# Private-Sector, Awareness & Consumer Ideas — a second-wave idea set *NSF NCAR / RAL · strategy working document · draft July 2026* The first ten ideas were framed around agency/research funding. This set is deliberately different: ideas aimed at **private-sector revenue** (the diversification the moment demands) and at **public awareness / science communication** (cheap, high-visibility, mission-aligned). Two of them — the AI-footprint visualizer and the shadow-study tool — are being prototyped as browser apps; the rest are strategic concepts. --- ## A. Private sector — where weather/climate software earns money ### A1. Independent hazard verification for parametric insurance & cat bonds ★ strongest **Concept:** The parametric insurance and catastrophe-bond markets pay out based on a measured hazard trigger (wind speed at a point, rainfall total, quake magnitude). The whole market rests on a **trusted, neutral hazard measurement** everyone agrees on — and disputes over trigger calculation are a known pain point. NCAR/RAL, as a neutral scientific institution, could be the **independent verifier / calculation agent** for weather-hazard triggers, and the certifier of the hazard models behind them. This is the verification thesis (Idea #1) pointed at a market that already pays for trust. **Who pays:** Re-insurers, ILS/cat-bond issuers, brokers (Guy Carpenter, Aon), parametric MGAs. **Why NCAR:** neutrality + scientific authority is the product; a vendor calculation agent has a conflict of interest. **Fit:** directly reuses Idea #1's machinery. High fundability, high strategic fit. ### A2. Wildfire & hurricane exposure stress-testing for insurers **Concept:** Turn RAL's fire (WRF-Fire/CFBM) and hurricane models into a scenario stress-testing service: "what does our book look like if *this* fire, under *these* winds, hits *this* WUI?" Couples to the evacuation/impact engine (Idea #5) and downscaling (Idea #8). **Who pays:** Primary insurers, re-insurers, state FAIR plans, wildfire-exposed utilities. **Why NCAR:** best-in-class physical hazard models + neutrality; catastrophe-model vendors (Moody's RMS, Verisk) are the incumbents to complement/verify, not out-build. ### A3. Climate-conditioned catastrophe scenario generation **Concept:** Physically-consistent, downscaled, climate-adjusted event sets (today's and future climate) for cat modeling — the "how is the tail changing" question insurers increasingly must answer. Built on downscaling (#8) + emulation (#4). **Who pays:** Re-insurers, asset managers, climate-risk analytics firms, regulators (stress tests). ### A4. Energy & renewables weather analytics (extend an existing revenue line) **Concept:** RAL already sells wind/solar forecasting (Xcel, Kuwait, Korea). Modernize it into a cloud-native, API-first product with uncertainty and downscaling — grid operators, traders, and renewable developers pay for local irradiance/wind with honest confidence bounds. **Who pays:** Utilities, ISOs/RTOs, energy traders, renewable developers, storage operators. **Why now:** a mature, proven, revenue-generating capability that just needs productizing. *Common thread:* A1–A3 all lean on the same neutrality-as-product logic and reuse Ideas #1/#5/#8. The re-insurance sector is the single most promising private buyer because its business **is** paying for trusted hazard science. --- ## B. Awareness / science communication ### B1. AI compute-footprint visualizer ★ being prototyped **Concept:** A browser tool that makes the resource cost of AI concrete and visceral: for a given model / query / session length, visualize the estimated **energy (Wh), water (mL), and carbon (g CO₂e)** — and translate them into intuitive equivalents ("≈ boiling N cups of tea," "≈ driving M meters," "≈ P seconds of a hairdryer"). Tunable by model size, data-center efficiency (PUE/WUE), grid carbon intensity, and cooling type, with clearly-cited ranges and an honest "estimates vary widely" disclaimer. **Why NCAR:** sits exactly at the energy–water–climate nexus NCAR studies; it's science communication about the climate cost of a technology everyone now uses, and it's cheap and highly shareable. A credibility-forward, neutral treatment (with real uncertainty) is something only a science institution does well. **Who it serves:** public education, journalists, policymakers, NCAR's own outreach/mission; funders: NSF education/broader-impacts, philanthropy, foundations. **Honesty caveat:** the numbers are genuinely uncertain and contested; the tool must foreground ranges and assumptions rather than a single scary figure. Done wrong it's misinformation; done right (transparent, tunable, cited) it's a model of good science communication. **Status: prototyping as `ai-footprint.html`.** ### B2. "Your weather, warming" — personal climate-stripes / local-trend explorer **Concept:** A browser tool where anyone enters a location and sees how *their* local climate has shifted (warming stripes, changing extremes, shifting seasons) from public reanalysis/observations — personal, local, and shareable. Communicates climate change through lived local experience. **Who it serves:** public engagement, education, media; leverages NCAR's data authority. ### B3. Extreme-event "what actually happened" explainers **Concept:** Fast, trustworthy, visual post-event explainers (the heat dome, the atmospheric river, the derecho) built on NCAR data — neutral scientific communication during the window when the public is paying attention. --- ## C. Consumer / built-environment ### C1. Shadow-study & solar-design tool ★ being prototyped **Concept (from the user's sketch):** An interactive browser app that shows where the sun will be — and where shadows fall — at any location, date, and time of year, to help design homes and sites: **window placement, building orientation, passive-solar gain, shading, and orientation for fastest snow melt** off roofs/driveways/walkways. The user tunes latitude/longitude, date (season), and time, plus simple massing (a house box, trees, neighbors), and watches the sun path and shadows update; a "snow-melt / solar-gain" mode highlights which surfaces get the most winter sun. **How it works (self-contained):** a solar-position algorithm (NOAA SPA or a compact approximation) computes sun azimuth/elevation from lat/lon/date/time; cast shadows from simple 3D massing; integrate daily/seasonal sun exposure per surface. All client-side, no libraries. **Why NCAR:** applies the same solar-geometry science behind RAL's solar-energy forecasting to an everyday, tangible problem; a strong public-facing demonstration of the lab's relevance to homes and communities. **Who it serves / who pays:** homeowners, architects, builders, solar installers, passive-house designers; funders/partners: DOE building-technologies, energy-efficiency programs, education, or a freemium/pro consumer/commercial model. Snow-melt orientation is a genuinely useful, underserved niche in cold-climate design. **Status: prototyping as `shadow-study.html`.** ### C2. Rooftop-solar / site-suitability quick-look **Concept:** Extend the shadow tool: given a location and roof orientation, estimate annual solar exposure and rough PV suitability — a consumer on-ramp that could feed RAL's energy-forecasting expertise. --- ## Prioritization note - **Build now (browser prototypes, cheap, high-visibility):** B1 (AI-footprint) and C1 (shadow-study) — both are self-contained, demonstrate NCAR science on relatable problems, and are exactly the kind of shareable artifact that earns attention and goodwill. - **Pursue for revenue (strategic):** A1 (parametric/cat-bond verification) is the standout — it monetizes the same neutrality-as-product thesis as the top-ranked idea, in a market that already pays for trusted hazard science. A4 (energy analytics) is the fastest revenue because it already exists and just needs productizing. - **Communication wins:** B1/B2/B3 are low-cost, mission-aligned, and buy public goodwill at a moment when the institution's public case matters.