Guardian outage-prediction dashboard — a US map of forecasted power-outage risk over a rolling-hills landscape

WindBorne Guardian: Extreme Weather Preparedness for Utilities

timeline: Spring 2026
role: Product Designer
Frontend Engineer
Product Manager
skills: Product Design
Frontend Engineering
0 --> 1
User Research

Guardian turns weather forecasts into operational decisions for power utilities — predicting outages 0–14 days ahead, flagging at-risk infrastructure, and helping crews stage where it matters before the storm hits.

Context

WindBorne operates the largest network of long-duration weather balloons in the world. The pressure, wind, temperature, and humidity data those balloons capture feed a global atmospheric model that is particularly strong on multi-day forecasting in regions where traditional radiosondes are sparse.

That forecasting edge is most valuable when getting the weather wrong is expensive. Power utilities sit squarely in that category — every major storm that knocks out a substation can cost tens of millions in restoration, lost load, and political pressure.

Problem

When we started talking to utilities, the picture was consistent. Operations teams already had dashboards for everything — weather, asset condition, crew rosters, GIS — but the tooling was built for monitoring, not for acting.

If a derecho was forecast to sweep through a service territory in five days, the lead-up looked like a flurry of phone calls, spreadsheet handoffs, and a lot of best-guessing about where to pre-stage trucks. The forecast kept getting better every year. The workflows around it had not kept up.

Forecasts arrived as raw probability fields, not as decisions about where crews should go.
Asset condition and outage risk lived in separate systems that nobody had time to reconcile mid-storm.
Critical infrastructure — hospitals, water plants, comms — was the first thing to fall through the cracks.

Design principles

Progressive disclosure: At a glance v.s. dive deeper Translate weather signals into operational language Establish trust through UI Avoid jumping off the credibility cliff Build explainable AI

Progressive disclosure: At a glance v.s. dive deeper

While there is a large amount of relevant information to disclose to the users, information should have different weights in the interface, and we shall not dump all the insights to the user at once. The goal is to incorporate both high-level situational summary and deep-dive analysis without overwhelming them upfront.

We should establish the hierarchy between what the users should understand immediately when they open up the interface, and what they can click a few more buttons and investigate later.

The interface should enable instant clarity: “What is the risk? How severe? Where should I pay attention?” Other detailed explanations can go “under the hood”, allowing users to understand what inputs go into consideration, the reasoning behind how an insight is constructed, etc.

Translate weather signals into operational language

Weather information is more useful when it is translated into the operational questions users need to answer.

The Emergency Planning Chief does not need to necessarily know that a severe storm is coming. They need to understand what a storm means to their system: How many customers may experience outages? How many lines or poles may be down? How many crews are needed?

By turning weather signals into operational impact, we help users understand storm impact, plan resources, and plan actions with more confidence.

Establish trust through UI

The interface is the most direct way users experience with the underlying WindBorne intelligence. To build trust, we need to be transparent, reliable, and honest about the information presented.

“Data last updated at: Today 5PM” — reduces confusion about whether they are looking at latest information.
Loading state when data is still loading — prevents mistaking an unfinished state for an empty/broken state.
“Data not available” when data failed to load — clearly communicates system limitations.
“Model estimates. Check real conditions” — sets the right expectation for model outputs.

Fig. Right expectation for model outputs

Fig. Reduce confusion

Fig. Loading state

Fig. Clearly communicates system limitations

Avoid jumping off the credibility cliff

Weather forecasts are inherently uncertain. No forecast can be 100% accurate or delivered with 100% confidence. Consequently, it is “impossible” to give the user a definite answer.

Because of this, we need to be thoughtful about how we present the predictions and recommendations to the users. We need to set the correct expectations, clearly communicating what we can reliably support today without underselling or overpromising what the system can prove.

Build explainable AI

When designing AI-powered products for Disaster Preparedness, especially for industries / user groups like utilities, who are risk-averse and conservative, we have to be careful of how we incorporate AI into their workflow and how we can make it a “glassbox” AI experience.

In order to build explainable AI, make sure we can explain:

What data goes into the model.
What is the output.
How is the output generated / reasoning behind the output.
What expectation users should have.
Traceability so AI is accountable.