Climate Loop is an AI and community powered platform designed to promote climate literacy by making extreme weather alerts easier to understand and act upon.
The platform translates and explains official alerts issued by meteorological agencies and civil protection authorities, transforming technical and structured information into clear and accessible guidance.
In addition, Climate Loop incorporates real time contributions from members of the local community who document and report climate related events in their region. By combining institutional alerts with community generated insights, the platform strengthens situational awareness and empowers individuals to make informed decisions in the face of extreme weather events.
Climate Loop is specifically designed to support communities affected by climate change and natural disasters, particularly those who lose homes, livelihoods, or safety due to extreme environmental events. These populations are often among the most exposed and, at the same time, among the least served by complex technical information systems. By simplifying and contextualizing alerts, the platform aims to increase accessibility, preparedness, and resilience for everyone, specially those who need it most.
Our goal is to bridge the gap between information and action. Technical data alone is often not enough to drive protective behavior. Climate Loop translates raw climate measurements into clear instructions:
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Technical data: "Precipitation: 80 mm expected."
Actionable guidance: "The river level will rise in 15 minutes. Do not cross any potential watercourses, even if they are small. Move now to your community's designated safe point." -
Technical data: "Wind speeds will reach 90 km/h."
Actionable guidance: "Strong winds are approaching. Secure loose objects and stay indoors until the storm passes." -
Technical data: "Temperature will drop to -5°C tonight."
Actionable guidance: "Freezing conditions expected. Cover exposed plants and ensure pets have warm shelter."
By presenting both the technical measurements and clear instructions, Climate Loop ensures users understand the situation and know exactly what to do, bridging the gap between information and action and potentially saving lives.
By contextualizing complex climate data into immediate, practical instructions, the platform transforms alerts into actionable guidance that can directly support safer decisions and potentially save lives.
The application also includes an AI powered assistant that allows users to interact conversationally with alerts, ask follow up questions, and request additional context about potential risks and recommended actions. This interactive layer enhances understanding by adapting explanations to the user’s needs in real time.
Additionally, Generative AI is used to provide automatic multilingual translations of community contributions and alert explanations. Contributors can submit information in their own language without concern for translation, while readers can choose to access content in their preferred language. By enabling seamless idiomatic translation, Climate Loop helps break linguistic barriers and ensures broader accessibility across diverse communities.
The "loop" in Climate Loop reflects a continuous feedback cycle. Community contributions and environmental data collected through distributed IoT devices are not only displayed to users but are also designed to enrich and continuously improve the predictive models over time. As more localized data is gathered, the system becomes more accurate, more contextual, and more responsive to the specific realities of each region. This creates a virtuous cycle in which communities both benefit from and actively strengthen the intelligence of the platform.
- System Architecture Overview
- Demo Script
- Datasets and Data Sources
- Governance and Data Responsibility
- Model Thresholds, Uncertainty, and Fallback Behavior
- Product Roadmap
- Marketing Research
Value proposition: Enable low-cost, scalable capture of hyperlocal climate data by combining community reports and affordable IoT sensors deployed in remote and underserved areas, reducing dependence on expensive infrastructure. This data is fused with official alerts and processed through machine-learning models to generate clear, plain-language guidance, translated and adapted for people with low literacy, multiple languages, and accessibility needs. The platform delivers actionable, life-saving forecasts and alerts while embedding strong AI governance by design, including risk classification, data-quality monitoring, human-in-the-loop controls, decision logging, and safe fallback logic, ensuring compliance with the EU AI Act and responsible AI operation.
Market and clients: The platform addresses a global gap: from 1970‑2021 there were 11 778 weather‑related disasters causing >2 million deaths and US$ 4.3 trillion in losses, and yet only half of countries have multi‑hazard early‑warning systems. Early‑warning investments yield a ten‑fold return and reduce mortality by six times. The weather‑information technologies market was worth US$ 9.2 billion in 2025 and is projected to reach US$ 18.9 billion by 2033, while the hazard‑warning segment had a US$ 1–1.5 billion market size in 2024 with a 25–30 % CAGR. This demonstrates commercial headroom and underscores the need for solutions like Climate Loop. Primary customers include local and regional governments, NGOs/humanitarian agencies, farmers, insurance and finance sectors, critical‑infrastructure operators, and community members; the platform also partners with national meteorological services, WMO/UN agencies, research institutes, IoT manufacturers and cloud/AI providers.
Channels, pricing and revenue streams: Climate Loop distributes its services through a web platform, planned native mobile apps and SMS/voice channels, as well as an API for institutions and technology companies. Institutional subscription plans (tiered by population served or API calls) and paid API/data licensing underpin revenue, while keeping basic alerts free for end‑users, increasing the data collection. Premium features (custom forecasts, early access) and consulting/integration services for governments and insurers provide additional streams. Public funding and sponsorships can subsidise alerts for vulnerable regions. For context, commercial weather APIs such as OpenWeather offer free daily quotas for up to 1 000 calls and charge per call thereafter, illustrating a viable pay‑per‑call model. The report also notes that 84 % of adults in developing economies own a mobile phone, validating mobile‑centric delivery.
Cost structure: Major costs fall into data acquisition/processing, IoT hardware deployment, cloud infrastructure, software development, community management, AI governance compliance, customer support and marketing. Examples of public early‑warning systems illustrate the scale of building an alternative strategy with a lower cost and using community data collection: the U.S. ShakeAlert earthquake network cost about US$ 100 million to build and US$ 39 million annually to maintain, while California’s state system was built for US$ 28 million with US$ 17 million annual operating costs. Using modular, low‑cost sensors and leveraging open data (ERA5, Open Meteo) helps keep Climate Loop’s costs low, while diversified revenue streams and partnerships ensure long‑term sustainability.
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For the best experience, open the prototype directly on your smartphone browser:
https://climateloop-prototype.lovable.appThis allows you to test more features like the community contribution flow that requires taking photos.
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If you choose to view the project on a computer browser, please note:
- You will be able to navigate the platform and explore most features.
- However, you will not be able to properly test the community contribution flow that involves capturing photos with the device camera.
Using a mobile device is strongly recommended to experience the prototype as intended.
This is not a native mobile application. It is a web based application developed as a prototype to validate the concept, flows, and core functionalities.
The long term vision, if the project continues to evolve, is to develop fully native mobile applications for iOS and Android, enabling deeper integration with device capabilities, improved performance, and enhanced offline support.
For now, accessing it via a mobile browser provides the closest experience to the intended final product.
- Climate data
- IoT data
- Emergency alerts
- Community contributions
- Frontend
- Backend
- Artificial Intelligence
The climate data used for the Machine Learning based predictions is real. A model was implemented using ERA5 data and the Open Meteo API, enabling us to generate predictions for Lugo, Galicia.
We do not rule out incorporating additional global scale historical climate datasets in the near future, including MERRA 2 from NASA, NCEP NCAR Reanalysis from NOAA, and other Copernicus Climate Change Service datasets, in order to further enhance model robustness and spatial generalization.
We developed a proof of concept IoT device using an Arduino UNO R1 board equipped with a BMP280 sensor for temperature and atmospheric pressure measurements, as well as a rain detection module capable of identifying water droplets.
The objective of this prototype was to validate the feasibility of local environmental data collection, as well as to assess sampling frequency, response time, and measurement consistency.
Based on the output generated by this device, we created mocked datasets simulating multiple distributed IoT devices operating across the Lugo, Galicia region. This allowed us to demonstrate how real time sensor networks could enrich the platform with hyperlocal environmental data while maintaining a controlled prototype environment.
For emergency alerts, we analyzed the structure defined by the Common Alert Protocol CAP standard and examined example RSS feeds published by governments and official emergency management and civil protection authorities.
The alerts are inspired by real emergency alerts but are currently mocked, since we selected Galicia as the region for the functional prototype and there was no guarantee that active alerts would be available for that region at this time.
One of the core components of Climate Loop is the community driven reporting mechanism, designed under a privacy by design and data minimization framework.
Users are required to create an account in order to submit contributions. Only the minimum necessary data is collected for authentication purposes: name, email address, and password.
Submissions include images. An AI based validation layer leveraging computer vision techniques is architecturally defined to:
- Detect and block images containing identifiable individuals
- Identify content inconsistent with the reported event description
- Reduce the risk of malicious or manipulated submissions
At this stage, the AI image validation layer is planned but not yet fully implemented. Community contribution data is currently mocked.
The frontend is a functional prototype developed using Lovable, enabling rapid iteration and interface adjustments according to project requirements.
The architecture is not platform locked. The generated code can be exported and migrated to other development environments, allowing continued evolution with a stronger focus on scalability and performance optimization.
Lovable Cloud was used to provision the backend layer and managed database for the prototype.
The backend currently stores:
- Weather prediction data generated through the Machine Learning pipeline
- Mocked emergency alerts structured according to the CAP model
- Mocked community contribution data
All backend components are exportable, ensuring portability and future migration to environments such as Google Cloud Platform for scalability and infrastructure hardening.
A Machine Learning approach was implemented to generate time series weather predictions for Lugo (Galicia, Spain), Valencia (Spain), Dakar (Senegal), and Rio de Janeiro (Brazil) using real historical and operational datasets.
For the AI assistant component, we integrated the Gemini 3 Flash Preview model provided by Google within the Lovable environment.
The assistant receives structured alerts and community descriptions and transforms them into simplified, user friendly explanations.
Generative AI is also used to automatically translate community contributions and alert explanations into multiple languages, breaking linguistic barriers and ensuring broader accessibility.