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A library designed to implement deep learning algorisms to climate data for weather and climate prediction.

License: Apache License 2.0

Python 7.51% Jupyter Notebook 92.49%

dlacs's Introduction

WoW you find a coding nerd there 👋 ⭐ 🌈 🚀 🍻

🌠 About me

My name is Yang and I'm a Research Software Engineer | Data Scientist | Meteorologist 🌤️ | Coding Nerd .

©️ Click these bages if you want to know more about me! ®️

💻 What I do

Yang is busy with coding and he has no time to answer your question 🔧 💦 ⭐ 🔨 💢 ✨ 💥 💡 ⌛, but here are some projects/software that he is very proud of:

DIANNA 🔍 Software for post-hoc explainability of deep neural networks for scientists.
AI4S2S ☀️ Integrating expert knowledge and artificial intelligence to boost (sub) seasonal forecasting.
EUCP ❄️ European Climate Prediction project.
EcoExtreML 🌿 Accelerating process understanding for ecosystem functioning under extreme climates.
Excited 🏔️ Global net ecosystem CO2 exchange fluxes estimation with machine learning.

📈 My contribution

dlacs's People

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cycle13 he1oise huangzq681 surfcao sharifsagar80

dlacs's Issues

Update data for publication

Update data (BayesLSTM and VAR forecasts data) for the publication on GRL.

Clean up cache files

Delete cache files (checkpoints) of jupyter notebooks.

Understanding uncertainties of Bayesan ConvLSTM output

Thanks very much for sharing the code, great job.
I'm trying to use your example regarding the prediction of the Lorenz 84 model with Bayesan ConvLSTM.
The example works without any problems, but I didn't understand how to have access to the uncertainties about the model fitness.
Are uncertainties the third output of this line? last_pred, _, _ = model(y_pred,timestep, training=False)

Thank you so much in advance

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