In this guest blog post, we asked AVTECH how access to our high-resolution weather data enhances safety and operational efficiency in the air, and why our investment in next‑generation, cloud‑based forecasting science is unlocking even greater value for airlines. AVTECH Chief Commercial Officer, Philip Nordfeldt, answered our questions below.
Why does advanced weather modelling, and the Met Office’s ‘optimal blend’, matter for aviation?
Jet fuel is the single largest variable cost for airlines, and is rarely very stable, impacted by geopolitical events, supply disruptions, and energy market shifts. For an industry already operating on razor-thin margins, that volatility is existential. At the same time, external pressures are intensifying: tightening carbon regulations, rising EU Emissions Trading System (ETS) costs, and growing passenger expectations around sustainability, are adding new financial weight to every flight operated.
In this environment, the accuracy of the weather data underpinning every flight decision has never mattered more. AVTECH's partnership with the Met Office was built on one core requirement: access to the most accurate, high-resolution weather forecasts available for aviation operations. Independent analysis across tens of thousands of flights has consistently shown that higher-resolution wind and temperature data significantly reduces forecast error compared to standard coarse-grid aviation weather products - and that accuracy directly translates into safer, more efficient flight operations.
The Met Office’s development of an ‘optimal blend’ of physics-based and machine-learning approaches to weather prediction means the already-proven accuracy of AVTECH services will only improve.
How can we turn advanced forecasts into operational value?
AVTECH's role is to take the Met Office's raw numerical weather forecasts and translate them into actionable, flight-specific guidance. Rather than presenting pilots or dispatchers with generic weather maps, we process high-resolution forecast data along each individual flight's four-dimensional trajectory, latitude, longitude, altitude and time. These tailored forecasts are then integrated into flight planning systems, pilot briefing tools, and directly into the aircraft's Flight Management Computer.
The operational value of this approach is not theoretical; it is measured, cumulative, and growing with every flight. Since AVTECH began operations, airlines using our services have collectively saved $571 million in fuel costs, burned 550 million fewer tonnes of fuel, avoided 1.8 million tonnes of CO₂ emissions, and recovered approximately 24 million flight hours through more efficient routing and timing. These are not projections or modelled estimates, they are the aggregated results of real flights, real crews, and real decisions supported by more accurate weather data than was previously available to aviation.
Why do you think the Met Office’s ‘optimal blend’ is so exciting for aviation?
The Met Office’s concept of an ‘optimal blend’ reflects a strategic shift in how forecasts are produced. Traditional physics-based numerical weather prediction remains the backbone of forecasting, using fundamental equations of atmospheric motion that are trusted, explainable, and physically consistent. Machine-learning approaches, by contrast, learn patterns from vast archives of data and can produce forecasts or enhancements far more quickly. Instead of choosing one approach, the Met Office is pursuing hybrid techniques that benefit aviation in several key ways.
Machine-learning speeds up computation and updates, giving aviation faster access to crucial forecast changes for time‑critical decisions. It improves accuracy by correcting model biases while physics ensures reliability, helping airlines reduce fuel use and improve weather‑related decision‑making. AI excels at processing diverse data like satellite, radar and aircraft observations, enabling aviation data to enhance models and, in turn, flight operations.
The blended approach opens the door to more tailored and scalable forecast products. Machine learning makes it increasingly feasible to generate aviation-specific indices, probabilistic outputs or statistically tuned products designed around real operational needs rather than generic weather parameters. For AVTECH, this creates opportunities for us to deliver even more aviation-centric decision support, differentiated by relevance and usability rather than raw data volume.
How can we tackle persistent challenges like clear-air turbulence?
One area where high-resolution modelling and blended approaches are particularly valuable is clear-air turbulence, which remains one of the most difficult hazards to predict. Turbulence often arises from fine-scale atmospheric structures such as wind shear and stability gradients that are easily missed in coarse models. Access to higher-resolution data, combined with smarter post-processing and pattern recognition, improves the ability to identify turbulence-prone regions along specific flight paths, supporting proactive mitigation rather than reactive response.
What is your vision for the future?
Over the next five to ten years, the forces reshaping aviation economics, fuel volatility, carbon regulation, margin pressure, and demand sensitivity, will only intensify. The airlines that navigate this environment most successfully will not be those that simply react to cost shocks, but those that have built structural efficiency into every flight they operate. That is the future AVTECH is building toward.
As forecast resolution increases, update cycles shorten, and AI-enhanced modelling matures, the relationship between advanced weather science and real-time flight optimisation will deepen in ways that go far beyond incremental fuel savings. Weather data will become continuously and dynamically embedded in both strategic planning and in-flight decision-making, enabling a new standard of operations that is simultaneously safer, more sustainable, and more resilient to the external pressures that today catch airlines off guard.
For AVTECH, continued collaboration with the Met Office ensures that every advance in atmospheric science is translated directly into practical tools that pilots trust, fuel efficiency teams can validate, and Chief Financial Officers can put a number on. The transition to next-generation cloud-based supercomputing and the development of optimal physics-and-machine-learning forecast blending are not abstract scientific milestones, they are the foundation of the next generation of AVTECH performance. Every improvement in forecast accuracy becomes a measurable improvement in fuel saved, cost avoided, and emissions reduced on every flight, for every airline that chooses to optimise.
