Google Using AI To Predict Cyclones.

Key Takeaway.

• Google’s Weather Lab uses AI to predict cyclone paths and intensity up to 15 days in advance with high accuracy.
• The tool offers real-time, interactive forecasts and is being tested with NOAA for potential integration into emergency planning.

Google DeepMind and Google Research have unveiled Weather Lab, a public preview of their experimental cyclone prediction model powered by AI. The new platform specializes in forecasting tropical cyclone formation, trajectory, intensity, size, and structure up to 15 days in advance, generating 50 possible storm scenarios to provide richer insights.

Google AI-Driven Cyclone Forecasting.

Traditional cyclone forecasting relies on physics-based models, which are accurate but computationally intensive. DeepMind’s AI model, built using stochastic neural networks and trained on decades of historical atmospheric data and nearly 5,000 recorded cyclone observations, offers predictions orders of magnitude faster. It can process and visualize a full ensemble forecast in real time, without supercomputers.

Early internal evaluations show that Weather Lab’s model matches or exceeds the accuracy of leading physics-based systems in both cyclone path and intensity forecasting. For example, it successfully predicted Cyclone Alfred’s landfall in Queensland seven days in advance, demonstrating high reliability for moderate scenarios.

Google Interactive Weather App.

Weather Lab’s interface allows users to explore live and historical forecasts side-by-side with established predictive models like those from the European Centre for Medium-Range Weather Forecasts (ECMWF). It includes WeatherNext Graph and WeatherNext Gen models and offers two-plus years of archived data for public research and evaluation.

Users can visualize cyclone predictions, including ensemble tracks, wind field maps, and probability zones, to better understand uncertainty and forecast variability. A dedicated “expert mode” lets trusted testers simulate cyclogenesis, visualizing potential future storms before formation, providing planning insights for emergency agencies.

Weather Lab is already collaborating with the U.S. National Hurricane Center (NHC), which reviews live AI forecasts alongside traditional tools. This marks the first time that AI-based cyclone predictions are being evaluated within an operational emergency forecasting environment. Through this cooperation, official forecasters gain access to alternative scenarios that could improve early warning systems.

Google emphasizes that Weather Lab is a research tool, not an official forecast provider. It aims to complement—not replace—public meteorological services. The company is also reaching out to academic, government, and meteorological organizations globally to further refine and expand the project.

Importance of Cyclone Prediction.

Accurate cyclone tracking is critical because cyclones have caused over $1.4 trillion in damage worldwide in recent decades. With longer lead times and more accurate intensity predictions, AI tools like the one showcased in Weather Lab could save lives, enable better evacuation planning, and improve disaster readiness.

Related Posts:

• How the Android Earthquake Alerts System Works?

Google Rolls Out ‘Deep Think’ Mode in Gemini 2.5 to AI Ultra Subscribers.

Key Takeaway.

• Google launches Deep Think mode for Gemini 2.5, offering advanced reasoning and step-by-step problem solving to AI Ultra users.
• Deep Think achieved gold-level performance at the International Mathematical Olympiad and scored 87.6% on LiveCodeBench.

Google has officially rolled out ‘Deep Think’, a powerful reasoning mode for Gemini 2.5 Pro, exclusively to AI Ultra subscribers. First teased during Google I/O 2025, this upgrade represents one of the most significant leaps in AI reasoning and structured problem-solving to date.

Now available on the web and mobile versions of Gemini, Deep Think allows the AI to take more time and apply deeper, multi-path reasoning to user prompts. The new feature comes with a dedicated button in the Gemini prompt bar and is aimed at users who need detailed answers to complex problems, especially in fields like mathematics, software development, and scientific research.

A New Way for Gemini to “Think”.

Unlike the traditional Gemini 2.5 response mechanism, Deep Think applies parallel hypothesis exploration, allowing it to simulate multiple reasoning paths before concluding with the most optimal answer. This mirrors a form of decision-making similar to how expert humans solve intricate challenges.

According to Google, this is enabled by what it calls a “higher thinking budget,” giving Gemini more processing power and internal resources to spend time analyzing, validating, and refining its outputs.

For advanced tasks, such as writing long code snippets, solving Olympiad-level math problems, or developing strategic plans, Deep Think now represents Gemini’s most powerful mode of cognition yet.

Credit: Google

Performance of Deep Think.

Google’s Deep Think mode, available in Gemini 2.5 Pro, significantly raises the bar for AI reasoning, creativity, and problem-solving. By enabling the model to explore multiple reasoning paths in parallel and synthesize stronger final outputs, Deep Think showcases dramatic improvements in several high-stakes performance benchmarks, many of which are used to test advanced human intelligence.

Key Benchmark Results with Deep Think.

1. LiveCodeBench (Coding Reasoning)

In coding benchmarks, Deep Think delivers a remarkable 87.6% score on LiveCodeBench, a major jump from the standard Gemini 2.5 Pro’s 80.4%. This benchmark tests the model’s ability to solve competition-level programming problems under strict constraints. With this performance, Deep Think now surpasses all major AI models, including OpenAI’s GPT‑4, Anthropic’s Claude 3.5, and Elon Musk’s Grok 4.

2. MMMU (Massive Multidisciplinary Multimodal Understanding)

When it comes to complex multimodal reasoning, Deep Think achieves an impressive 84.0% on the MMMU benchmark. This test evaluates the model’s ability to handle cross-domain questions that involve interpreting text, images, tables, and other structured data. The high score demonstrates Gemini's growing strength in understanding and synthesizing diverse types of information.

3. International Mathematical Olympiad (IMO) Gold Medal Standard

An advanced version of Gemini with Deep Think achieved a breakthrough by solving 5 out of 6 problems from the International Mathematical Olympiad, earning a gold medal–level score. This is one of the most prestigious mathematics contests in the world, and Gemini’s performance was officially verified by IMO coordinators, making it the first time an AI has independently demonstrated such elite mathematical ability.

4. Creative Reasoning and Synthesis

Beyond raw accuracy, Deep Think is designed for deliberative, multi-path reasoning. The model takes more time to “think,” allowing it to simulate several solution paths, compare outcomes, and arrive at refined conclusions. This approach results in more structured, step-by-step responses, better self-verification, and increased reliability, especially for solving STEM problems, complex business logic, and academic tasks that require precision. These results position Gemini as one of the most academically capable AI systems ever deployed to the public.

Also Read: Google Launches Gemini Drops Feed to Centralize AI Tips and Updates.

Who can access Deep Think?

As of today, Deep Think is rolling out in phases to users subscribed to the AI Ultra tier at $249.99 per month in the US. AI Ultra Access comes with:

• Daily usage limits to balance computing cost and performance.
• Tool-enabled mode (when allowed) that lets Gemini use code execution, web search, and other APIs during its reasoning process.
• Structured output formatting for step-by-step solutions, logic trees, and even visual representations of reasoning.

Developer Preview on Deep Think.

Google also confirmed that API access to Deep Think for both tool-enabled and tool-free variants will be offered to select developers and enterprise partners in the coming weeks. This move could reshape how businesses deploy autonomous agents, customer support bots, and research assistants.

Notably, Deep Think can be integrated into long-context workflows, with Gemini 2.5 already supporting 1 million tokens in its context window. Reports suggest Google may soon expand this further to 2 million tokens, making it suitable for full-document analysis, multi-step reasoning, and long-form content generation.