Silurian
Tailored foundation models for weather and grid forecasting, trained on your own asset and environmental data feeds.
Silurian is one of very few vendors with published, named proof that a tailored weather model beats a generic feed on a real operational decision — 0.72 average precision on rime-ice, 72-hour warnings, on the record at CIGRE 2025 with Hydro-Québec. It also releases real infrastructure, not just decks: radrs and the raystacks work landed in September 2026 as open tooling you can run yourself. That combination makes it worth a conversation for utilities and agencies whose losses concentrate in icing, cyclone, or asset-level weather risk. Teams that need a cheap forecast API, or that can't dedicate data engineering to co-training, should look at generic forecast feeds instead.
Verified 2d ago · liveness 58/100 · cite: rightaichoice.com/tools/silurian
- Energy utilities with rime-ice, icing, and severe-weather grid risk
- Government and disaster-management agencies needing localised weather intelligence
- Infrastructure operators with asset-level weather risk in icy or remote regions
- Enterprises with mature data pipelines ready to co-develop a custom model
- Individuals or teams needing free or instant weather API access
- Small businesses without dedicated data pipelines or data engineering staff
- Hobbyists and consumer weather app users
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Skip Silurian if you need a cheap, instant weather API rather than a co-developed model, or if you have no reliable asset telemetry and no data engineering staff to feed one.
Tailoring a GFT to your feeds means committing client-side data engineering time — sensor coverage gaps, feed cleaning, and pipeline maintenance land on your team, not the vendor's.
Silurian scopes engagements around your risk surface, so the practical budget comparison is against the internal cost of building a comparable forecasting model yourself — a multi-engineer research effort — rather than against a per-call weather API subscription. For a utility or agency with concentrated icing or cyclone losses, that framing usually favours a vendor deployment. For a small operator with limited data coverage, a generic forecast feed will cost far less and may be adequate.
In short
Silurian — Tailored foundation models for weather and grid forecasting, trained on your own asset and environmental data feeds. Best for Energy utilities with rime-ice, icing, and severe-weather grid risk, Government and disaster-management agencies needing localised weather intelligence, Infrastructure operators with asset-level weather risk in icy or remote regions. Contact Sales pricing.
What's new in Silurian
Checked 2 days agoAcross the latest 2 updates: 1 launch and 1 news mention.
Finding models in a Raystack
Silurian explains how raystacks organize radar observations into aligned arrays for training across sweep and volume boundaries, so radar data is consistent before it reaches a model.
Introducing radrs: faster NEXRAD loading in Python
Open-source NEXRAD Level 2 reader with a Rust core and an xarray-compatible Python interface, released by Silurian for radar and ML engineers.
What people actually say about Silurian — is it worth it?
We scanned public community sources for Silurian on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Silurian? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Custom Generative Forecasting Transformer (GFT) trained on client data
- Co-training on proprietary asset telemetry and environmental sensor feeds
- Foundation model lineage from Aurora, an Earth-system foundation model
- High-resolution regional weather forecasting
- Rapidly refreshing AI-powered forecast updates
- Day-ahead rime-ice alerts at 0.72 average precision (Hydro-Québec)
- 72-hour severe weather early warnings
- GFT-C variant predicting tropical cyclone intensification and dissipation
- Grid observation data integration for icing and rime warnings
- Asset-level forecasting for utility and infrastructure assets
- Weather impact simulation on physical assets
- radrs: open-source NEXRAD Level 2 reader with Rust core and xarray interface
- raystacks for aligning radar observations across sweep and volume boundaries
- Deployment scoped through a direct vendor engagement
About Silurian
Silurian builds custom Generative Forecasting Transformer (GFT) foundation models for weather and infrastructure risk, trained around the data you already own rather than a shared off-the-shelf feed. The pitch is leverage: your asset telemetry, grid observations, and environmental sensor readings are the thing a generic forecast API cannot see, and Silurian co-trains a model on them. The company comes out of the Aurora research lineage — the team describes Aurora as the first foundation model for the Earth system — and deployments sit with large utilities and government agencies. The published proof points are specific. In work with Hydro-Québec, Silurian's GFT produced day-ahead rime-ice alerts at 0.72 average precision, supporting 72-hour severe-weather warnings and cutting dispatch costs; the results were presented at CIGRE 2025. A second variant, GFT-C, targets tropical cyclone intensification and dissipation, demonstrated on Pacific storm Henriette and Atlantic hurricane Erin. Alongside the forecasting work the team ships open tooling for radar practitioners: radrs, a NEXRAD Level 2 reader with a Rust core and an xarray-compatible Python interface released in September 2026, and a write-up on raystacks, which align radar observations into consistent arrays across sweep and volume boundaries for training. High-resolution regional forecasts round out the core product. Buyers reach Silurian through a deployment conversation. Engagements are scoped around your risk surface, so the useful comparison is not another weather API but the internal cost of building this model yourself — or accepting the resolution and asset-blindness limits of an off-the-shelf feed.
Behind the Verdict
Silurian's proposition is narrow and unusually well-evidenced. Most weather AI companies sell a better general forecast; Silurian sells a model trained on the observations only you have — grid telemetry, icing sensors, asset-level readings — and the published Hydro-Québec result gives that claim a number behind it: day-ahead rime-ice alerts at 0.72 average precision, feeding 72-hour severe-weather warnings and cutting dispatch costs, presented at CIGRE 2025. The GFT-C variant extends the same approach to tropical cyclone intensification and dissipation, with Pacific storm Henriette and Atlantic hurricane Erin as the named demonstrations. The team's lineage matters too: they are the researchers behind Aurora, which they describe as the first foundation model for the Earth system, so the architecture is not a wrapper around someone else's API. Strengths: a tailored model that folds in proprietary feeds generic forecasters structurally cannot see; a research pedigree that produces peer-visible results rather than benchmark charts; and a genuine contribution to the surrounding toolchain. In September 2026 Silurian released radrs, an open-source NEXRAD Level 2 reader with a Rust core and a Python xarray-compatible interface, and published a piece on raystacks, the technique for aligning radar observations across sweep and volume boundaries so training data is consistent. That is the work of people who ingest radar for a living, and it is useful to your team whether or not you ever sign a contract. Where it doesn't fit: the evidence base is deep but narrow — a small number of named deployments concentrated in ice and cyclone risk, so a buyer in, say, heat or flood risk has less to benchmark against. Value depends on the quality and coverage of the feeds you can supply; a thin sensor network means a thin model. And Silurian publishes little beyond a blog and a booking link, so your evaluation will lean on the deployment conversation and your own data audit rather than a product tour. Treat this as a co-development engagement with a research lab attached, not a subscription you switch on.
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Real-world workflow fit
Concrete scenarios for the personas Silurian actually fits — and what changes day-one when you adopt it.
Upload historical grid observation and icing sensor data, co-train a GFT variant, and run day-ahead rime-ice alerts against the 72-hour warning window ahead of winter storms.
Outcome: Dispatch teams schedule preventive work from alerts rather than reacting after ice accumulates, the pattern behind the reported Hydro-Québec dispatch cost reduction.
Feed regional environmental sensor data into a tailored model and use the GFT-C cyclone variant to track intensification and dissipation on an active storm.
Outcome: Localised forecast tracks for the agency's own territory, rather than a global feed whose resolution misses the assets being protected.
Adopt radrs to load NEXRAD Level 2 volumes in Python, then use raystacks to align observations across sweep and volume boundaries before training.
Outcome: Consistent training arrays assembled without writing a Level 2 parser from scratch — usable even before any commercial engagement.
Use Cases
- Deploy a custom foundation model to forecast high-resolution regional weather for an energy grid.
- Train a model on utility-grade grid observation data to flag rime-ice risk 72 hours ahead.
- Simulate asset-level weather impacts for infrastructure resilience planning.
- Predict tropical cyclone intensification and dissipation paths using GFT-C.
- Fold proprietary data feeds into a tailored Earth-intelligence model for a government agency.
- Reduce dispatch costs and operational risk with actionable day-ahead weather alerts.
- Move an asset operator from reactive storm response to preventive scheduling.
- Produce localised forecasts for remote or icy infrastructure where generic models underperform.
Models Under the Hood
as of 2026-10-10
Limitations
- Silurian publishes very little product detail beyond a blog and a booking link, so your evaluation leans on the deployment conversation and an audit of your own data rather than a product tour or published changelog.
- Every engagement starts with a deployment discussion, which implies a co-development cycle and dedicated data engineering on the client side.
- The models are tailored to each client's data, so value depends on the quality and coverage of the feeds you can supply — sparse sensor coverage means a weaker model.
- Public evidence of performance comes from a small number of named deployments (Hydro-Québec rime-ice, GFT-C on Henriette and Erin) rather than a broad customer roster, so buyers outside ice and cyclone risk have less to benchmark against.
as of 2026-10-09
Verification history
We have re-verified Silurian 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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Where the pricing makes sense
The company stage and team size where Silurian's pricing actually pencils out — and where peers do it cheaper.
Silurian scopes engagements around your risk surface, so the practical budget comparison is against the internal cost of building a comparable forecasting model yourself — a multi-engineer research effort — rather than against a per-call weather API subscription. For a utility or agency with concentrated icing or cyclone losses, that framing usually favours a vendor deployment. For a small operator with limited data coverage, a generic forecast feed will cost far less and may be adequate.
Setup time & first value
How long it actually takes to get something useful out of Silurian — broken out by persona, not the marketing-page minute.
Expect a co-development timeline, not a same-day signup. A radar engineer can be productive with the open-source radrs library in an afternoon since it ships an xarray-compatible Python interface. For a utility or agency, first value comes after scoping your risk surface and co-training on your feeds — plan on weeks of joint work, with client-side data preparation usually the long pole.
Switching to or from Silurian
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a generic weather API: replace the feed with a GFT co-trained on your asset telemetry, using your existing sensor data as the differentiator.
- →From an internal forecasting model build: hand the foundation-model research to Silurian and keep your team on the data pipeline and operational integration.
- →From a spreadsheet or manual icing workflow: feed grid observations into a GFT that produces day-ahead rime-ice alerts at 0.72 average precision.
- →From a third-party radar preprocessing stack: adopt radrs, the open-source NEXRAD Level 2 reader with a Rust core, for loading in Python.
- ↗To a generic forecast API: straightforward if you only consumed forecast output, but a model co-trained on your proprietary feeds cannot be carried across intact.
- ↗To an in-house build: you would need the sensing data and research staff to reproduce a Generative Forecasting Transformer from scratch.
- ↗To another tailored-model vendor: exports of your training data travel with you, but retraining and revalidation of alert thresholds is required.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Silurian”, and we withheld 6: 6 could not be judged, because “Silurian” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Silurian.
Official links
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Featured Head-to-Head Comparisons
Silurian vs Spider Cloud
If your business needs custom, high-resolution weather forecasting for infrastructure resilience, Silurian is your specialized enterprise partner. For AI agents and RAG pipelines requiring fast, cost-effective web data extraction, Spider Cloud is the clear choice with its freemium model, Rust engine, and expanding feature set. Choose based on your data domain: weather vs. web.
Silurian vs Temporal Ai
Temporal AI and Silurian serve completely different needs. Choose Temporal if you need a battle-tested durable execution platform to build reliable AI agents or orchestrate microservices with automatic retries and state recovery—especially with its recent usage-based billing and Serverless Workers. Choose Silurian only if you are a utility or government agency requiring custom AI-based weather forecasting models trained on your own data; it is a specialized enterprise tool with no self-service pricing.
Silurian vs Screenplayiq
ScreenplayIQ and Silurian serve entirely different domains: screenplay analytics for entertainment vs. weather simulation for infrastructure resilience. Your choice depends purely on your industry: if you write feature films, ScreenplayIQ offers affordable tiers with box office prediction; if you manage weather-exposed infrastructure, Silurian’s custom models provide enterprise-grade forecasts. There is no overlap.
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