Guardian outage-prediction dashboard floating over a golden 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 helps power utilities get ready for storms. It predicts outages up to 14 days ahead, shows which parts of the grid are most likely to fail, and helps planners decide where to send crews before the weather hits.

Context

WindBorne flies the world's largest fleet of long-duration weather balloons. Their data feeds a global forecast model that can see a storm coming days before other forecasts do.

But the people who run a power grid don't plan in weather terms. They plan around their own system: which areas will get hit, how many customers will lose power, and how many crews to send where.

So our job was to help utilities use those extra days: show them what is at risk, early enough to get ready for it.

Problem

Every utility we talked to already had plenty of dashboards: one for weather, one for equipment health, one for crew schedules, one for the map of their network. None of them showed what a coming storm meant for their grid, so planners pieced it together over phone calls and spreadsheets.

Five days before a big storm, a planning chief would put a weather map next to a map of their power lines and guess where to send trucks early. A wrong guess put crews in the wrong place, and customers waited longer for their power to come back.

  • The forecast showed where wind and rain were likely. Someone still had to work out what that meant for their poles, lines, and crews.
  • Weather, equipment health, and past outages lived in separate tools, and no one had time to compare them with a storm on the way.
  • Hospitals, water plants, and other critical sites were the most important to protect, and the easiest to lose track of.

Design principles

Progressive disclosure: At a glance v.s. dive deeperTranslate weather signals into operational languageEstablish trust through UIAvoid jumping off the credibility cliffBuild 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.

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.