The storm arrives at 4:47pm. Plan for it at noon.
Concept demonstration — modeled against a real prior production and twelve months of regional ensemble data.
Hero visual placeholder · Weather Intelligence
Challenge
Public weather forecasts answer the wrong question for production teams.
A film crew, an event commander, a giga-project site lead — none of them need to know whether it will rain in the city today. They need to know whether a specific square kilometer of ground will be unsafe between 4:47pm and 5:23pm, and what to do about it.
The gap between forecast and decision is where productions lose money, miss windows, and break things.
We wanted to close it.
System
We built Sirius Weather Intelligence.
An ensemble forecasting layer that blends three public global models with a regional microclimate model trained on the geographies our clients actually operate in. The output is not a forecast. It is a decision packet.
For every site, every shoot window, every event call sheet, the system produces a single page: the window is green, amber, or red. The drivers — wind shear at altitude, precipitation probability, dust transport, lightning proximity, ground temperature — are each visible at a glance with their confidence. The recommended action is named in plain language. Production gets a digestible answer instead of a meteorology slide.
Under the hood: ECMWF + GFS + ICON ensembles refined by a regional model trained on twelve months of Levant + Gulf observations. Microclimate corrections apply per coordinate. Confidence is propagated through every step. Every prediction cites the contributing models and the regional adjustments that moved it.
Outcome
We back-tested the system against a real prior outdoor production — twelve days of filming across two locations. Public forecasts identified the right call window in seven of twelve days. Our ensemble identified the right call window in eleven of twelve, with a single false-amber on day four (the storm arrived but tracked north of the site by six kilometers).
The model is now in conversation with two production houses and one giga-project site team about a live pilot.
By the numbers
Modeled: 11/12
correct call-window decisions vs 7/12 baseline
Target: 6h
lead time on a decision packet
Target: ±2km
microclimate spatial precision
Modeled: 89%
confidence calibration vs hold-out
12 months
regional training horizon
Concept
pilot deployment pending
Concept demonstration. Modeled values were produced by back-testing against a real prior production. Real-deployment performance will depend on the operating geography and the available local sensor mix.
Tech stack
Global ensembles
ECMWF IFS, NOAA GFS, DWD ICON, blended with per-driver weights learned on regional observations.
Regional model
Custom downscaling model trained on twelve months of Levant + Gulf observation data with terrain-aware features.
Microclimate refinement
Gaussian-process correction per coordinate; uses local sensor data when available.
Decision packaging
Plain-language outputs per call window: green / amber / red with named drivers and a recommended action.
Confidence propagation
Calibrated probability flows from the ensemble through the regional refinement to the decision layer. Every number on the screen has a confidence next to it.
Delivery
Production-side decision packets via API + cinematic dashboard. Notification mesh for the call-sheet thread.
Citation discipline
Every forecast is traceable to the ensemble members and the regional adjustments that shaped it.
Team and credits
Humans
- Concept direction[Founder]
- Modeling lead[Name]
- Regional data partner[Partner]
Agents
- Sirius BuilderTrained the regional downscaling model and built the ensemble blending layer.
- Sirius ObservatorySourced and curated the twelve-month regional observation corpus.
- Sirius ComposerDesigned the decision packet layout and the cinematic forecast dashboard.
- Sirius VoiceComposed the showcase narrative.