Levelmodel
Classweather
Parametersnot in the notes
Contextnot in the notes
Released13 Nov 2023
Opennessunknown
Runs onnot in the notes
Statusactive
Benchmark Results
maintainer-run
WeatherBench 2 2domain
Z500 day 10, deterministic, NeuralGCM 0.7 vs ERA5maintainer-run
751.4
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 10, deterministic, NeuralGCM ENS (mean) vs ERA5maintainer-run
606.8
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 5, deterministic, NeuralGCM 0.7 vs ERA5maintainer-run
267.1
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 5, deterministic, NeuralGCM ENS (mean) vs ERA5maintainer-run
266.2
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 3, deterministic, NeuralGCM ENS (mean) vs ERA5maintainer-run
126.2
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 3, deterministic, NeuralGCM 0.7 vs ERA5maintainer-run
115.7
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 1, deterministic, NeuralGCM ENS (mean) vs ERA5maintainer-run
44
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Z500 day 1, deterministic, NeuralGCM 0.7 vs ERA5maintainer-run
37.9
0450900
Score in RMSE m2/s2. Lower is better.
year 2020, 240x121 grid
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).
R&D Topics
Identity
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- other
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- sensor
- Output
- sensor
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
Developer
Googlebig tech, USA
Googlebig tech, USAModelNeuralGCM (0.7 and ENS)Google, weather
NeuralGCM (0.7 and ENS)Google, weatherR&D TopicsProbabilistic Ensemble Weather Forecasting with Machine LearningMachine-Learning Medium-Range Weather ForecastingDeep-Learning Climate Emulation
Use CasesWeather ForecastingAgricultureUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
Badan Meteorologi, Klimatologi, dan Geofisika (BMKG)Contract signed, 5 Feb 2026Badan Meteorologi, Klimatologi, dan Geofisika (BMKG)In production, 23 Sep 2025Deployments of these use cases; none names this model.
Badan Meteorologi, Klimatologi, dan Geofisika (BMKG)Contract signed, 5 Feb 2026Badan Meteorologi, Klimatologi, dan Geofisika (BMKG)In production, 23 Sep 2025Deployments of these use cases; none names this model.Organisations 1
R&D topics 3
Machine-Learning Medium-Range Weather ForecastingGlobal forecast models learned from reanalysis data that predict the next 10 to 15 days m…produced byProbabilistic Ensemble Weather Forecasting with Machine LearningMachine-learning models that produce many possible weather futures, giving forecast uncer…produced byDeep-Learning Climate EmulationLearned models of the coupled Earth system that run for years to centuries, for seasonal…produced by
Sources
- Dash researchsites.withgoogle.com https://researchsites.withgoogle.com/weatherbench/deterministic/
- arxiv.org/html/2311.07222 https://arxiv.org/html/2311.07222
- arxiv.org/abs/2311.07222 https://arxiv.org/abs/2311.07222
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "NeuralGCM (0.7 and ENS)", https://aurion.semai.id/models/neuralgcm/, records of October 2026.