Google WeatherNext 3 Makes Forecasts Five Times Sharper

Google DeepMind and Google Research have introduced WeatherNext 3. It the firm describes as its most advanced and accurate global weather model so far.

Key points

• WeatherNext 3 is additionally beginning to power weather experiences throughout Google Search, the Gemini app, Google Maps, Google Maps Platform Weather API, and Google Earth Engine starting today.
• According to Google's evaluations, the model delivered a Continuous Ranked Probability Score improvement of up to 60% against IMERG in medium-range global forecasts.
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• Users can access the global forecasts through BigQuery and Earth Engine, or download the data in bulk through Google Cloud Storage.

Independent live evaluations by Brightband additionally rank it as the most accurate global weather model at present available.

Unlike previous AI weather systems that relied mainly on delayed numerical weather prediction data, WeatherNext 3 can learn directly from real-time observations, including satellite and weather station data.

Meanwhile, the model produces a new global forecast every hour and can provide predictions at resolutions as detailed as 5 kilometers. Higher-Resolution and More Frequent Forecasts.

WeatherNext 3 generates hourly forecasts throughout a number of spatial resolutions while maintaining consistency between large-scale weather patterns and local conditions.

While other surface variables employ 10km resolution, key surface variables such as temperature and moisture can be forecast at 5km resolution. Atmospheric variables such as wind speed are forecast at 25km resolution.

Google notes this provides a global weather picture roughly five times sharper than WeatherNext 2. It produced forecasts on a 25km grid every six hours.

Meanwhile, the system uses a Functional Generative Network mesh transformer that combines hourly geostationary satellite mosaics with traditional historical analysis.

It can produce dense weather fields, cyclone tracks, and forecasts for specific weather station locations.

Motorola Goes Major at IFA With New Phone, Watch and Snap Charger. Uses Live Satellite and Weather Station Data.

One of the biggest changes in WeatherNext 3 is the data applied to generate forecasts.

Most AI weather models, including WeatherNext 2, train on information produced by numerical weather prediction models. These physics-based systems rely on supercomputers and can carry a data delay of around six hours.

Google notes this delay can create difficulties when forecasting rapidly changing conditions such as rain and surface temperatures.

WeatherNext 3 instead uses a continuously updated mosaic of global geostationary satellite observations.

This allows it to generate a new forecast every hour using the latest available satellite data.

Google notes the shorter update cycle can provide earlier and more detailed information when storms, fronts or precipitation systems develop swiftly.

For context, the model additionally trains directly on sparse weather station observations, allowing it to better capture local differences caused by geography, including coastlines, valleys and mountain ranges.

Google notes this could be particularly useful in parts of Latin America, Africa and Asia-Pacific, where high-resolution regional forecasting has traditionally been limited by the high computing costs of conventional models. Major Improvement in Rain and Snow Forecasting.

Google has additionally focused on improving precipitation forecasting, an area where both traditional and AI-based global weather models have struggled.

WeatherNext 3 trains on NASA's Integrated Multi-satellite Retrievals for GPM, or IMERG, alongside Google's own global precipitation reanalysis based on satellite radar data.

It additionally improved by up to 30% against MRMS and 10% against rain gauge measurements at early forecast lead times.

WeatherNext 3 can additionally reproduce sharper boundaries around precipitation systems instead of producing the more blurred estimates seen in previous models. Built for Renewable Energy Forecasting. WeatherNext 3 introduces weather predictions designed specifically for renewable energy production.

In practice, the model can forecast wind speeds at a height of 100 meters, roughly matching the height of plenty of wind turbines.

It additionally provides high-resolution cloud cover and solar radiation forecasts to assist solar farms estimate how much sunlight will reach the ground.

Google notes this information can assist grid operators and renewable energy developers estimate future power generation and better match supply with consumer demand. Gemini 3.8 Flash Brings Frontier-Level Performance at Flash Rates. Where WeatherNext 3 Is Available.

Google is making WeatherNext 3 data available to researchers, developers and businesses without requiring them to set up the model themselves.

WeatherNext 3 is additionally beginning to power weather experiences throughout Google Search, the Gemini app, Google Maps, Google Maps Platform Weather API, and Google Earth Engine starting today. Google notes the model particularly improves longer-term forecasts.

For forecasts created a day or more in advance, users could see precipitation predictions that are up to 50% more accurate, with the largest improvements anticipated in regions where weather forecasting has historically been less reliable.

Google notes that weather remains inherently unpredictable and advises users to rely on local meteorological agencies or national weather services for official forecasts, severe weather warnings, and public safety information. Stay Connected with ProPakistani.

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Taken together, the developments around google WeatherNext 3 Makes Forecasts Five Times Sharper point to a situation that is still moving, and the coming days should bring more clarity.