GEMI revolutionizes weather and climate data through a decentralized network of smartphones and stations, enabling more accurate forecasts, early warnings, and new markets in climate protection and resilient infrastructure
GEMI solves the problem of inaccurate, large-scale weather models and missing climate data by enabling high-resolution, decentralized real-time forecasting – globally, efficiently, and people-powered.
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The BRIGAID Business Development Programme has been successfully completed. A MAF+ assessment has been conducted and its results have been enriched and incorporated into a business plan document.
GEMI uses a decentralized network of weather stations and smartphones to collect and process real-time weather and climate data. Local stations provide standard metrics (e.g., temperature, pressure, precipitation), while smartphones contribute microdata such as altitude, pressure variations, and optionally sound or images. This data is pre-processed by edge AI directly on the device, anonymized, and transmitted through a distributed network to a central AI platform. There, high-resolution weather models are created—with accuracy down to 100 m instead of the traditional 10–28 km grid. By distributing computation (similar to SETI@home), GEMI is scalable, energy-efficient, and globally applicable. The system delivers more precise forecasts, faster early warnings, and valuable data for climate research—even in underserved regions.
GEMI (Global Real-time Meteorology Initiative) is a next-generation weather and climate data platform that leverages the power of distributed networks, smartphones, and artificial intelligence to deliver real-time, high-resolution forecasts and climate insights.
How it works:
1.Data Collection:
GEMI integrates data from local weather stations (temperature, wind, pressure, humidity, precipitation, UV) and enhances it with real-time sensor data from smartphones—such as air pressure, altitude, temperature, and optionally audio or images.
2.Edge Processing:
A lightweight AI on the smartphone filters and analyzes the data locally (Edge AI), reducing unnecessary transmission, preserving privacy, and ensuring efficient use of device resources.
3.Distributed Computing:
Devices voluntarily contribute unused processing power (e.g., at night while charging) to perform local calculations. These partial models are sent via secure decentralized networks to a central AI engine.
4.Model Synthesis:
A cloud-based AI system merges the distributed results into a coherent, high-resolution global weather and climate model with precision down to 100 meters. Continuous learning and feedback improve model accuracy over time.
5.Output & Use:
Forecasts, alerts, and real-time weather maps are delivered to users via the GEMI app, partner APIs, or emergency response systems.
Development approach:
The system is built modularly using existing mobile platforms (iOS/Android), open data standards, and scalable AI frameworks. It starts with regional pilots (e.g. Alpine region) using existing weather stations, then scales globally as more users join. Edge AI and distributed computing frameworks like BOINC/SETI@home serve as technical blueprints. Privacy, security, and energy efficiency are key design principles. GEMI is designed to operate independently or as a supplement to existing forecasting systems, drastically improving micro-scale accuracy, especially in regions without dense infrastructure.
This innovative, citizen-powered system democratizes climate intelligence—faster, smarter, and more inclusive.
Like any advanced technology, GEMI performs best under certain conditions. Its full potential is realized when a broad user base participates and enough sensor data is available. In regions with low smartphone density or limited access to local weather stations, data coverage may initially be thinner. However, GEMI continuously improves as more users join worldwide.
Not all smartphones have the same built-in sensors (e.g., barometer, thermometer), which may affect the range and precision of collected microdata. Still, even basic inputs like air pressure and altitude provide valuable contributions to localized weather models.
Some limitations may also arise from mobile operating systems such as Android or iOS, which impose restrictions on background processing and sensor access. GEMI is designed to comply with these frameworks, only collecting anonymized, technically permitted data to ensure user privacy.
In remote areas with poor connectivity or during power outages, data transmission may be delayed. Additionally, certain extreme events—like sudden lightning strikes or earthquakes—remain challenging to predict, even for the most advanced systems.
Despite these factors, GEMI remains a highly scalable and resilient innovation. With each additional participating device, the global weather and climate model becomes more accurate—even in previously underserved areas. GEMI’s strength lies in its combination of intelligent technology, citizen-powered data collection, and adaptive AI that can respond dynamically to diverse conditions around the world.
GEMI revolutionizes weather and climate data through a decentralized network of smartphones and stations, enabling more accurate forecasts, early warnings, and new markets in climate protection and resilient infrastructure
GEMI solves the problem of inaccurate, large-scale weather models and missing climate data by enabling high-resolution, decentralized real-time forecasting – globally, efficiently, and people-powered.
The BRIGAID Business Development Programme has been successfully completed. A MAF+ assessment has been conducted and its results have been enriched and incorporated into a business plan document.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
GEMI uses a decentralized network of weather stations and smartphones to collect and process real-time weather and climate data. Local stations provide standard metrics (e.g., temperature, pressure, precipitation), while smartphones contribute microdata such as altitude, pressure variations, and optionally sound or images. This data is pre-processed by edge AI directly on the device, anonymized, and transmitted through a distributed network to a central AI platform. There, high-resolution weather models are created—with accuracy down to 100 m instead of the traditional 10–28 km grid. By distributing computation (similar to SETI@home), GEMI is scalable, energy-efficient, and globally applicable. The system delivers more precise forecasts, faster early warnings, and valuable data for climate research—even in underserved regions.
GEMI (Global Real-time Meteorology Initiative) is a next-generation weather and climate data platform that leverages the power of distributed networks, smartphones, and artificial intelligence to deliver real-time, high-resolution forecasts and climate insights.
How it works:
1.Data Collection:
GEMI integrates data from local weather stations (temperature, wind, pressure, humidity, precipitation, UV) and enhances it with real-time sensor data from smartphones—such as air pressure, altitude, temperature, and optionally audio or images.
2.Edge Processing:
A lightweight AI on the smartphone filters and analyzes the data locally (Edge AI), reducing unnecessary transmission, preserving privacy, and ensuring efficient use of device resources.
3.Distributed Computing:
Devices voluntarily contribute unused processing power (e.g., at night while charging) to perform local calculations. These partial models are sent via secure decentralized networks to a central AI engine.
4.Model Synthesis:
A cloud-based AI system merges the distributed results into a coherent, high-resolution global weather and climate model with precision down to 100 meters. Continuous learning and feedback improve model accuracy over time.
5.Output & Use:
Forecasts, alerts, and real-time weather maps are delivered to users via the GEMI app, partner APIs, or emergency response systems.
Development approach:
The system is built modularly using existing mobile platforms (iOS/Android), open data standards, and scalable AI frameworks. It starts with regional pilots (e.g. Alpine region) using existing weather stations, then scales globally as more users join. Edge AI and distributed computing frameworks like BOINC/SETI@home serve as technical blueprints. Privacy, security, and energy efficiency are key design principles. GEMI is designed to operate independently or as a supplement to existing forecasting systems, drastically improving micro-scale accuracy, especially in regions without dense infrastructure.
This innovative, citizen-powered system democratizes climate intelligence—faster, smarter, and more inclusive.
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