From 2d3687103053b5247cb0688988d7f8036f36f895 Mon Sep 17 00:00:00 2001 From: Kristin Branson Date: Sun, 10 Nov 2024 11:59:33 -0500 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- index.html | 16 ++++++++-------- index.xml | 6 +++--- posts/FourCastNet/index.html | 10 +++++----- search.json | 2 +- sitemap.xml | 2 +- 6 files changed, 19 insertions(+), 19 deletions(-) diff --git a/.nojekyll b/.nojekyll index cd546e5..f0f7fd0 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -c5ef4841 \ No newline at end of file +8811d10e \ No newline at end of file diff --git a/index.html b/index.html index 7a06111..f1e710a 100644 --- a/index.html +++ b/index.html @@ -186,7 +186,7 @@ +
Categories
All (2)
AI-For-Science (1)
Numerical-Methods (1)
Transformers (1)
Weather-Forecasting (1)
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diff --git a/index.xml b/index.xml index d24db7c..20bea90 100644 --- a/index.xml +++ b/index.xml @@ -21,10 +21,10 @@ Kristin Branson and Magdelena Schneider https://janelia-cvml.github.io/blog/posts/FourCastNet/ - AIForScience - NumericalMethods + AI-For-Science + Numerical-Methods Transformers - Weather + Weather-Forecasting https://janelia-cvml.github.io/blog/posts/FourCastNet/ Fri, 08 Nov 2024 05:00:00 GMT diff --git a/posts/FourCastNet/index.html b/posts/FourCastNet/index.html index 815a6eb..9c5d5eb 100644 --- a/posts/FourCastNet/index.html +++ b/posts/FourCastNet/index.html @@ -164,10 +164,10 @@
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There’s a pretty remarkable dataset available in the realm of AI for science: the ERA5 global reanalysis dataset, which contains hourly estimates of numerous 3D atmospheric, land, and ocean features at a horizontal resolution of 30 km over the past 80 years. This dataset was synthesized from up to 25M-per-day measurements from Earth-observing satellites and weather stations. Can machine learning be used to learn to forecast the weather better, either by more efficiently/effectively solving the weather PDEs or by learning a better model?

The FourCastNet paper [Pathak, 2022] trains an Adaptive Fourier Neural Operator (AFNO) network [Guibas, 2022] to predict a collection of atmospheric variables at the next time step (6 hours into the future) given the current readout for those variables. The AFNO is an interesting choice here: it takes inspiration from both neural operators, which are designed to efficiently solve PDEs, and Vision Transformers (ViT), which can learn complex functions from large image datasets. We know that weather is well-modeled by PDEs, and the ERA 5 dataset is converted into images where each pixel location corresponds to a \(.25^\circ \times .25^\circ\) latitude/longitude region, and each channel corresponds to a different atmospheric variable, so maybe some amalgam of neural operators and transformers makes sense.

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Training

Evaluation

Most of the paper is devoted to evaluating the performance of the weather forecasts. They show that their predictions are qualitatively accurate on predicting the formation and trajectory of a cyclone and hurricane and patterns of an atmospheric river within the range of 2-4 days. They quantitatively compare the accuracy of the FourCastNet prediction to the ECMWF’s physics-based Integrated Forecasting System (IFS). Accuracy is worse (with a few exceptions), but comparable. More recent ML-based models, including GraphCast [Lam, 2023] and GenCast [Price, 2024] report performance better than ECMWF’s.

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