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What is WeatherNext?

WeatherNext is a collection of advanced AI-based models created by Google DeepMind and Google Research, aimed at offering state-of-the-art weather forecasting. These innovative models demonstrate superior speed and efficiency compared to traditional physics-based methods, resulting in more reliable forecasts. By enhancing the precision of weather predictions, these advancements have the potential to play a crucial role in disaster preparedness, ultimately helping to save lives in the face of extreme weather events while also improving the reliability of renewable energy systems and supply chains. WeatherNext Graph is particularly notable for providing more accurate and efficient deterministic forecasts than current systems, generating a single forecast for each designated time and location with a 6-hour interval and a 10-day projection. Furthermore, WeatherNext Gen is adept at producing ensemble forecasts that exceed the performance of the leading models, thus granting decision-makers a better grasp of weather uncertainties and the risks linked to extreme weather phenomena. This remarkable enhancement in forecasting capability is set to revolutionize our approach to managing and mitigating the effects of climate variability, ensuring communities are better equipped for future challenges. As a result, the integration of WeatherNext into various sectors could lead to more effective strategies for addressing the complexities of changing weather patterns.

What is NVIDIA Earth-2?

NVIDIA Earth-2 represents a cutting-edge platform that transforms weather and climate forecasting through high-performance, AI-driven technology, integrating various open-source models, libraries, and frameworks to deliver professional-level predictions without relying on traditional supercomputing capabilities. This groundbreaking system is equipped to handle the entire process from collecting raw observational data to producing high-resolution forecasts, including localized storm warnings and medium-range predictions that can extend up to 15 days. By harnessing the power of generative AI architectures, Earth-2 dramatically speeds up computational tasks while either preserving or improving accuracy compared to traditional forecasting methods. The Earth-2 suite includes specialized models like Atlas for multi-variable medium-range forecasting, StormScope for short-term localized weather insights, HealDA for global data assimilation, CorrDiff for enhancing regional resolution, and FourCastNet 3 aimed at effective global predictions. Additionally, this platform not only improves forecasting precision but also democratizes access to sophisticated forecasting tools, enabling a broader audience to benefit from these advancements. As such, Earth-2 marks a significant step forward in making weather forecasting more efficient and accessible to all.

Media

Media

Integrations Supported

AlphaCode
AlphaEvolve
AlphaFold
Chinchilla
Gemini
Gemini Deep Research
Gemini Diffusion
Gemini Enterprise
Gemini Robotics
Gemini platform
GitHub
Google AI Studio
Google Cloud BigQuery
Google Cloud Platform
Google Earth Engine
Gopher
Music AI Sandbox
Project Mariner

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Google DeepMind

Date Founded

2010

Company Location

United Kingdom

Company Website

deepmind.google/science/weathernext/

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

www.nvidia.com/en-us/high-performance-computing/earth-2/

Categories and Features

AI Models

Not specified

Categories and Features

AI Models

Not specified

Weather Intelligence

Not specified

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