IBM and NASA Release Open-Source AI Model on Hugging Face for Weather and Climate Applications
IBM and NASA have released a new AI foundation model for weather and climate applications, available as open-source on Hugging Face. This model, developed with contributions from Oak Ridge National Laboratory, offers a flexible and scalable approach to address various challenges related to short-term weather and long-term climate projection.
The model was pre-trained on 40 years of Earth observation data from NASA's MERRA-2 and can be fine-tuned for global, regional, and local scales. Two fine-tuned versions are available: one for climate and weather data downscaling, and another for gravity wave parameterization.
This foundation model is part of the Prithvi family of AI models and has already been tested by Environment and Climate Change Canada for additional weather forecasting use cases. It aims to provide actionable science and improve the accuracy of weather and climate simulations.
IBM e NASA hanno rilasciato un nuovo modello di intelligenza artificiale per applicazioni meteorologiche e climatiche, disponibile come open-source su Hugging Face. Questo modello, sviluppato con il contributo del Laboratorio Nazionale Oak Ridge, offre un approccio flessibile e scalabile per affrontare varie sfide legate alla previsione meteorologica a breve termine e alla proiezione climatica a lungo termine.
Il modello è stato pre-addestrato su 40 anni di dati di osservazione della Terra forniti da MERRA-2 di NASA e può essere affinato per scale globali, regionali e locali. Sono disponibili due versioni affinate: una per il downscaling dei dati climatici e meteorologici, e l'altra per la parametrizzazione delle onde gravitazionali.
Questo modello di base fa parte della famiglia di modelli di intelligenza artificiale Prithvi e è già stato testato da Environment and Climate Change Canada per ulteriori casi d'uso nella previsione meteorologica. Mira a fornire scienza applicabile e migliorare l'accuratezza delle simulazioni meteorologiche e climatiche.
IBM y NASA han lanzado un nuevo modelo de inteligencia artificial para aplicaciones relacionadas con el clima y el tiempo, disponible como código abierto en Hugging Face. Este modelo, desarrollado con contribuciones del Laboratorio Nacional Oak Ridge, ofrece un enfoque flexible y escalable para abordar diversos desafíos relacionados con la previsión del tiempo a corto plazo y la proyección climática a largo plazo.
El modelo fue preentrenado con 40 años de datos de observación de la Tierra de MERRA-2 de NASA y se puede ajustar para escalas globales, regionales y locales. Hay disponibles dos versiones ajustadas: una para el descenso de datos climáticos y meteorológicos, y otra para la parametrización de ondas gravitacionales.
Este modelo base es parte de la familia de modelos de inteligencia artificial Prithvi y ya ha sido probado por Environment and Climate Change Canada para otros casos de uso en previsión meteorológica. Su objetivo es proporcionar ciencia utilizable y mejorar la precisión de las simulaciones climáticas y meteorológicas.
IBM과 NASA는 기상 및 기후 응용 프로그램을 위한 새로운 AI 기본 모델을 발표했으며, Hugging Face에서 오픈 소스로 제공됩니다. 이 모델은 오크리지 국립 연구소의 기여로 개발되었으며, 단기 기상 예보와 장기 기후 예측과 관련된 다양한 문제를 해결할 수 있는 유연하고 확장 가능한 접근 방식을 제공합니다.
이 모델은 NASA의 MERRA-2에서 40년간의 지구 관측 데이터로 사전 훈련되었으며, 글로벌, 지역 및 로컬 스케일에 맞게 조정할 수 있습니다. 기후 및 기상 데이터 저해상도 처리용 조정된 버전과 중력파 매개변수화를 위한 또 다른 조정된 버전이 제공됩니다.
이 기본 모델은 프리트비 AI 모델 가족의 일원이며, 이미 환경 및 기후 변화 캐나다에 의해 추가 기상 예측 사용 사례를 위해 테스트되었습니다. 이는 실행 가능한 과학을 제공하고 기상 및 기후 시뮬레이션의 정확성을 향상시키는 것을 목표로 합니다.
IBM et NASA ont publié un nouveau modèle d'intelligence artificielle pour des applications météorologiques et climatiques, disponible en tant que source ouverte sur Hugging Face. Ce modèle, développé avec les contributions du Laboratoire national d'Oak Ridge, offre une approche flexible et évolutive pour relever divers défis liés aux prévisions météorologiques à court terme et aux projections climatiques à long terme.
Le modèle a été pré-entraîné sur 40 ans de données d'observation de la Terre provenant de MERRA-2 de la NASA et peut être affiné pour des échelles globales, régionales et locales. Deux versions ajustées sont disponibles : l'une pour le redimensionnement des données climatiques et météorologiques, et l'autre pour la paramétrisation des ondes gravitationnelles.
Ce modèle de base fait partie de la famille de modèles d'intelligence artificielle Prithvi et a déjà été testé par Environnement et Changement climatique Canada pour des cas d'utilisation supplémentaires en prévision météorologique. Il vise à fournir une science appliquée et à améliorer la précision des simulations météorologiques et climatiques.
IBM und NASA haben ein neues KI-Grundlagenmodell für Wetter- und Klima-Anwendungen veröffentlicht, das als Open Source auf Hugging Face verfügbar ist. Dieses Modell, das mit Beiträgen des Oak Ridge National Laboratory entwickelt wurde, bietet einen flexiblen und skalierbaren Ansatz zur Bewältigung verschiedener Herausforderungen im Zusammenhang mit kurzfristigen Wettervorhersagen und langfristigen Klimaprognosen.
Das Modell wurde mit 40 Jahren Erdbeobachtungsdaten von NASAs MERRA-2 vortrainiert und kann für globale, regionale und lokale Skalen angepasst werden. Es sind zwei angepasste Versionen verfügbar: eine für die Herunterrechnung von Klima- und Wetterdaten und eine andere für die Parametrisierung von Schwerewellen.
Dieses Grundlagenmodell gehört zur Prithvi-Familie von KI-Modellen und wurde bereits von Environment and Climate Change Canada für zusätzliche Anwendungsfälle in der Wettervorhersage getestet. Es zielt darauf ab, umsetzbare Wissenschaft bereitzustellen und die Genauigkeit von Wetter- und Klimasimulationen zu verbessern.
- Release of a new AI foundation model for weather and climate applications
- Model is available as open-source on Hugging Face
- Flexible and scalable approach for short-term weather and long-term climate projection
- Pre-trained on 40 years of Earth observation data
- Can be fine-tuned for global, regional, and local scales
- Potential to improve accuracy of numerical weather and climate models
- None.
New AI foundation model offers insights beyond forecasting for scientists, developers, and businesses to better understand and analyze weather and climate data
Because of its unique design and training regime, the weather and climate foundation model can tackle far more applications than existing weather AI models, as outlined in a paper recently published on arXiv, "Prithvi WxC: Foundation Model for Weather and Climate." Potential applications include creating targeted forecasts based on local observations, detecting and predicting severe weather patterns, improving the spatial resolution of global climate simulations, and improving how physical processes are represented in numerical weather and climate models. In one experiment in the above identified paper, the foundation model accurately reconstructed global surface temperatures from a random sample of only five percent original data, suggesting a broader application to problems in data assimilation.
This model was pre-trained on 40 years of Earth observation data from NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). As a foundation model, it has a unique architecture which allows it to be fine-tuned to global, regional, and local scales. This flexibility makes it suited for a range of weather studies.
The foundation model is available for download on Hugging Face, along with two fine-tuned versions of the model that tackle specific scientific and industry-relevant applications. These are:
- Climate and weather data downscaling: A common meteorological practice is downscaling—inferring high-resolution outputs from low-resolution variables. Typical data inputs include temperature, precipitation, and surface winds, all of which can have varied resolutions. The model can depict both weather and climate data at up to 12x resolution, generating localized forecasts and climate projections. The fine-tuned downscaling model is available on the IBM Granite page on Hugging Face.
- Gravity wave parameterization: Gravity waves are ubiquitous throughout the atmosphere and can affect many atmospheric processes related to climate and weather, such as cloud formation and aircraft turbulence. Traditionally, existing numerical climate models have not sufficiently captured gravity waves, which leads to uncertainties in terms of how exactly gravity waves can affect climate processes. This weather and climate foundation model can help scientists better estimate gravity wave generation, to improve the accuracy of numerical weather and climate models and constrain uncertainty when simulating future weather and climate events. This gravity wave parameterization model is being released as part of the NASA-IBM Prithvi family of models on Hugging Face.
"Advancing NASA's Earth science for the benefit of humanity means delivering actionable science in ways that are useful to people, organizations, and communities. The rapid changes we're witnessing on our home planet demand this strategy to meet the urgency of the moment," said Karen St. Germain, director of the Earth Science Division of NASA's Science Mission Directorate. "The NASA foundation model will help us produce a tool that people can use: weather, seasonal, and climate projections to help inform decisions on how to prepare, respond, and mitigate."
"This space has seen the emergence of large AI models that focus on a fixed dataset and single use case — primarily forecasting. We have designed our weather and climate foundation model to go beyond such limitations so that it can be tuned to a variety of inputs and uses," said Juan Bernabe-Moreno, Director of IBM Research Europe and IBM's Accelerated Discovery Lead for Climate and Sustainability. "For example, the model can run both on the entire earth as well as in a local context. With such flexibility on the technology side, this model is well-suited to help us understand meteorological phenomena such as hurricanes or atmospheric rivers, reason about future potential climate risks by increasing the resolution of climate models, and finally inform our understanding of imminent severe weather events."
"As a premier research institution and computing facility, we're focused on supporting teams to make research breakthroughs across many areas of science," said Arjun Shankar, director of the National Center for Computational Sciences at Oak Ridge National Laboratory. "Our collaboration with IBM and NASA to support the creation of the Prithvi weather and climate foundation model was a key part of our goal to bring advanced computing and data to problems of national importance, in this case, for weather and climate applications, which need continued computational science and model skill improvements to be impactful."
IBM has already collaborated with Environment and Climate Change Canada (ECCC) with a view to test the flexibility of the model with additional weather forecasting use cases. With the model, ECCC is exploring very short-term precipitation forecasts using a technique called precipitation nowcasting that ingests real-time radar data as input. The team is also testing the downscaling approach from global model forecasts at 15 km to km-scale resolution.
This weather and climate model is part of a larger collaboration between IBM Research and NASA to use AI technology to explore our planet, and joins the Prithvi family of AI foundation models. Last year, IBM and NASA made the Prithvi geospatial AI foundation model the largest open-source geospatial AI model available on Hugging Face. This geospatial foundation model has since been used by governments, companies, and public institutions to examine changes in disaster patterns, biodiversity, land use, and other geophysical processes. The foundation model and the gravity wave parameterization model can be accessed through the NASA-IBM Hugging Face page and the downscaling model can be accessed through the IBM Granite Hugging Face page.
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