WeatherBench 2: A benchmark for the next generation of data-driven
  global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather
forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to
accelerate progress in data-driven weather modeling. WeatherBench 2 consists of
an open-source evaluation framework, publicly available training, ground truth
and baseline data as well as a continuously updated website with the latest
metrics and state-of-the-art models:
https://sites.research.google/weatherbench. This paper describes the design
principles of the evaluation framework and presents results for current
state-of-the-art physical and data-driven weather models. The metrics are based
on established practices for evaluating weather forecasts at leading
operational weather centers. We define a set of headline scores to provide an
overview of model performance. In addition, we also discuss caveats in the
current evaluation setup and challenges for the future of data-driven weather
forecasting.
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Stephan Rasp
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Stephan Hoyer
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Alexander Merose
admin_assigned
2023-08-31T12:27:00.146Z
6313a82117838d05194fca7b
/avatars/ab4749d69a329e8d9dbbbc7dfc7bf483.svg
Alex Merose
user
alxmrs
64f0138277777df14e3a78cc
Ian Langmore
64f0138277777df14e3a78cd
Peter Battaglia
64f0138277777df14e3a78ce
Tyler Russel
64f0138277777df14e3a78cf
Alvaro Sanchez-Gonzalez
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Vivian Yang
admin_assigned
2023-08-31T12:28:57.432Z
64e81395f2f5545edaf04e5d
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VivianYang
user
VivianYang
64f0138277777df14e3a78d1
Rob Carver
64f0138277777df14e3a78d2
Shreya Agrawal
64f0138277777df14e3a78d3
Matthew Chantry
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Zied Ben Bouallegue
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Peter Dueben
64f0138277777df14e3a78d6
Carla Bromberg
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Jared Sisk
admin_assigned
2023-08-31T12:30:21.513Z
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/avatars/9d90dc4d70901b0d4486ad2b24d43658.svg
Jared Siskin
user
jtsiskin
64f0138277777df14e3a78d8
Luke Barrington
64f0138277777df14e3a78d9
Aaron Bell
64f0138277777df14e3a78da
Fei Sha
WeatherBench 2: A benchmark for the next generation of data-driven
  global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather
forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to
accelerate progress in data-driven weather modeling. WeatherBench 2 consists of
an open-source evaluation framework, publicly available training, ground truth
and baseline data as well as a continuously updated website with the latest
metrics and state-of-the-art models:
https://sites.research.google/weatherbench. This paper describes the design
principles of the evaluation framework and presents results for current
state-of-the-art physical and data-driven weather models. The metrics are based
on established practices for evaluating weather forecasts at leading
operational weather centers. We define a set of headline scores to provide an
overview of model performance. In addition, we also discuss caveats in the
current evaluation setup and challenges for the future of data-driven weather
forecasting.
WeatherBench 2 provides an improved framework for evaluating data-driven weather forecasting models using established operational metrics and an open-source dataset.