What’s New: November 2024

New Machine Learning Technology for Optimal Solar Power Output Forecasts

Clients managing solar power plants can now benefit from a machine learning approach that calibrates weather forecast data with historical data from their sites to deliver optimal, site-tailored power output forecasts.

This new system can infer panel tracking types, orientation, and tilt — even for sites where these parameters are not well known. It significantly improves performance, particularly for heterogeneous sites with panels with differing orientations, tilts, and tracking types.

New Parameters to Predict Thunderstorms and Severe Turbulence

Meteomatics offers several basic parameters useful for the aviation sector, including wind, precipitation, freezing rain, icing forecasts and indices, and hail.

To further complete our offering and meet industry needs, Meteomatics has also developed specialized convection and turbulence parameters optimized for our high-resolution 1k models. 

These include convective categories, convection top height, and turbulence EDR, giving aviation professionals tools to predict where and when strong storms and severe turbulence will happen, and how intense they’ll be. 

This helps increase situational awareness for flight dispatchers, air traffic controllers, and pilots using Electronic Flight Bags (EFBs) in the cockpit.

Convective Categories

Convective categories show how likely clouds are to become stormy. A high category (3 or above) means there’s a good chance of strong storm activity and thunderstorm clouds forming. 

By knowing the likelihood of storm activity, pilots and flight planners can adjust routes to avoid turbulent or hazardous areas, ensuring smoother and safer flights.

Thunderstorm Height

This parameter provides the height of a thunderstorm within the specified time interval. A value is returned only if a thunderstorm occurrence is likely.

The height of a thunderstorm is an important indicator of its strength and intensity. Tall thunderstorms are more likely to produce severe weather, such as strong winds, heavy rain, hail, and lightning. For aviation, knowing the height of thunderstorms is essential for avoiding turbulence and other hazardous conditions, allowing dispatchers and pilots to navigate safely around stormy areas.

Turbulence Eddy Dissipation Rate (EDR)

EDR is considered the industry standard for anticipating and assessing the intensity of turbulence along a flight path. Knowing the level of turbulence helps pilots adjust their altitude to avoid rough areas, making the flight more comfortable for passengers and putting less strain on the aircraft. It also helps save fuel and makes flights more efficient overall.

The animation below shows the storms in Spain on October 29, 2024, captured by Meteomatics’ EURO1k model. It gives an overview of the precipitation (left), EDR turbulence (middle), and the convective index (right), with extensive dark red areas indicating intense thunderstorms. In this scenario, turbulence comes not only from the powerful convection from storm clouds, but also from the upper-level air disturbances and the ripple effects generated by these massive storms.

Extended Lead Time to 15 Days

We’ve integrated the latest ECMWF IFS model upgrade (Cycle 49r1), which introduces significant advancements to both the IFS model and the data assimilation system.

The most significant improvement is the extension of the lead time from 10 to 15 days, which substantially enhances the data quality of the Meteomatics Mix model for days 10 to 15. This extended lead time is valuable for all industries, particularly for our energy products, including wind and solar power forecasts.

Additional enhancements in Cycle 49r1 include the assimilation of 2m temperature observations, updates to the land-surface model, improved resolution and soft-recentering of the ensemble of data assimilations (EDA), and the activation of the stochastically perturbed parameterisations (SPP) model uncertainty scheme across all ensemble configurations. With ECMWF-IFS being a key model in our forecast mix, these improvements further enhance our forecast skill.