The gap between regional weather reports and local reality

Your nearest official weather station is likely miles away. It sits at an airport, or a military installation, or a government monitoring site. It measures what’s happening there — not at your distribution center, your workplace, or your front door.

For many applications, that gap can be the operational problem. Weather doesn’t just affect regions. It affects addresses. Real-time weather monitoring depends on data that reflects what’s happening at the actual location that matters – and for a long time, the data resolution hasn’t matched that reality.

Currents on Demand (COD) was built to close it.

More than one source of truth

No single data source can tell you what’s happening at an arbitrary point on the map right now. A numerical weather model covers every location on the globe but smooths over local variation. A nearby airport station knows exactly what it measured — but it’s still miles away, at a different elevation, with its own microclimate data that doesn’t translate directly to your location. Doppler radar shows precipitation as it is falling through the sky, but lacks explicit information about certain surface conditions, such as temperature.

Currents on Demand delivers real-time weather monitoring and helps power a hyperlocal forecast that could vary greatly for two locations fairly close to each other, including in areas of rapidly varying terrain.

COD draws from all of these simultaneously:

• Numerical weather model output — physics-based atmospheric simulations updated on a regular cycle, used as the first-guess foundation for every estimate
• Doppler radar — real-time reflectivity and precipitation-type data, refreshed as frequently as every 5 minutes
• Lightning network data — active thunderstorm signatures in the vicinity of a request point
• Satellite cloud imagery — cloud cover and precipitation signals at spatial scales that surface stations and radar alone cannot capture
• Quality-controlled surface observations — surface weather observations drawn from nearby official surface weather stations and personal weather station data, filtered for accuracy and recency before entering the calculation

How the blending engine weighs ground truth

The engine runs two parallel computations, then merges them. One path starts with the model as a foundation. The second layers in observational data to estimate values at the unmeasured point by weighting nearby station readings according to their distance and internal consistency with neighboring stations. Closer, more consistent stations carry more weight. Stations reporting stale or anomalous data are downweighted or excluded.

The engine then determines how much weight to give each path based on what data is available and fresh at that moment. If radar coverage is strong and recent observations exist nearby, the observational path carries more influence. If coverage is thin, the model provides the backbone. The blend shifts automatically.

Elevation differences between a request point and nearby stations are accounted for — a ridgeline and the valley below it behave very differently, and the system respects that. Land and water boundaries are treated separately, because a point on a lakeshore and an inland point a few miles away have fundamentally different weather profiles.

On-demand compute: Built for real-time accuracy

This entire computation happens on demand — at the moment of the request. The result reflects conditions as they stand right now, not as they stood an hour or hours ago. A minutes-fresh estimate is a different product from a 2-hour-old one.

When someone opens a weather app powered by COD, they receive the most current blended estimate the system can produce.

This matters operationally. A snowsquall can develop in minutes. A fog bank can lift in half an hour. Extreme weather can move faster than a scheduled data refresh. Businesses that depend on real-time weather data — deploying crews, routing deliveries, suspending outdoor work, activating equipment protections — carry direct operational risk when that data lags actual conditions.

What real-time weather monitoring means for your operations

Consistently, COD can produce a trustworthy estimate for any point on the map using the best available data from every source simultaneously. Real-time weather monitoring doesn’t require a dedicated surface weather station at every job site, delivery stop, or field location — though the system will incorporate that data if you have it.

Whether you’re managing a utility grid, running a construction fleet, operating a cold-chain logistics network, or protecting field assets across thousands of locations, hyperlocal extreme weather can disrupt operations in ways a regional forecast won’t catch — the operational question is the same: What is happening right now, right here? COD exists to answer it.

The forecast said clear skies. So why is everything grounded?

It happens thousands of times a year at airports around the world. Operations teams reviewed the forecast. Clear skies. Light winds. No significant weather in the corridor. Plans were locked. Crews were scheduled. Gates were assigned.

Then the atmosphere did what it often does: it didn’t cooperate.

Ultimately, this isn’t a story about bad weather; it’s a story about why traditional, single-outcome models are failing, and why probabilistic weather forecasting is rapidly becoming the new standard for aviation risk management.

The cost of uncertainty

Weather isn’t a fixed number but a range of possible outcomes – shaped by temperature gradients, jet stream position, moisture flux, terrain interactions, and dozens of other variables in constant motion. Yet, most companies, including many of the world’s largest airlines, still anchor multi-million-dollar decisions to a single, deterministic forecast. One number. One outcome. No probability. No range.

A deterministic forecast tells you what the atmosphere will “most likely” do. However, most likely isn’t certain – and in weather-sensitive industries, the distance between most likely and actual can be measured in dollars. Often millions of them.

The airline industry sits at the sharp end of this problem. Weather is one of the most consistent and costly drivers of flight delays and cancellations across the U.S. national airspace system and around the world. The direct costs – ground stops, cancellations, crew repositioning, passenger compensation – represent only a fraction of the total impact. Indirect costs from network disruptions, reputational damage, and downstream schedule recovery compound the impact for hours, days, or longer.

And here’s the uncomfortable truth: a significant share of those costs aren’t inevitable. They’re the predictable result of consequential decisions made with incomplete information.

That isn’t aviation weather risk management. That’s putting on blinders and hoping for the best.

The danger of an average forecast

When an operations center sees a deterministic forecast showing “36°F and light rain” at a major hub, they plan accordingly. What they can’t see – because the deterministic model doesn’t show it – is that there may be a 40% probability that temperatures will drop below freezing by 5:30 a.m. This would trigger de-icing queue requirements, slower departure rates, and a compressed departure push window, and by 7 a.m., a cascade of delays that ripples across the entire network for the rest of the day.

A single number concealed a critical, plannable risk. The cost wasn’t due just to the weather – it was caused by the data gap.

 The airline decision points where it matters most

Airlines face weather risk at almost every point in the operational chain – from pre-departure planning through en-route contingencies to arrival sequencing. Three decision types stand out as high-leverage opportunities when probabilistic data is applied:

Fuel planning: A deterministic forecast showing smooth air leads many dispatchers to plan minimum legal fuel loads. A probabilistic model that shows a 35% chance of a thunderstorm developing along the route changes that calculation entirely. Carrying additional fuel is cheap insurance. Diverting to an alternate airport because you ran short is not – in cost, time, or passenger experience.

Crew and aircraft pre-positioning: When a storm’s clearing time is uncertain, a single forecast might show clearing by noon. A probabilistic model might show a 40% chance of poor conditions lingering past 2 p.m. That distinction drives a completely different pre-positioning strategy – hedging to avoid a late-afternoon scramble for available crews and aircraft to cover downstream departures.

Go/no-go decisions at the margins: Borderline weather situations are where the benefit of quantitative uncertainty information is highest. Probabilistic weather – mapped to impacts on system capacity – gives dispatchers a structured weather risk management framework to mitigate risks rather than betting on a single weather outcome.

None of this requires a complete operational overhaul. It requires better data feeding into the decisions your team is already making – giving those decisions a quantitative foundation they currently lack.

Building the operational case for probabilistic intelligence across industries

At The Weather Company, we’ve spent decades building the data infrastructure and scientific methodology to make probabilistic weather forecasting practical for enterprise operations.

Our ensemble-based forecast products provide a full range of plausible atmospheric states – including the low-probability, high-impact events that deterministic models routinely obscure. For airlines, energy operators, logistics networks, and any business where weather is a material risk, these weather risk management solutions are no longer a competitive differentiator. They are the table stakes of responsible operations.

Businesses that continue operating on single-outcome forecasts will keep absorbing costs that, in hindsight, could have been mitigated. On the other hand, businesses that move to probabilistic weather intelligence gain something more than just a better forecast – they enable a structured, data-driven process for making weather-impacted decisions that hold up under scrutiny, regardless of what the atmosphere decides to do next.

Weather is uncertain. Your response to it shouldn’t be.

The forecast gap that’s costing you money

Picture this: a regional distribution center shuts down for a winter storm warning. The storm tracks 30 miles south. Trucks sit idle. Shelves go unstocked. You’ve just lost a day of revenue to weather that never arrived at your site.

Flip it. An airline keeps a hub open based on a calm forecast. A line of thunderstorms develops faster than the regional model predicted. Now you have ground stops, diverted flights, and crew timing out across the network.

This is the forecast gap. Traditional weather models predict conditions on wide grids — sometimes 7-10 miles (10-13 km) across. That works for telling a city whether to grab an umbrella. It doesn’t work when your business lives or dies by what happens at one warehouse, one airport, one substation, or one field.

The cost of doing nothing

For business leaders, weather impact on business is not a side issue. It’s a line item.

Think about what an imprecise forecast actually costs:

• Lost revenue from unnecessary closures or cancellations
• Safety incidents when conditions turn worse than predicted
• Wasted labor when crews show up for work that gets scrubbed
• Supply chain chaos when shipments get rerouted on bad information
• Insurance exposure when teams are caught off guard

The status quo treats every site in a region the same way. Your fulfillment center gets the same forecast as the office park 20 miles down the highway. Your wind farm gets the same forecast as the suburb next door. That mismatch is where money leaks out.

How AI is closing the gap

The Weather Company is using AI weather prediction to push forecasting into places it’s never reached before.

Hyper-local predictions, down to the football field

What is a hyper-local weather forecast? Think of a traditional forecast as a blurry photo. You can see the general shape of what’s coming, but the details are fuzzy. AI-driven super-resolution acts like a sharpening filter. It takes that blurry image and brings it into focus, down to the level of detail on a football field.

Seeing the forecast others miss. The Weather Company’s AI-powered super-resolution technology transforms standard weather model data into hyper-local wind forecasts 10x more detailed.

That means a forecast is tuned to the specific warehouse loading dock, runway, turbine, store, or field where your work actually happens. The Weather Company is working to operationalize these super-resolution products in the coming months.

Sharper forecasts for the weather that hurts most

Hyper-local, sunny-day forecasts are useful. But the extreme weather business impact is where forecasting precision pays off most: hurricanes, freeze forecasts, derechos, flooding, and wildfire conditions.

We’re working with a 1-kilometer dataset from MITRE, known as Weather 1K, which will sharpen predictions for these high-impact hazards. For a COO, that means better lead time and better precision on exactly the events that drive business continuity decisions.

Ensemble forecasting: planning for what could happen

A single forecast tells you one story. Ensemble weather forecasting tells you many.

Think of it like a financial stress test. You don’t make capital decisions based on one assumed market scenario. You model many. Weather should work the same way.

An ensemble runs the forecast many times with slightly different starting points. The result is a range of possible outcomes, each with its own probability. Instead of being told “it will be 72 degrees and breezy,” you get “there’s an 80% chance temperatures stay between 68 and 75, with a 20% chance of stronger winds by mid-afternoon.”

A single storm prediction followed by a probabilistic forecast (generated from 10-mem ensemble).

The challenge with ensembles has always been cost. Running a physics-based weather model repeatedly is computationally expensive. AI changes the math. An AI emulator can produce ensemble forecasts at a fraction of the compute cost, and we plan to launch this enhancement later this calendar year.

The differentiator: AI plus expert humans

What is the difference between AI weather forecasting and traditional weather models? Traditional models run on fixed schedules at coarse resolution. AI forecasting adapts in real time, surfacing patterns across far more data than any human could process. Google, NVIDIA, and several startups are building AI weather models. What sets The Weather Company apart is our Human-over-the-Loop (HOTL) approach, where expert human forecasters work in parallel with AI rather than after it.

A modern forecaster has to process a flood of data: model outputs, satellite imagery, radar, observations, and ensemble guidance. That volume can lead to cognitive overload, even for the best meteorologists. AI helps cut through the noise. It surfaces the signals that matter, summarizes the range of outcomes, and frees our forecasters to do what humans do best: apply judgment, context, and accountability.

The result is a partnership. AI handles scale and speed. Humans handle nuance and decision support. Neither alone is as good as the two together.

What this looks like for enterprise

The Weather Company serves more than consumers. Aviation weather forecasting is one area where the stakes are highest, and our enterprise clients span logistics, retail, and beyond. Better forecasts translate into better decisions across the operation:

• Logistics and retail: Companies like The Home Depot use weather intelligence to position inventory ahead of demand spikes and reroute around disruption.
• Aviation: Precise wind, storm, and visibility forecasts keep flight planning, ground operations, and crew scheduling on track.
• Energy: Utilities and renewable operators use wind and temperature forecasts to balance load, schedule maintenance, and protect infrastructure.
• Agriculture: Growers and food producers use freeze, frost, and rainfall guidance to protect yield and time field operations.
• Insurance: Carriers use hazard forecasts to pre-position resources and communicate with policyholders before events.

In every case, the upgrade is the same: site-specific guidance, probabilistic confidence, and human expertise on top.

The bottom line

How is AI improving weather forecast accuracy? By combining physics-based science with real-time machine learning, AI can continuously refine predictions against the freshest available data, closing the gap between what models expect and what the atmosphere actually does. Weather is one of the few business risks you cannot negotiate with. But you can negotiate with uncertainty. AI weather forecasting, combined with the physics-based science that has anchored meteorology for decades and the human forecasters who put it all in context, gives leaders sharper, more local, and more honest information about what’s coming.

The forecast gap is closing. The question is whether your operations are ready to take advantage of it.

Key takeaways

• Predictive weather intelligence is becoming a critical part of modern airport and airline ground operations.
• Real-time lightning alerting and synchronized situational awareness help ground operations teams maintain safety while reducing unnecessary ramp closures.
• Unified operational visibility across mobile crews, duty managers, and network operations centers supports faster, more confident decision-making during weather disruptions.
• AI-driven insights and network-level weather visibility help aviation leaders anticipate operational impacts before disruptions escalate.
• Maverick Ground Ops™ combines high-fidelity alerting, predictive intelligence, and aviation forecasting expertise to help airports and airlines improve operational resilience.

A summer thunderstorm is building 20 miles from the airport. Ramp crews are monitoring lightning alerts. Duty managers are juggling gate changes. Airside operations and FBO teams are tracking incoming traffic to manage capacity and ground service equipment windows. In moments like these, every minute matters.

For airport and airline ground operations leaders, weather has always been part of the job. But today’s operational environment demands more than reactive monitoring. Teams need synchronized visibility, faster situational awareness, and predictive insights that help them act before disruptions escalate.

Introducing Maverick Ground Ops™: a modernized, situational awareness solution built to help airlines and airports move from fragmented weather workflows to unified, predictive operations.

The risks of siloed airside visibility

When legacy systems lack real-time synchronization, even the slightest disconnect can create significant operational friction. That fragmentation slows response times and creates avoidable confusion during irregular operations (IROPS). The operations center may see one set of weather alerts while ramp agents receive different information on mobile devices – or no information at all.

Lightning is one of the clearest examples of this hazard. 

Ground operations managers face constant pressure to protect crews while minimizing unnecessary ramp closures. Close the ramp too late, and safety risks rise. Keep it closed too long, and delays cascade through the network, impacting turn times, gate availability, crew schedules, and passenger experience.

The challenge is rarely a lack of weather data. It’s a lack of contextual, operationally relevant intelligence delivered consistently across teams. Operating from disconnected systems means vital weather alerts can slip through the cracks. This airside fragmentation forces teams to work reactively, creating avoidable confusion during critical severe weather events and delaying the precise coordination needed to protect passengers and personnel.

One operational picture from the ops center to the ramp

Maverick Ground Ops was designed as an enterprise-grade SaaS platform with mirrored web and mobile functionality, helping everyone from duty managers to ramp leads access the same operational intelligence in real time.

That shared visibility becomes especially important during rapidly evolving weather events. A network coordinator may need a high-level risk outlook view of weather impacts across multiple hubs. Meanwhile, a station manager may need localized lightning proximity alerts and predictive insights into surface movement disruptions. Both perspectives matter. Both need to stay synchronized.

The shift from reactive monitoring to predictive operations

Ground operations leaders don’t just need to know what the weather is doing right now. True efficiency requires visibility into what weather conditions will mean for the operation 15, 30, or 60 minutes ahead.

Maverick Ground Ops was designed around that operational reality, converting raw predictive forecasting into clear operational intelligence. For ground operations teams, that translates into more confidence around cross-team decisions like:

• When lightning threats are approaching the airfield.
• When the trailing strike window has elapsed to safely declare an all-clear and reopen the ramp.
• Whether incoming convection could reduce arrival and departure rates.
• How FBOs can safely optimize fueling and staging timelines during volatile weather.
• Which hubs and stations across the network require immediate operational prioritization.

Inside the platform: Tools built for synchronized decision-making

To turn raw weather data into unified, actionable team intelligence, Maverick Ground Ops embeds key capabilities directly into the daily workflow:

• High-fidelity lightning alerts: Real-time proximity warnings delivered via visual banners, audio signals, and push notifications synched across web and mobile devices.
• Configurable safety range rings: Dynamic, color-shifting visual rings (Caution and Warning) configured globally using ICAO codes to eliminate localized setup errors.
• Global Surface Movement (GSM) mapping: Clear, interactive visual tracking of airside assets paired with critical surface-level context with the Smart NOTAMs overlay. .
• AI-driven predictive modeling: Built-in Terminal Airspace Convection Risk (TrACR) and Airport Arrival Rate (AAR) insights to project surface and capacity impacts up to an hour ahead.
• Risk outlook dashboard: A color-coded, network-wide weather risk summary, so you know where to position aircraft and resources across hubs and stations for faster recovery.

Interactive mapping visualizes cloud-to-cloud and cloud-to-ground temporal strikes.

Precision matters when every minute impacts throughput

Operational resilience depends on timing. A ramp closure that extends even 10 or 15 minutes longer than necessary can ripple across an entire hub operation. Aircraft utilization, baggage movement, gate sequencing, and staffing efficiency all begin to compress. That’s why forecast accuracy and update frequency matter so much in aviation environments.

Human expertise and rapid-refresh data layers

Our forecasting approach combines proprietary modeling, AI-enhanced forecasting systems, and human expertise to deliver continuous weather intelligence. Designed for the pace of airside operations – where conditions can shift in minutes – Maverick Ground Ops is supported by high-resolution GRAF technology, Forecasts on Demand (FOD), and rapid-refresh updates across precipitation, wind, and other critical parameters. For lightning specifically, alerts can reach operators within 12–30 seconds of a strike, giving ramp teams the reaction time they need to make safe, confident decisions.

With Forecasts on Demand (FOD), users can quickly anticipate operational impacts.

Weather intelligence is becoming a competitive operational advantage

As aviation operations become more interconnected, weather intelligence is no longer just a safety function. It’s becoming a core operational performance driver. Backed by nearly 30 years of specialized aviation forecasting expertise, Maverick Ground Ops is built to deliver exactly that. By combining high-fidelity alerting, AI-driven insights, and mirrored web-and-mobile SaaS capabilities, the tool helps airlines, airports, and FBO footprints move faster, coordinate better, and protect their personnel with total confidence.

Airlines, airports, and FBO footprints that can better anticipate weather impacts may gain a distinct advantage in:

• Safeguarding personnel: Empowering safety officers and ground managers with real-time, context-specific thresholds to protect crews without manual guesswork.
• Maximizing throughput: Keeping aircraft, baggage, and fueling operations moving efficiently by confidently tightening operational windows.
• Reducing unnecessary ramp closures: Utilizing high-fidelity alerting to minimize cascading network delays and eliminate the cost of over-alerting.
• Optimizing turn times: Synchronizing web-to-mobile workflows so ramp leads and station managers stay completely aligned.
• Unified network resilience: Giving global network coordinators and executive leadership a single risk outlook view to prioritize resources across an entire footprint in seconds.

The ability to move from reactive response to predictive coordination may increasingly separate resilient operations from disrupted ones. That’s why platforms like Maverick Ground Ops are focused not only on visibility but operational foresight. Because if you can see operational impacts developing before they escalate, your teams have more opportunities to act decisively.

The future of ground operations is connected and predictive

The mission hasn’t changed — maximizing airside throughput with a proactive safety posture remains paramount. The difference is that now, modern aviation operations teams have the synchronized airside awareness and predictive network visibility to outpace disruptive weather before it impacts the network.

View the Maverick Ground Ops virtual launch recording to see how predictive weather intelligence is helping aviation operations move faster, coordinate better, and maintain safety across the network.

A forecast high of 102°F versus 105°F may seem trivial to an average consumer checking the weather. For an enterprise operator managing real-time demand across a power grid, that tiny three-degree variance changes everything. It dictates load forecasting, alters grid-balancing operations, and significantly elevates localized outage risks.

Because ambient environmental conditions impact everything from renewable energy thresholds to crew mobilization and physical restoration timelines, modern utilities can no longer afford to simply consume basic weather data — they must actively operationalize weather intelligence. By treating precision forecasting as a core asset, utilities can effectively bridge the gap between grid resilience and community expectations.

The rising cost of extreme weather on grid reliability

NOAA data reveals that billion-dollar extreme weather events are occurring roughly three times more frequently than in the 1980s, placing unprecedented pressure on grid reliability forecasting and distribution outage response planning.¹

That pressure is increasingly visible across daily operations. Weather-related events caused 80% of major U.S. power outages reported between 2000 and 2023, with outages occurring twice as often over the last decade compared to the early 2000s.² Additionally, the average annual number of weather-related power outages has increased by nearly 80% since 2011.³

Key drivers of weather-related disruptions

• Severe weather (58%): High winds, heavy rain, and severe thunderstorms cause the majority of disruptions.⁴

• Winter weather (23%): Snow, ice, and freezing rain cause prolonged physical grid strain.⁵

• Tropical cyclones (14%): Hurricanes drive some of the longest-lasting regional outages nationwide.⁶

To mitigate these risks, The Weather Company delivers actionable insights through an integrated ecosystem of Weather Data APIs, Forecasts on Demand™ (FOD) technology, and a centralized briefing desk model. Together, these capabilities can help utilities move past static, reactive forecasts to protect power grid reliability proactively.

How weather intelligence supports grid reliability forecasting

Grid reliability forecasting requires more than a single deterministic forecast value. Operators need to visualize the full spectrum of possible outcomes associated with an approaching weather front.

This is why probabilistic forecasting has become essential. Rather than relying on a single baseline metric, utilities evaluate multiple simulated scenarios to calculate statistical likelihoods.

Inside the briefing desk: Turning forecasts into operational decisions

At the core of these workflows is The Weather Company’s briefing desk — a centralized forecasting team providing enterprise-grade support during high-impact weather events like hurricanes, freezing rain, and extreme heat.

Case study: Mitigating risk on independent grids

For one major utility serving millions of customers across Texas, this briefing desk support has anchored storm operations for nearly a decade. The stakes are uniquely high here: Texas experienced the highest number of reported weather-related outages between 2000 and 2023, followed by Michigan and California.⁷

Because Texas operates an independent electric grid, local utilities can’t easily draw power from neighboring interconnections during peak demand or major outages — making forecast accuracy paramount. When severe weather threatens, the decision to mobilize out-of-state mutual aid crews can cost millions of dollars. Those commitments must be made two to five days before conditions deteriorate.

The power of Human-Over-The-Loop (HOTL) oversight

Within our briefing desk model, meteorologists review data before it reaches the client, adding a vital layer of human interpretation. This weather forecasting solutions grid-balancing operations approach combines AI-enhanced forecast modeling with experienced meteorologists who “nudge” outputs based on hyper-local conditions or historical model biases.

For example, adjusting a wind speed forecast by just 3–5 mph based on local terrain variables can shift a utility’s outlook from standard operations to an elevated mobilization posture. For the Texas utility, the briefing desk delivers daily forecast discussions, color-coded risk matrices, 48-hour planning tables, and 5-day wind and thunderstorm probability graphs to enable complete operational readiness.

Balancing the grid with variable renewable energy forecasting

Unlike traditional baseload generation, renewable energy is highly sensitive to rapid weather shifts. For solar operations, utilities must track cloud cover percentages, irradiance, and short-term cloud advection. For wind assets, accurate forecasts are required at both the surface level and turbine hub height, where wind shear creates additional complexity.

Advanced weather forecasting solutions supporting grid-balancing operations help utilities anticipate fluctuations in renewable generation across diverse geographic footprints. If a cloud bank impacts one solar array, operators can see precisely when a nearby asset will pick up the load.

As renewable generation and Battery Energy Storage Systems (BESS) integrate deeply into the grid, precision forecasting dictates market participation. Recent studies show that accurate solar forecasting combined with optimized battery storage allows hybrid systems to supply roughly 60% of commercial load demand, drastically lowering dependence on traditional grid power.⁸

Improving distribution outage response and financial resilience

The financial stakes of grid disruption are immense. The U.S. Department of Energy estimates that power outages cost the U.S. economy $150 billion annually, with weather causing the lion’s share.⁹ Hyper-local weather intelligence gives dispatchers early visibility into where and when conditions will break, enabling faster staging decisions and reducing unplanned downtime.

Precision data also optimizes distribution outage response by helping utilities:

• Mobilize and position mutual aid resources cost-effectively.
• Prioritize critical restoration workflows.
• Manage customer expectations (utilities providing timely outage alerts score 52 points higher in J.D. Power customer satisfaction indexes).¹⁰

During elevated-risk events, the briefing desk increases its cadence, issuing real-time updates on utility-specific hazards like ice accumulation, lightning frequency, and wind gust thresholds.

Emerging weather challenges across the energy sector

As the energy landscape decentralizes, weather forecasting demands are expanding beyond traditional utility boundaries:

• EV charging infrastructure: Cold snaps reduce EV battery range and spike charging demand, while extreme heat shifts charging patterns across metro areas. Granular forecasts help utilities anticipate these localized grid strains.
• Battery storage optimization: BESS operators rely on hyper-local forecasts to execute charge/discharge cycles and maximize energy arbitrage opportunities.
• Green hydrogen production: Electrolysis is most economical when powered by surplus renewable energy. Precise wind and solar forecasts directly influence production efficiency.
• Data center micro-climates: Cooling systems consume enormous amounts of data center energy. Tech companies require localized climate intelligence to model cooling load requirements and manage energy costs.

Operational weather intelligence for a resilient grid

The modern utility challenge is no longer about accessing weather data — it is about translating that data into operational actions. This shift toward grid resilience is mirrored in federal priorities, such as the Department of Energy’s $3.5 billion grid modernization funding initiative.¹¹

By combining advanced Weather Data APIs, FOD technology, and HOTL meteorological expertise, The Weather Company empowers utilities to master grid-balancing operations, improve variable renewable energy forecasting, and execute flawless distribution outage response.

Why the best forecast can still fall short

Here’s a thought experiment. A meteorologist delivers a perfect 10-day forecast: every temperature, every chance of rain, every gust of wind, called exactly right. Then no one looks at it.

Was it a good forecast?

Dr. Allan Murphy, one of the many influential researchers in the history of meteorology, argued that forecasts possess no intrinsic value. They acquire value only through their ability to influence decisions.² Strip away the decision and you strip away the value entirely.

That idea has stuck with us at The Weather Company. It’s both a challenge and a north star.

For decades, the industry has largely focused on one question: How accurate is the forecast? That’s a necessary question, but on its own it’s not enough. The better question is: How much does the forecast actually change what someone does?

That shift in thinking is at the heart of everything we’re building.

The gap between accurate and useful

Think about the last time you checked a weather app before heading out. You probably noticed the temperature, a precipitation percentage, and maybe an icon of some clouds. Now ask yourself: did that answer what you actually needed to know?

If you were planning a morning run, you needed to know whether heading out at 7 a.m. or 9 a.m. gave a better window, not just that rain was “likely” at some point today. If you were deciding whether to water your garden, you needed to know if meaningful rain was coming in the next 24 hours or how much it rained recently, not the general weekly outlook. If you were commuting during a snowstorm, you needed to know when accumulation would begin and how quickly road conditions would change, not only how much snowfall would accumulate.

This is the gap. On one side sits weather forecast accuracy. On the other side sits forecast relevance: the translation of atmospheric data into personalized weather forecast guidance tied to your specific decision, at your specific location, at your specific moment.

That’s the difference between a weather company and The Weather Company – the one that doesn’t just tell you what the atmosphere is doing, but what you should do about it.

What it means to build weather for where you are

Here’s where it gets interesting.

Knowing that rain is coming to a hiking trail is useful. Knowing that rain will begin at 6:42 a.m. at the trailhead where you planned to start your hike and will end by 9:15 a.m. is actionable. Those are two very different things.

Consider a logistics coordinator trying to keep a supply chain moving. A generic regional forecast telling them it might rain doesn’t help them plan. But knowing that a line of severe thunderstorms will hit their primary Midwest distribution hub at exactly 3:00 p.m. allows them to proactively reroute delivery fleets, adjust worker shifts, and protect high-value cargo before the first drop falls.

This is the core of what we mean by weather for where you are, in the context you care about. The Weather Company, through weather.com, The Weather Channel app, and Storm Radar, is building experiences that connect highly precise, hyperlocal forecasts to the activities and choices that define a person’s day.

From raw data to real-life decisions

Explore activity-specific local forecasts within The Weather Channel app.

Here’s how contextually intelligent weather data changes the game in daily life:

Running or cycling: Seeing there’s a 70% chance of rain today tells you very little about whether it’s a safe time for an outdoor workout. A contextually intelligent forecast tells you that the window between 6 and 8 a.m. is dry, breezy, and 58°F, ideal conditions, before afternoon storms roll through. That’s the difference between a great morning workout and a miserable one.

Golfing: Every golfer knows what it means when the horn sounds on the 10th hole. Round over. Cart back. Day ruined. The best weather app for golfers tells you a storm is likely to arrive at 2:15 p.m., so you move your tee time to 8 a.m., finish all 18 holes, and are in the clubhouse with a cold drink before the first clap of thunder.

Gardening: The question isn’t “will it rain this week?” It’s “will it rain enough tomorrow that I don’t need to water today?” A weather forecast for gardeners, tied to soil moisture context and expected precipitation amounts, not just percentages, answers the question you’re actually asking.

Skiing and snowboarding: The difference between a powder day and an icy grind is a few degrees and a few hours. A hyperlocal weather forecast for skiing tells you when temperatures will drop, where new snow will fall, and how wind affects conditions at elevation – turning a forecast into a trip-planning tool.

Scaling context to the enterprise

This need for context doesn’t stop with recreation; it scales directly into enterprise operations. Take a tractor-trailer driver facing a winter storm: The single most valuable piece of information isn’t total accumulation, it’s when accumulation begins. Leaving 45 minutes earlier isn’t a disruption. Getting stuck on an icy highway is.

In each case — whether you’re saving a round of golf or protecting a multi-million dollar supply chain — the raw, highly-accurate data is the same. What changes is the context, the layer of intelligence that makes the data speak to your specific decision.

The road ahead

Dr. Murphy’s framework identified three dimensions of what makes a forecast “good”:

1. Consistency: Does it reflect the forecaster’s best judgment?
2. Quality: Does it correspond to what actually happened?
3. Value: Does it produce incremental benefit for the decision-maker?

Most of the industry has spent 30 years focused on the first two. We think the next era belongs to the third.

That means continuing to invest in AI and machine learning that improve hyperlocal weather precision and everyday forecast accuracy alike. It means building personalized weather forecast experiences that go beyond the generic daily summary. It means developing weather intelligence that meets businesses and consumers where they are – mid-run, mid-flight, mid-commute – with exactly the information they need to make a better call.

Whether you’re leading a business or training for a marathon, weather shapes outcomes. The goal has never been to give you more weather data. It’s been to help you make better decisions. That’s what separates a weather company from The Weather Company, and it’s what we’re building towards every day.

If you think cleaning is just a “Spring” thing, you’re considering only one small part of the real calendar. For savvy CPG cleaning brands, cleaning isn’t only a season – it’s a predictable consumer mindset triggered by the world outside. A sudden thunderstorm or a high-pollen Thursday are what can truly deliver the “why” behind a cleaning product purchase. (In fact, the connection between environment and intent serves as a fundamental blueprint for almost any CPG brand looking to drive growth.)

Parents are the kings and queens of clean

So, who’s most often looking for a streak-free view of the week ahead? The parents on our platforms.

A recent survey of The Weather Channel digital users underscores the massive weather influence on cleaning habits and who is leaning into these environmental cues. It turns out, parents are the undisputed cleaning champions. Compared to non-parents, they clean more frequently, both inside and outside the home – and two out of three parents are directly motivated to clean by the weather.¹

High-intent cleaning habits by the numbers

The data reveals a clear story of consistent, high-intent cleaning behavior:

Between the extra mess, the seasonal germs, and the constant hum of family activity, parents represent a highly engaged audience with a consistent, year-round demand for cleaning solutions.

The cadence of cleaning

While spring remains the undisputed peak for domestic renewal, parent cleaning habits follow a multi-seasonal rhythm. Brands specializing in indoor care should treat spring as their primary launchpad, but a closer look at the full year reveals a powerful secondary window in the autumn transitions.

Seasonal percentage of parents conducting major indoor cleaning:

Rather than limiting budgets to a single annual push, home care brands achieve maximum efficiency through a sustained, evergreen approach – capturing the massive surge of spring renewal, capitalizing on the fall reset, and leveraging real-time weather triggers during the quieter summer and winter maintenance blocks.

How weather influences cleaning behavior

It turns out a messy room isn’t the only thing driving people to grab a mop. Changes in the weather act as a massive psychological trigger for household chores, with two-thirds of parents admitting that the weather directly motivates their cleaning routines.⁸

Rain and oppressive heat are the primary catalysts for these sudden bursts of domestic productivity, forcing families indoors and fundamentally shifting their daily priorities.

Cleaning for health and pollen

When allergens spike or cold and flu season hits, cleaning habits ramp up, too. These moments underscore the direct link between weather impact, household priorities, and increased CPG spending.

Weather: The force multiplier for household intent

When you align your message with the forecast, you aren’t just reaching someone who buys detergent; you’re reaching them at the exact moment their environment has made cleaning a top priority.

We solve for predictable incremental performance by identifying the exact moments when consumers shift from “passive” to “planner.” Weather influence acts as a force multiplier on consumer activities because it shapes the subconscious decision-making process. Our neuroscience research proves that when an ad aligns with the current weather-driven mindset, ROI can increase by nearly 20%.¹¹

Proof of performance for CPG & cleaning brands

• Sales lift: 3.4x the CPG benchmark for major household brands¹²
• High-value actions: 44% higher DTC conversion rate via weather-driven signals¹³
• Attention score: 24% higher Attention Unit score via integrated creatives compared to industry benchmarks¹⁴

Seamless activation for cleaning brands and beyond

With data defined by world-class forecasting accuracy, we provide an AI-driven ecosystem that automates the connection between the sky and the shelf. For brand marketers and agency trading desks, we’ve removed the operational friction. You can easily activate high-intent audiences through:

• Weather Targeting intelligence: Leverage Weather Targeting to sync your brand with the moments that matter most. We combine real-time and historical forecast data with AI and trusted behavioral signals to reveal moments of true consumer intent. Access this intelligence through a single PMP Deal ID or Curated Performance Deal to turn environmental shifts into predictable incremental performance.
• The Weather Channel digital ecosystem: Own the high-traffic “planning hours” by taking over the brand-safe, Integrated Marquee of The Weather Channel app or sponsoring key planning moments aligned with your brand.

Making your media mix shine

Weather shapes more than just the daily forecast; it defines how weather and climate influence the way people live. It’s the difference between a product sitting on the shelf and one that’s going into the shopping cart. For CPG brands, the connection between weather and human activities is measurable, actionable, and – most importantly – predictable.

Don’t let your strategy gather dust. Use the weather to buy an outcome.

Whether you’re a weekend warrior or daily fitness fanatic, understanding how weather impacts your specific outdoor activities will make the difference between a perfect training session or a soggy disappointment. That’s why we’ve created activity forecasts: specialized weather insights designed for popular outdoor pursuits.

Custom forecasts for every adventure

Explore activity-specific local forecasts within The Weather Channel app.

Activity forecasts go beyond basic temperature and precipitation to provide conditions summaries, optimal timing windows, and activity-specific details that matter most to your performance.

How to access activity forecasts

Getting your personalized activity weather is simple:

1. Open The Weather Channel app
2. Scroll down the “Today” homepage
3. Tap on any listed activity to access detailed forecast information
4. Toggle between activities using the top navigation to compare conditions

For a more personalized experience, use the “Add an activity” feature to get the latest weather impacts for all of your outdoor activities.

You can also turn on alerts to get notified about severe weather or major changes that may impact your plans.

Available activity forecasts

Take mountain biking and cycling to the next level with The Weather Channel activity forecasts, exclusively in the app.

Running

Get the complete picture for your training runs with best and worst times over the next 24 hours. We highlight “feels like” temperatures for proper hydration planning, and detailed conditions, including precipitation, humidity levels, air quality, and UV index.

Cycling

Specialized for cyclists, including rain accumulation data that is crucial for understanding road conditions and puddle depth. Factor in wind speed, air quality, humidity and sunset times to help you choose the safest, most comfortable time to train.

Hiking

Discover hiking destinations and seasonal guides for national parks while getting the weather intelligence you need for safe adventures. You’ll get updated precipitation, thunderstorm potential, UV index, visibility, wind speed, humidity, and more.

Camping

Enhanced with stargazing forecasts and precise sunrise/sunset times to maximize those perfect outdoor moments.

Tennis & Pickleball

Get humidity readings with descriptive terms like “muggy” so you know exactly what to expect on the court, as well as precipitation, “feels like” temperatures, wind speed, UV index, and air quality.

Golf

The most comprehensive forecast includes 15-day ratings from “Poor” to “Perfect” golfing conditions, cloud cover analysis (important for shadows and visibility), and integration with Supreme Golf to find local courses with pricing and booking options.

Premium features that enhance your activities

• 15-minute forecast: Perfect for spontaneous pickup games, last-minute trail runs and rapid weather changes that affect outdoor plans
• Advanced map layers: Get detailed precipitation tracking, wind patterns and storm movement data
• 72-hour future radar: See exactly when weather will impact your location for precise activity timing
• 192-hour extended forecasts: Plan your entire week with confidence
• Morning Brief newsletter: The best news and weather content delivered to your inbox daily

Whether you’re training for competition, maintaining fitness routines or simply enjoying the great outdoors, our activity forecasts provide the specialized weather intelligence you need to make informed decisions and maximize every adventure.

Activity forecasts are available in The Weather Channel app. Premium features require subscription after the free trial period. Must have a registered weather.com account.

Weather affects nearly every decision people make – from planning a weekend trip to managing a complex operation. While most people have access to basic weather forecasts, there is a vast difference between a general forecast and the high-precision data required to support accurate, timely decision-making. At The Weather Company, we are constantly improving the technology behind our forecasts so that everyone – from individual users to large enterprises – can rely on the most accurate weather intelligence available.

We provide more than just a weather report. We provide a sophisticated technological ecosystem designed to reduce uncertainty. Through our GRAF system, our adoption of the JEDI framework, and our cloud-based resilience on AWS, we offer a level of accuracy and reliability that helps anyone turn weather challenges into confident decisions. Together, these innovations strengthen modern AI weather forecasting capabilities and smarter decision-making.

High-resolution forecasting with GRAF

At the heart of our capability is GRAF, which stands for the Global High-Resolution Atmospheric Forecasting system. To understand the value of GRAF, it helps to look at how weather models work. Most models divide the world into a grid of squares, and a computer calculates the weather for each square. If the squares are too large, the model misses small but important details like a thunderstorm hitting a specific neighborhood, a wind gust affecting a local power grid, or rain arriving earlier than expected on a travel day.

Most models use a square grid of fixed size. GRAF uses a variable resolution grid and provides higher resolution over populated areas.

While many global models use large grid squares (often 10 to 15 kilometers wide), GRAF provides much higher detail where it matters most. It features 4-kilometer refinement regions over the Continental United States and Europe. This means the model provides a much sharper look at weather patterns in these key economic zones, while maintaining a coarser, efficient resolution across the rest of the globe. The result is more precise numerical weather prediction in regions where decisions – personal and professional alike – often carry the greatest impact.

Depiction of 4-km refinement regions over Europe and Continental United States.

The JEDI framework and AI integration

A weather model is only as good as the information you feed into it. In the world of meteorology, the process of feeding real-world data into a computer model is called Data Assimilation (DA).

Understanding data assimilation

Think of data assimilation as a “reality check” for the computer. Every hour, millions of data points arrive from satellites, weather stations, airplanes, and sensors. However, this data is often messy or arrives at different times. Data assimilation takes all these scattered pieces of information and blends them into a single, accurate picture of what the atmosphere looks like right now.

The JEDI framework

To make our data assimilation as powerful as possible, we have adopted a framework called JEDI (Joint Effort for Data assimilation Integration). JEDI acts as a universal adapter for weather data. It allows us to incorporate new types of observational data much faster than traditional systems to improve overall weather forecast reliability and accuracy. These data include but are not limited to: satellite radiances, aircraft observations, radiosondes, and pressure sensor readings from smartphone users (with consent).

Our transition to this technology has moved through several key phases:

• Initial phase: We replaced our older data systems with the JEDI framework, which immediately improved our ability to process complex information.
• Next phase: We began “fully cycling” the system. JEDI now works in a continuous loop, constantly updating the model with fresh data throughout the day.
• Following phase: We implemented new surface assimilation algorithms. This update allows the system to better represent ground-level conditions that affect everyday forecasts.

GRAF AI applications

One of the most significant benefits of the JEDI framework is that it drives GRAF AI applications. These applications have been trained on years of historical weather data to recognize patterns and make fast predictions. Because JEDI provides such a clean and accurate starting point, these AI tools produce even more reliable insights and further advance AI weather forecasting capabilities.

Future advancements in data assimilation

We are constantly working to stay ahead of the curve. A major part of this work involves the development of Ensemble-Based Systems in collaboration with the National Center for Atmospheric Research (NCAR). Instead of running just one forecast, an ensemble system runs many versions at once. This approach is similar to a global ensemble forecast system, helping anyone plan more effectively by providing a probability of an event occurring.

Reliability when it matters most

Accurate weather data is only valuable if it is available when you need it most. Severe weather events are precisely when forecast access matters – and precisely when systems are under the greatest strain. To address this, we have ported our GRAF framework to Amazon Web Services (AWS) architecture, creating a robust disaster recovery solution that keeps our data flowing regardless of conditions.

While most weather models run on specialized physical supercomputers, these systems can be vulnerable to outages. By running on AWS, we have built a highly resilient, always-on architecture that supports our weather recovery solutions and keeps data accessible without interruption – whether you are an individual checking conditions ahead of a trip or an operation depending on continuous forecast feeds during a high-impact event.

A cloud-native future

We are migrating our entire GRAF production environment to AWS. This move offers two major benefits:

• Reliability through redundancy: Built-in redundancy strengthens weather forecast reliability by allowing another part of the system to take over instantly if an issue occurs.
• Scalability: We can increase computing power instantly during massive events, ensuring your GRAF data is delivered on time.

Translating weather intelligence into confident decisions

Whether you are optimizing a global supply chain or simply planning a weekend family outing, the quality of your weather data directly impacts the quality of your choices. An unexpected storm or an unpredicted shift in wind can disrupt a major logistics network just as easily as it can ruin a long-planned trip.

By combining high-resolution numerical weather prediction through the GRAF weather model, cutting-edge AI weather forecasting, and robust cloud architecture, The Weather Company provides everyone with access to a higher standard of weather forecasting accuracy. As we continue to advance our data assimilation capabilities and build toward a cloud-native future, our goal remains clear: to maximize weather forecast reliability so you can navigate changing conditions with total confidence.

The problem: Raw data is a scattered puzzle

Think of raw weather radar like listening to a hundred different people speaking different languages at the same time. It is loud, it is confusing, and it is full of mixed messages. Weather radar data is obtained in varying ways across different areas. The quality, type, and format of the information you receive depend entirely on where you look. Creating a reliable weather experience starts with effective radar data processing.

First, radar data varies heavily depending on the specific radar site location. A radar stationed on a flat plain operates differently than a radar positioned near a coastal mountain range. Second, we have to account for the frequency of radar scan strategies. Some radar stations complete a full scan of the sky every two minutes. Other stations might take ten minutes to complete a single rotation. If you try to view these different scans at the same time, you get a distorted picture of reality. Achieving consistent global weather radar coverage requires synchronizing these differences into a unified view.

The challenge of false returns and ground clutter

Beyond the timing and location, we face the physical limitations of the radar itself. Single-site radar data frequently struggles with ground clutter and anomalous propagation radar effects. These are technical terms for false returns. Radar beams do not just bounce off raindrops. They bounce off tall office buildings. They bounce off mountain peaks. They even bounce off large flocks of birds and swarms of insects.

These biological and physical false returns create massive amounts of visual noise. To a raw radar system, a flock of migrating birds can look exactly like a heavy thunderstorm. We quality control these false returns so that an end user is only shown radar data that is actual precipitation.

Finally, we face the challenge of overlapping coverage. Storms do not care about state lines or radar boundaries. Often, a single storm system is tracked by multiple radar sites at the same time. Meteorological decisions need to occur when radar sites overlap in their coverage so that one final answer of precipitation is available to users. If two radars tell two different stories about the same storm, decision-makers are left guessing.

The agitation: Bad data drives costly mistakes

Let us look closely at what happens if we skip the cleanup process. If we did not perform the needed preparations to collect and combine the single sites into one mosaic, it could leave you with wildly inconsistent information between radar locations.

Bad data leads to bad decisions – for anyone relying on a forecast. Imagine a logistics manager rerouting an entire fleet because a massive storm cell appeared over a distribution hub, or a family canceling a long-planned outdoor event. In both cases, the sky was perfectly clear. The “storm” was anomalous propagation – a radar beam bouncing off a building. The cost is real either way: wasted resources, frustrated people, and eroded trust in the tools they depend on. When a real storm finally approaches, that hesitation becomes risky.

You cannot make confident decisions – personal or professional – when you’re constantly reacting to ghosts on a screen. False alarms cost money. Unnecessary delays frustrate everyone affected. Over time, reacting to bad data erodes trust in the forecasts people rely on. When a real storm finally does approach, your team might ignore the warning because they have been burned by false returns in the past. This hesitation puts people and their plans at genuine risk.

The solution: A unified and clean global view

This is where my team steps in, and it is the part of my job that I love the most. The Weather Company eliminates the guesswork. We do the heavy lifting so you can focus on what matters – whether that’s running a business or just making plans.

Our process starts with massive scale. The Weather Company collects data from around the globe, including more than 300 single-site radar locations. We gather data from across continents to build the foundation of our radar system. In addition to these single-site locations, we actively collect regional mosaics in areas that already have a combination of their regional radars. We pull all of these disparate data feeds into one centralized hub, creating a foundation for accurate real-time precipitation data and operational weather intelligence.

Collection is only the first step. The Weather Company performs Quality Control algorithms on these datasets to identify and remove false radar returns. We scrub the data clean. Our algorithms are specifically trained to recognize the distinct signatures of non-weather objects. We remove the buildings. We take out the mountains. We strip away the ground clutter and eliminate the false biological returns which are picked up on radar scans

To give you a clear perspective on how critical this process is, look at the two images below.

Raw, unedited radar data. Notice the scattered, chaotic returns near the center of the image. Quality Control is being done to identify these false biological and physical returns before they reach the user.

The same region after our Quality Control algorithms have cleaned the dataset. The false returns and ground clutter have been successfully removed, leaving only verified precipitation.

Synchronizing datasets into a seamless mosaic

Once the data is scrubbed clean of false returns, we solve the problem of overlapping radar sites. The Weather Company uses an advanced process to synchronize these individual datasets. We take the different scan frequencies and the varying location data, and we weave them together seamlessly. We produce a Global Radar Mosaic product representing an accurate picture of current precipitation, and delivering dependable real-time rainfall data worldwide.

The human element: 24/7 expert meteorological review

Even the most advanced technology requires a human touch. Think of our algorithms as the heavy machinery clearing the main road, and our meteorologists as the specialized crew polishing the final surface. After all of the automated quality control algorithms are run, our team of meteorologists at The Weather Company constantly looks over the radar mosaic. They monitor the feeds 24/7/365 to manually identify and remove any false radar returns that slipped through the automation. This final layer of expert review is completely seamless and timely, delivering a pristine, reliable product without sacrificing the critical reaction speed every forecast demands.

Whether you need a weather data API integrated into enterprise software or simply a forecast you can trust on your phone, this clean data works for you.

This Global Radar Mosaic powers smarter decisions for businesses and individuals alike. When you look at our mosaic, you are not looking at raw noise. You are not looking at mountains or birds. You are looking at a rigorously tested, quality-controlled, and synchronized view of the atmosphere.

You get one final answer. You get the truth.

Filling the gaps: A truly global picture

We recognize that physical radar infrastructure is limited or nonexistent in certain parts of the world, specifically over open oceans and in developing nations. To combat this and maintain continuous global coverage, we continuously work on projects that utilize alternative sources of precipitation observation. We actively fill these geographical gaps using satellite imagery, global lightning networks, and data-assimilated numerical weather prediction models. This multi-layered approach guarantees you have reliable weather intelligence even in areas outside of traditional radar range.

A highlight of our Global Radar Mosaic. By synchronizing hundreds of cleaned data feeds and applying strict human review, we provide one accurate, worldwide view of active weather.

From real-time observation to short-term forecasting

Securing an accurate picture of current precipitation is only the beginning. Once we have this clean, verified representation of what is happening right now globally, it becomes the foundation for the future state. We use this pristine data to enable advanced decision-making products, such as highly accurate short-term weather forecasting, automated precipitation alerting, and weather intelligence derived from advanced numerical forecast models. This shifts anyone – from enterprise operators to everyday users – from simply knowing where a storm is, to understanding how the latest numerical weather forecast may affect their plans in the hours ahead.

Weather affects everyone, every day. Let us help you turn atmospheric uncertainty into confident decisions.