For meteorologists, energy professionals, and commodity traders, Cirrus brings multiple models, observations, and probabilistic guidance together to help turn weather uncertainty into better-informed decisions.
Weather forecasts rarely offer a single, perfectly consistent answer.
One model may bring a storm through faster than another. Ensemble members can show a relatively narrow range of possible outcomes one day and a much wider range the next. For renewable energy, those differences can translate into materially different expectations for solar or wind generation.
For meteorologists and other weather-informed decision-makers, the challenge isn’t eliminating that uncertainty. It’s understanding what the available guidance is saying, where it agrees, where it diverges, and what those differences could mean for the decision at hand.
Recent enhancements to Cirrus, Spire’s satellite-derived forecasting visualization platform, make that process easier by bringing more model guidance, observations, and probabilistic information into one customizable dashboard.
Cirrus v1.5, released in September, introduced Model Comparison, additional guidance from leading European weather models, an ensemble-based power forecast, Madden-Julian Oscillation (MJO) visualization, and new options for exporting analysis. Together, these capabilities give users more ways to investigate forecast uncertainty rather than relying on a single forecast in isolation.
Here’s how to put them to work.
Start with the question: Where do the models agree?
Compare multiple forecast models and observations at a single location to quickly identify where guidance agrees and diverges.
Comparing multiple forecast models is a familiar part of a meteorologist’s workflow. Different models use different architectures, initial conditions, data-assimilation approaches and representations of atmospheric processes. As a result, they won’t always produce the same forecast.
Those differences are useful information.
Broad agreement among independent models can provide greater context around a forecast scenario. Divergence can signal that the evolution is less settled and deserves closer examination. Neither should be interpreted as a simple guarantee of what will happen, but both can help forecasters assess the range of possible outcomes.
The Model Comparison capability introduced in Cirrus v1.5 is designed to make that analysis easier.
Users can select a point on the map or an existing forecast site and compare guidance from Spire High-Resolution Forecast, Spire’s global AI weather model, and from European forecasting centers ECMWF and the German Weather Service (DWD), including ECMWF IFS Ensemble,
ECMWF AIFS Ensemble, and DWD ICON-EU. The ECMWF ensemble products include mean and spread out to 15 days, while ICON-EU provides hourly regional guidance for Europe.
Two viewing modes support different ways of interrogating the forecast. Compare overlays the selected models for a variable, making differences readily visible. Focus emphasizes one model’s forecast while retaining the others as context.
Compare mode reveals differences across models, while Focus mode highlights one forecast with other guidance retained for context.
Users can also highlight different combinations of models and model runs and move between synchronized 48-hour, seven-day, and full forecast windows.
The result is less time moving between separate displays and more time examining the meteorological question itself.
Add observations to the comparison
Model-to-model agreement is only part of the picture.
Cirrus can display observations from a location alongside the corresponding forecasts. That gives forecasters another important reference point: how recent model guidance compares with what is actually being observed.
Consider an approaching North Atlantic storm, for example. A meteorologist could view the system across Spire and external model guidance on the map, then investigate a specific location through Model Comparison.
Do the models agree on timing but differ on wind speed? Is one model consistently warmer than observed conditions? Does disagreement increase later in the forecast period?
Rather than asking which model is simply “right”, the forecaster can investigate how and where the guidance differs and use those differences as additional context for the forecast.
That distinction becomes even more important when moving from individual models to ensembles.
From one forecast to a distribution of possible outcomes
A deterministic forecast represents one possible evolution of the atmosphere. An ensemble approaches the problem differently by producing multiple forecasts, or members, that collectively help describe a range of possible outcomes.
That distribution can reveal information a single forecast cannot.
A relatively tight grouping of ensemble members indicates that the ensemble is producing a narrower range of outcomes. A wider distribution indicates greater spread among the members. How that information should influence a decision depends on the application, forecast horizon, and consequences involved.
For energy users, Cirrus v1.5 extends that concept from weather variables directly into expected power generation.
The ECMWF ensemble power plume is Cirrus’ first ensemble-based power forecast. ECMWF IFS ensemble weather forecasts are passed through Spire’s Power Generation Forecast, producing a range of predicted generation outcomes. Within Cirrus, users can see the ensemble mean, individual members, and a shaded spread around the ensemble mean, with generation expressed directly in megawatts.
Ensemble forecasts are available for both wind and solar power generation across all seven U.S. ISO/RTO markets and multiple European countries, extending this probabilistic view across a broader range of energy markets.
In a wind example over Germany, instead of seeing only one expected generation curve, a user can see how much the ensemble members agree or disagree about potential output.
Wind power forecasts for Germany show the range of potential generation outcomes in megawatts across ensemble members and other model guidance.
That changes the question from:
“How much wind generation is forecast?” to: “What range of generation outcomes does the weather guidance support, and how is that range changing?”
For an energy trader, renewable-energy forecaster or grid operator, that range can provide valuable context for evaluating weather-related generation risk.
A shortwave solar-radiation map layer introduced with the release provides another way to investigate the meteorological conditions underlying the solar-power forecasts.
What model disagreement can and can’t tell you
It’s tempting to translate model agreement directly into “high confidence” and disagreement into “low confidence.” In practice, forecast uncertainty deserves a more nuanced interpretation.
Agreement does not guarantee that the models are correct. Models can share biases, respond similarly to the same initial conditions, or converge on a scenario that ultimately does not occur.
Likewise, disagreement is not necessarily a weakness. It can reveal genuine sensitivity in the forecast and expose plausible alternative outcomes that would be hidden by looking at one deterministic solution.
A useful analysis therefore combines several signals:
- Model-to-model differences: Do independent forecasting systems depict a similar evolution?
- Run-to-run consistency: Is the expected scenario remaining relatively stable as new guidance arrives?
- Ensemble spread: How broad is the range of outcomes within a probabilistic forecasting system?
- Observations: How is the forecast performing against current conditions?
- Forecast horizon: Is the decision focused on the next several hours, the next week, or a broader sub-seasonal pattern?
Cirrus brings these signals closer together so users can evaluate uncertainty as part of the forecast workflow rather than as a separate analytical step.
Extend the view from days to broader patterns
Not every weather-sensitive decision is driven by the next few hours or days.
For commodity markets and longer-range planning, larger-scale patterns can provide context about how weather risks may evolve over subsequent weeks.
Cirrus v1.5 added a phase-space panel for monitoring the Madden-Julian Oscillation (MJO), a large-scale pattern of tropical convection that can influence weather patterns around the world.
Users can step through the MJO’s forecast evolution over time and display individual ensemble members, providing a view into both the expected progression and the uncertainty around it.
Track the forecast evolution of the MJO alongside broader weather patterns to add context to longer-range risk.
That capability complements Cirrus’ weather-regime visualization and provides another bridge between near-term forecast analysis and the longer-range perspective supported by Spire’s AI-driven forecasting capabilities.
For a meteorologist, this might mean monitoring whether ensemble members increasingly favor a particular MJO evolution. For a commodity analyst, the value may be in understanding whether broader atmospheric signals are becoming more or less consistent with weather patterns relevant to a market or region.
The principle remains the same: uncertainty itself contains information.
Build uncertainty into the daily workflow
Export maps, charts and dashboards to share forecast insights in briefings, presentations and operational updates.
The analytical improvements in Cirrus v1.5 are accompanied by several workflow updates designed to make those insights easier to use and communicate.
Map, chart, and dashboard panels can now be exported as PNGs or animations, making it easier to incorporate forecast analysis into operational briefings, presentations, morning notes, and customer communications.
Cirrus also provides more control over how forecasts move between maps and charts. Users can open a forecast in a new window or send it to a selected panel, while chart panels can retain their own locations rather than changing with the latest map selection.
Dashboard loading has also been improved, and an updated charting tour helps users navigate the available tools.
These may be quieter additions than a new forecast model, but they address an important part of operational forecasting: insight only has value if it can move efficiently from analysis to decision-making and communication.
A more useful way to think about forecast uncertainty
The growing combination of AI models, traditional numerical weather prediction, and ensemble forecasting is giving weather professionals more guidance than ever before.
The challenge is increasingly not access to another forecast. It’s making sense of multiple forecasts at the same time.
That’s where tools for comparison and probabilistic analysis become particularly valuable. Spire adds another dimension to that analysis through its proprietary satellite observations, providing an independent source of atmospheric data and forecast signal rather than relying solely on the same inputs available to the broader forecasting community.
Instead of treating model disagreement as noise to work around, meteorologists, energy professionals, and other weather-informed decision-makers can examine it directly: comparing independent guidance, checking it against observations, assessing ensemble distributions, and following how those signals evolve over time.
Cirrus v1.5 brings more of that analysis into a single workflow, helping users move beyond What does the forecast say? toward a more useful question:
What does the full range of forecast guidance tell me about the decision I need to make?
