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Silurian is a weather-forecasting startup founded by former Microsoft AI researchers who worked on Aurora, Microsoft’s atmospheric foundation model. Its Generative Forecasting Transformer (GFT) is designed to generate global forecasts, while a newer U.S. regional model targets faster, higher-resolution updates. Silurian has published benchmark results and launched API products, but its accuracy claims are largely company-reported; independent validation, customer scale, and public pricing remain unclear.
Who founded Silurian?
Silurian was founded in 2024 by Cristian Bodnar, Jayesh Gupta, and Nikhil Shankar. Y Combinator lists Bodnar as chief scientist, Gupta as CEO, and Shankar as chief engineering officer; the company joined YC’s Summer 2024 batch and is based in Kirkland, Washington. Y Combinator’s company listing describes Silurian’s goal as building foundation models to simulate Earth, starting with weather.
Mark Baum was also part of the launch team, but GeekWire reported that he later left the company. He should not be assumed to be part of Silurian’s current operating team.
What did the founders do at Microsoft?
The founders’ Microsoft connection is relevant because they worked on Aurora, an AI foundation model for Earth’s atmosphere. That work put them close to the practical challenges of applying machine learning to weather: training on large atmospheric datasets, comparing AI forecasts with numerical weather prediction, and turning model output into decisions that matter to users.
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That background does not make Silurian a Microsoft subsidiary or establish Microsoft endorsement. The available reporting supports describing its founders as former Microsoft AI researchers, not presenting Silurian as a Microsoft product or spinout.
What is Silurian’s GFT model?
Silurian’s Generative Forecasting Transformer, or GFT, is a machine-learning model intended to simulate future global atmospheric states. The company says the model has 1.5 billion parameters. GeekWire reported that the global model produces forecasts extending to two weeks at roughly 11-kilometer resolution. Those are reported product characteristics, not a guarantee of performance at every location or lead time. Silurian’s Earth API announcement describes the model and its intended access through the company’s API.
Traditional numerical weather prediction repeatedly solves equations describing atmospheric physics, using extensive computing and observational infrastructure. An AI model instead learns patterns from historical data and generates future states from an estimate of current conditions. Once trained, such a model may generate forecasts faster and at lower runtime cost, but its results still depend on input data, initialization, the conditions represented in training, and how performance is measured.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors“Generative” here means that the model generates forecast states from learned patterns; it does not mean arbitrary weather invention. Silurian describes its models as physics foundation models, but its public material does not fully specify their architecture or physical constraints. It would be premature to call GFT physics-free—or to assume that its design guarantees physically consistent results.
What are GFT’s accuracy claims worth?
Silurian’s published global evaluations compare GFT with ECMWF’s HRES, Google DeepMind’s GraphCast, and regional systems including HRRR and ICON. The company says its evaluations cover 2023 and use weather-station observations from Meteostat alongside ECMWF analysis or reanalysis datasets. Silurian defines its skill score as relative improvement against a selected baseline. The company’s evaluation description is the primary source for those claims.
Rank #2
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- AUTHENTIC HYPER-LOCAL DATA: Monitor your actual home and backyard weather conditions with our wireless and Wi-Fi-enabled sensor array measuring wind speed/direction, temperature, humidity, rainfall, UV intensity, and solar radiation
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- ENHANCED WIFI: Enables your station to transmit its data wirelessly to the world's largest personal weather station network (optional setting)
- JOIN THE COMMUNITY: Connect to Ambient Weather Network to customize your dashboard tiles, share hyperlocal weather conditions via social feeds and create your own forecasts (coming soon)
These results are evidence that Silurian has evaluated its model against established systems, but they are not independent confirmation that GFT is universally more accurate. Rankings can change by variable, forecast lead time, geography, season, and verification dataset. A global score does not establish better performance at a particular wind farm, utility service area, or agricultural site. Comparisons can also be difficult when models use different input analyses, update schedules, resolutions, or post-processing.
Accuracy is not the same as operational value. A modest improvement in wind or solar forecasts could be useful to one grid operator if it changes dispatch decisions; the same difference may not matter to another buyer. Buyers need location- and use-case-specific tests rather than relying on a single aggregate score.
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Silurian announced GFT-US in April 2025 for the contiguous United States. The company says it runs at approximately 3-kilometer resolution, updates hourly, and is delivered a median of about one minute after the hour. It also says the model is available roughly 20 minutes before NOAA’s HRRR forecast. In its published evaluation, Silurian reports better results than HRRR on selected temperature and wind-speed measures through particular forecast lead times. Its example uses more than 2,000 stations across the contiguous U.S.; the company cautions that station-data quality varies and regional biases exist. Silurian’s GFT-US announcement provides those qualifications.
These specifications describe different things, not interchangeable measures of quality:
- Resolution is the spacing of the model grid; 3 kilometers does not automatically mean a forecast is accurate at a specific site.
- Delivery time is when a forecast becomes available. Earlier output may help time-sensitive operations, but only if it is sufficiently reliable to change a decision.
- Accuracy is closeness to observations under a stated metric, variable, location, and forecast horizon.
- Operational usefulness depends on whether the forecast improves a real workflow or outcome.
What can customers access?
Silurian’s Earth API is aimed at developers and organizations integrating weather data into their systems. The company describes global coverage over land and sea, hourly forecasts, a browser playground, and Python and TypeScript SDKs. Its announced variables include 100-meter wind speed and direction, surface solar radiation, snowfall accumulation, precipitation type, and other atmospheric conditions. The Earth API announcement describes the offering.
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- Simple Setup and Use: Install 2 AA batteries (not included) in the outdoor weather station sensor and easily hang on a post or tree branch using the integrated hanger to begin receiving your weather forecast and hyperlocal conditions
- Real-Time Weather Conditions: This indoor outdoor weather station has an indoor temperature gauge and an outdoor temperature thermometer for indoor and outdoor temperature, humidity, and barometric pressure trends from an outdoor temperature sensor
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- Dynamic Forecast Icons and Moon Phase: With multiple thermometers & weather instruments data, this digital indoor outdoor thermometer display has trend arrows and provides the current moon phase to further impact your weather monitoring capabilities
Silurian’s public API documentation lists hourly and daily forecast endpoints and variables such as temperature, feels-like temperature, precipitation accumulation and probability, snowfall, cloud cover, humidity, wind, pressure, dew point, downward solar radiation, and wind at 100 meters. It also lists portfolio, past-forecast, experimental U.S. regional, and cyclone endpoints. Documentation alone does not establish that every endpoint is generally available, production-ready, or included in every commercial arrangement; confirm access, authentication, quotas, and terms with Silurian.
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Who could use Silurian forecasts?
Silurian’s strongest apparent fit is with organizations whose operations or revenue are materially affected by weather. The applications below are plausible uses, not evidence of named customer deployments.
Renewable energy and utilities
Wind and solar operators may use forecasts for generation planning, grid balancing, dispatch, maintenance, curtailment, and transmission planning. Utilities may also use weather data to anticipate demand, prepare for severe weather, and plan around icing or asset risks. Silurian specifically highlights wind at 100 meters and solar radiation, but buyers would need to test performance against their own sites and operational data.
Agriculture, transport, and infrastructure
Farmers and agricultural businesses could apply forecasts to irrigation, frost or heat alerts, crop protection, harvest timing, and logistics. Transport and infrastructure operators might use them for routing, aviation or maritime planning, construction schedules, and severe-weather disruption management. Silurian’s YC profile also names agriculture, logistics, infrastructure, and defense among its intended sectors; that positioning does not establish adoption in each one.
Insurance and weather risk
Potential insurance uses include weather-risk analysis, claims triage, parametric triggers, and accumulation analysis. High-impact decisions require more than average forecast scores: a buyer should examine event-level performance, uncertainty information, and how outputs can be audited.
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- Weather Clock: The indoor weather station display is a large, color LCD Display with the current time, date, and an adjustable dimmer, making it convenient to read and easily view indoor and outdoor data, time, and conditions
- Weather Forecast: The outdoor weather station collects elevation data and combines it with barometric pressure data from the indoor weather station to provide a personalized weather forecast 12 hours from your current conditions
How does Silurian compare with established forecasting options?
Silurian is entering a field with several different kinds of competitors. A government forecast system, an AI research model, and a commercial weather API are not equivalent products.
Public forecasting agencies
NOAA and the National Weather Service provide foundational U.S. forecasts, observations, models, warnings, and public-service infrastructure. ECMWF is a major global forecasting organization and benchmark. Silurian is not replacing that whole ecosystem; it is trying to offer a faster, more specialized, or commercially integrated forecasting option. A fair comparison must account for more than one model score: public systems also provide broad access, warning services, operational continuity, and meteorological expertise. See NOAA and the National Weather Service and ECMWF.
Big Tech AI weather models
Microsoft Aurora is part of the founders’ technical background. Google DeepMind’s GraphCast and GenCast are reference points in AI weather forecasting, while Nvidia has also pursued weather-model initiatives. The important distinction for a buyer is whether a model is a research result, a publicly accessible model, or an operationally supported product with the coverage, integration, and service terms needed for business use. Google DeepMind’s research portal covers its work.
Commercial weather services
Commercial providers may combine forecasts with APIs, alerts, historical data, radar or satellite products, nowcasting, or industry-focused risk analysis. Silurian’s apparent differentiators are its own foundation models, rapid forecast generation, energy-related variables, and the possibility of adapting models to customer assets or observations. Public evidence does not yet establish a durable advantage in reliability, coverage, or total cost over commercial alternatives such as Tomorrow.io, OpenWeather, or The Weather Company.
What should a prospective customer verify?
A technical buyer should evaluate Silurian against the forecast decisions it actually needs to make, not just against a global leaderboard. A pilot or procurement review should cover:
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- WWVB Atomic Weather Station with Auto DST: Weather atomic clock with indoor/outdoor temp always keeps precise time and date by receiving the WWVB atomic signal. The self setting digital weather clock will automatically adjust to daylight saving time with auto DST feature, no more resetting twice a year.
- Personal Weather Forecast Station: This weather stations wireless indoor outdoor predicts the next 12-24 hours weather condition with a 7-day calibration through the pressure of your location which provides you a better outing experience.
- 5 Level Adjustable Backlight Brightness: The weather clock indoor outdoor temperature atomic with backlight dimmer function helps you avoid high-intensity light that disturb your sleep and easily check the weather situation during the day.
- Site-level performance: compare the relevant variables and horizons with local stations, sensors, SCADA, or asset data; measure seasonal and regional bias.
- Resolution and terrain: test whether the grid captures local coastlines, mountains, urban effects, or wind-farm conditions. A 3-kilometer grid does not by itself establish hyperlocal accuracy.
- Latency and cadence: confirm when forecasts are generated and delivered, and whether the workflow needs hourly updates, minute-level nowcasting, or multi-day planning.
- Variables and uncertainty: check for required fields such as hub-height wind, solar radiation, precipitation type, snowfall, or icing. Ask whether the service supplies probabilities, prediction intervals, or ensembles, rather than only point forecasts.
- Backtesting: request reproducible out-of-sample results with separate training, validation, and test periods; examine extreme events as well as average performance.
- Operational terms: verify API uptime, rate limits, retention, recovery, support, incident response, and how model upgrades are versioned.
- Data rights and accountability: establish whether customer observations can be used for adaptation, who owns derived models and outputs, and whether forecasts can be audited for consequential decisions.
What remains unproven?
Independent and extreme-event validation
The central performance evidence available publicly is Silurian’s own evaluation material and company statements. Aggregate scores may hide weaknesses during hurricanes, atmospheric rivers, tornado-supporting conditions, rapid cyclogenesis, heat waves, ice storms, or unusual precipitation. A customer should ask for event-level verification and detailed definitions of each comparison: the exact NOAA or ECMWF product, variable, lead time, geography, metric, verification dataset, and whether the forecast is deterministic or probabilistic.
Data inputs and changing conditions
Weather forecasts depend on accurate initial conditions. Silurian’s public material does not establish in comprehensive detail which observations, analyses, and external datasets feed every product, or whether access to those inputs creates dependencies or licensing constraints. Buyers should also consider distribution shift: climate trends, changing observation networks, and unprecedented conditions can make historical training data less representative of future weather.
Customer traction, pricing, and service commitments
The public sources cited here do not establish named customer deployments, measured customer outcomes, pricing, quotas, revenue, or enterprise service levels. Silurian clearly targets business users, but that is different from proving product adoption or a mature support operation. Its public API documentation and announcements are useful evidence of product direction, not proof that every listed feature is generally available.
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Cost and energy claims
AI inference can be fast after training, but training large models also consumes substantial computing resources. GeekWire reported the company’s argument that AI forecasting can use a fraction of the operational energy of continuously running traditional supercomputers, while noting training energy costs. That is a company claim, not a comprehensive independently verified lifecycle comparison. GeekWire’s profile provides the reported context.
Should Silurian be taken seriously?
Silurian is more than a model-announcement headline: it has named founders with relevant atmospheric-AI experience, a global GFT, an Earth API, and a regional U.S. product with published specifications and evaluation claims. That makes it a credible entrant worth considering for technical and weather-sensitive businesses, especially those exploring energy applications.
The decisive test is still operational: whether its forecasts are reliably better for a buyer’s locations and decisions, including difficult events, and whether that gain justifies integration and commercial costs. Its published benchmarks are a starting point for evaluation, not a substitute for independent validation or a location-specific trial.
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