors" variable in the input file and generates predictor-target pairs on the fly for each batch of data. It also has the ability to prescribe external fields such as incoming solar radiation.
  • The SmartDataGenerator is deprecated in favor of SeriesDataGenerator.

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).

Kushal Shingote
Android Developer📱📱 iOS Apps📱📱 Swift | Xcode | SwiftUI iOS Swift development📱 Kotlin Application📱📱 iOS📱 Artificial Intelligence 💻 Data science
Kushal Shingote
Dashboard for the COVID19 spread

COVID-19 Data Explorer App A streamlit Dashboard for the COVID-19 spread. The app is live at: [https://covid19.cwerner.ai]. New data is queried from G

Christian Werner 20 Jan 10, 2022
Kaggle: Cell Instance Segmentation

Kaggle: Cell Instance Segmentation The goal of this challenge is to detect cells in microscope images. with simple view on how many cels have been ann

Jirka Borovec 5 Oct 24, 2021
Tutorials and implementations for "Self-normalizing networks"

Self-Normalizing Networks Tutorials and implementations for "Self-normalizing networks"(SNNs) as suggested by Klambauer et al. (arXiv pre-print). Vers

Institute of Bioinformatics, Johannes Kepler University Linz 1.5k Jan 26, 2022
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021) PyTorch implementation of SnapMix | paper Method Overview Cite

DavidHuang 114 Jan 01, 2022
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

18 Dec 27, 2021
NeRF Meta-Learning with PyTorch

NeRF Meta Learning With PyTorch nerf-meta is a PyTorch re-implementation of NeRF experiments from the paper "Learned Initializations for Optimizing Co

Sanowar Raihan 64 Jan 15, 2022
Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

Manel Baradad Jurjo 58 Jan 30, 2022
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet)

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet) (

Wei-Ting Chen 24 Jan 19, 2022
Neural style transfer as a class in PyTorch

pt-styletransfer Neural style transfer as a class in PyTorch Based on: https://github.com/alexis-jacq/Pytorch-Tutorials Adds: StyleTransferNet as a cl

Tyler Kvochick 29 Dec 15, 2021
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

On the Bottleneck of Graph Neural Networks and its Practical Implications This is the official implementation of the paper: On the Bottleneck of Graph

55 Jan 14, 2022
Mall-Customers-Segmentation - Customer Segmentation Using K-Means Clustering

Overview Customer Segmentation is one the most important applications of unsupervised learning. Using clustering techniques, companies can identify th

NelakurthiSudheer 2 Jan 02, 2022
Train a deep learning net with OpenStreetMap features and satellite imagery.

DeepOSM Classify roads and features in satellite imagery, by training neural networks with OpenStreetMap (OSM) data. DeepOSM can: Download a chunk of

TrailBehind, Inc. 1.3k Jan 06, 2022
A hifiasm fork for metagenome assembly using Hifi reads.

hifiasm_meta - de novo metagenome assembler, based on hifiasm, a haplotype-resolved de novo assembler for PacBio Hifi reads.

36 Jan 02, 2022
Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由,在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Mutian He 46 Jan 25, 2022
SWA Object Detection

SWA Object Detection This project hosts the scripts for training SWA object detectors, as presented in our paper: @article{zhang2020swa, title={SWA

202 Jan 13, 2022
Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data.

Deep Learning Dataset Maker Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data. How to use Down

deepbands 5 Jan 06, 2022
UFPR-ADMR-v2 Dataset

UFPR-ADMR-v2 Dataset The UFPR-ADMRv2 dataset contains 5,000 dial meter images obtained on-site by employees of the Energy Company of Paraná (Copel), w

Gabriel Salomon 2 Jan 27, 2022
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

Jason Ren 17 Oct 31, 2021
Cours d'Algorithmique Appliquée avec Python pour BTS SIO SISR

Course: Introduction to Applied Algorithms with Python (in French) This is the source code of the website for the Applied Algorithms with Python cours

Loic Yvonnet 0 Jan 26, 2022
QuALITY: Question Answering with Long Input Texts, Yes!

QuALITY: Question Answering with Long Input Texts, Yes! Authors: Richard Yuanzhe Pang,* Alicia Parrish,* Nitish Joshi,* Nikita Nangia, Jason Phang, An

ML² AT CILVR 37 Jan 16, 2022