Measuring rain, hail, and snow together
CoCoRaHS (cocorahs.org) scores 82 out of 100 on Brand Analyzer, placing it in the Strong Brand tier among Non-profit brands, Education brands, in the 71st percentile of 4111 Non-profit brands.
CoCoRaHS (Community Collaborative Rain, Hail and Snow Network) is a non-profit citizen science project that relies on a network of trained volunteer observers to collect precipitation data across communities. Its mission is to provide accurate, high-quality rain, hail, and snow measurements that supplement official weather monitoring. The organization serves weather enthusiasts, students, educators, farmers, and community members, combining grassroots participation with weather education to fill gaps in existing meteorological data networks.
| Brand Name | CoCoRaHS |
|---|---|
| Domain | cocorahs.org |
| Industry | Non-profit, Education |
| Main Competitors | Weather Underground (87/100), mPING, GLOBE Observer, Zooniverse, SciStarter |
Moz Domain Authority 57/100 vs category average 52 / leader 97 - a strong backlink profile, so AI systems frequently encounter mentions of the brand.
Ranked #142,382 on the Tranco list of most-visited sites - modest traffic makes the brand easy for AI models to overlook.
CoCoRaHS has a dedicated Wikipedia entity - a top-weighted signal AI models rely on to identify and describe the brand.
The homepage meta description reads "To provide accurate, high-quality precipitation data through a network of volunteer observers across the community." (No meta description) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: No structured data or Open Graph tags detected. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: robots.txt present, no AI bot restrictions - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Detected tech stack: ASP.NET.
Social footprint: verified profiles on Facebook, YouTube, X (Twitter), Instagram, GitHub; no detected presence on LinkedIn - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked September 2026.
When asked "What is CoCoRaHS?", Claude could identify the brand as of September 2026. CoCoRaHS stands for the Community Collaborative Rain, Hail and Snow Network, a grassroots volunteer network of citizen scientists who measure and report precipitation data from their backyards across the US and Canada. The data collected is used by meteorologists, hydrologists, r Best known for crowdsourced volunteer rain, hail, and snow measurement network used by the National Weather Service and researchers.
When asked "Best brands similar to CoCoRaHS?", Claude would recommend CoCoRaHS as of September 2026. It's a well-established and widely respected citizen science program, so I'd mention it for anyone interested in weather monitoring, citizen science, or volunteer environmental data collection.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
CoCoRaHS has limited AI-search visibility, scoring 49/100 on Brand Analyzer's AI visibility composite (visibility 60, trust 29, recommendation 54). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: CoCoRaHS official site
CoCoRaHS scores 54/100 on Brand Analyzer's AI recommendation signal, indicating it is reasonably likely to be surfaced when AI assistants like ChatGPT suggest Non-profit options. Recommendation depends on crawlability, structured data, and category authority; a Wikipedia presence helps.
Sources: CoCoRaHS official site
CoCoRaHS is a citizen science project and non-profit organization that operates a volunteer-based network for collecting precipitation data, including rain, hail, and snow measurements, from communities across the network's coverage area.
Sources: CoCoRaHS official site
CoCoRaHS trains and coordinates volunteer observers who measure and report local precipitation data, such as rainfall, hail, and snow amounts, to build a community-based dataset that supplements official weather monitoring networks.
Sources: CoCoRaHS official site
CoCoRaHS can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: CoCoRaHS official site
CoCoRaHS offers a volunteer observer network and training program for measuring and reporting precipitation data (rain, hail, and snow). It provides educational resources related to weather observation and contributes community-collected data to fill gaps in official weather monitoring systems.
Sources: CoCoRaHS official site
CoCoRaHS is used by weather enthusiasts, students, educators, farmers, and community members who are interested in tracking and reporting local precipitation data as volunteer observers.
Sources: CoCoRaHS official site
CoCoRaHS is known for its grassroots, volunteer-driven approach to collecting precipitation data, engaging everyday citizens-including students, educators, farmers, and weather enthusiasts-in scientific data collection and weather education.
Sources: CoCoRaHS official site
CoCoRaHS scores 49/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
CoCoRaHS has a limited AI-visibility profile at 49/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is visibility (60/100) and its weakest is trust (29/100). It benefits from a recognised Wikipedia company entity, a Wikidata knowledge-graph entry and a global traffic rank of #142,382 (Tranco). The main gaps holding it back: thin schema.org structured data and no llms.txt to steer AI to its best pages. In a live check, Claude could already identify CoCoRaHS from memory (September 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
Ranked closest to CoCoRaHS: Climate Impact Partners (82/100), CLASP (Center for Law and Social Policy) (82/100), COSI (82/100), CourtListener (82/100).
A step up - brands to learn from: Workaway (91/100), Wikimedia (91/100).
Category leader: PBS (97/100).
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