"New ECMWF Model Versions: 50r1 and AIFS V2s"

Q1: What is the difference between AIFS-Single and AIFS-ENS Control member?

...

Noted. To help with this we could learn not just from analyses but also from short range forecasts (which we already do for precipitation).

"Extreme / Interesting Case Studies"

Extreme cases reported

A representative from the World Energy and Meteorology Council  (UEA, Norwich) raised the issue of extreme cold events, especially how well these can be predicted when they are linked to broader weather types, and asked if there was any verification for weather regime forecasts.*

The representative from Serbia was working on regional climate change and reported that they have started monitoring forecast extremes (T2m, precipitation) using forecasts (e.g. EFI) as part of a rapid weather attribution study. Croatia highlighted an extreme wind event in northern Croatia that was well predicted seven days in advance. DMI (Denmark) identified an early cold spell in late October last year as a significant event and noted that the IFS appeared to perform well in that case, although capturing extreme low temperatures was often problematic in the forecasts – they do postprocessing, but it doesn’t do much for the extremes.        

Product queries and replies

The representative from Croatia (an aviation forecaster) asked about the use of EFI wind speed for aviation forecasting, including how well the product performs and whether it has been verified. We explained that ECMWF regularly publishes an annual report that includes EFI verification results. We said the verification of EFI of gusts is more challenging*.

The same representative noted that the CAT products are highly appreciated by forecasters. They also asked about an orographic gravity wave product, and we explained that the CAT product already contains a turbulence-mixing term related to orographic gravity waves*.  They were also interested in tropopause folding as a source of turbulence and that a 2D product identifying the location of tropopause folds could be useful; we noted that the 2 PVU product is available, but it does not explicitly identify  tropopause folds, only the highest altitude of 2 PVU*. The CAT product should already give higher turbulence estimates in these regions, but this could be explored further.

They also mentioned that aircraft icing products (not currently existing from the IFS) would be very useful. The visibility probability meteograms were very much appreciated.The representative from Croatia also asked about forest fires, which are critical for airport operations if there are fires nearby. We showed the EFI fire product (eccharts), the Copernicus web (EFFIS), and CAMS as relevant resources.

Challenging weather events identified by participants

Several representatives highlighted specific weather phenomena that remain particularly challenging in operations. For Montenegro, the main difficulties are fog, wind, ocean-waves (which have poor skill in the IFS in that part of the Adriatic). For the sub-seasonal forecasts cold episodes seem to be less well predicted than heatwaves. The representative also noted that ECMWF is their main source of data. Overall precipitation guidance performs better than their limited-area model, although the IFS tends to over-estimate precipitation over Montenegro, and wind guidance is less satisfactory. In response to a question from Linus about the impact of heat waves on holidaymakers, the representative explained that this is not a major issue because most tourism is concentrated in coastal regions, while heat waves occur more often inland. The representative from Nigeria said precipitation is the most important forecast quantity for them and the most challenging forecast situations are related to precipitation and wind gusts associated with convective storms (noting they’ve seen a lot of storms so far in 2026). We mentioned experimental AEW forecasts and although there hadn’t been much interest in this, he thought other colleagues from the region could be interested. The representative from Mercator (Norway) said the ocean-wave model tends to underpredict the wave height (same for the ENS), and off the coast of Norway it was noted that the extreme waves associated with strong storms were not always correctly placed or represented well in the forecasts.

* Material relevant for each query raised by the participants (& passed to them)

1) Euro-Atlantic Weather Regimes and Their Modulation by Tropospheric and

Stratospheric Teleconnection Pathways in ECMWF Reforecasts

CHRISTOPHER D. ROBERTS,

a MAGDALENA A. BALMASEDA,

a LAURA FERRANTI,

a AND FREDERIC VITARTa

DOI: 10.1175/MWR-D-22-0346.1

2) How far in advance can we predict changes in large-scale flow

leading to severe cold conditions over Europe?

Laura Ferranti Linus Magnusson Frédéric Vitart David S. Richardson

Q J R Meteorol Soc.

2018;144:1788–1802. https://doi.org/10.1002/qj.3341

3) Year-roundsub-seasonalforecastskillfor

Atlantic–Europeanweatherregimes

DominikBüeler1 LauraFerranti2 LinusMagnusson2

JulianF.Quinting1 ChristianM.Grams1

Quarterly

JournaloftheRoyalMeteorologicalSociety,147(741),

4283–4309.Availablefrom:https://doi.org/10.1002/qj.4178

4) https://confluence.ecmwf.int/display/FUG/Section+8.1.11+Potential+vorticity+charts

5) Evaluation and Improvement of the ECMWF Aviation

Turbulence Forecasts

Han‐Chang Ko1, Hye‐Yeong Chun1, and Peter Bechtold2

Journal of Geophysical Research:

Atmospheres,130,e2024JD043158.

https://doi.org/10.1029/2024JD043158

"How would you like to see ECMWF products evolve in future"

Overview:

The meeting focused on gathering feedback on ECMWF products, with particular emphasis on AI-based models, visualisation challenges, and user workflow efficiency. Participants discussed future directions for product development and how to better support operational forecasters.

1. AI models and explainability

- Strong interest in AI-based forecasting products (AIFS), but participants highlighted the need for explainability.

- Trust in forecasts depends on understanding why a model produces a given result, not just the output itself.

- There is currently a gap compared to traditional physics-based models where forecasters have well-developed conceptual understanding.

2. Data overload and efficiency

- A major concern is the increasing volume of data from multiple models, ensembles, and cycles.

- Forecasters do not have more time, so efficiency in accessing and interpreting data is critical.

- There is a need for smarter tools that filter, prioritise, and summarise information rather than adding more raw data.

3. Visualisation improvements

- Current workflows involve mentally integrating multiple datasets, which is inefficient and error-prone.

- Users requested more integrated views combining multiple parameters and models in a single interface.

- Better support for interactive exploration (zooming, layering, custom views) was highlighted.

4. AI for decision support

- AI could help guide forecasters by identifying critical regions, events, and relevant products.

- Concept of chatbot-style interfaces or query-driven dashboards to retrieve tailored information was discussed.

- Potential to automate selection of thresholds, projections, and relevant diagnostics.

5. Product strategy

- Important not to duplicate existing products in AI form without clear added value.

- Focus should be on novel diagnostics and summarised products (e.g. EFI-like indicators, event-based views).

- Products should serve broad needs first, with customisation layered on top.

6. Verification and trust

- Users are interested in real-time or near-real-time indicators of forecast skill.

- Desire for clearer linkage between forecasts and observations to assess reliability quickly.

- Objective verification and comparison between models were suggested enhancements.

7. Customisation and flexibility

- Demand for on-demand computation (e.g. clustering, regional focus, aggregation over different

time windows).

- Need for flexible meteograms and probability-based visualisations tailored to user thresholds.

8. Infrastructure and data access

- Preference for service-based access (querying ECMWF systems) rather than downloading large datasets.

- Importance of standard formats and interoperability across systems.

9. Resolution and regional needs

- Requests for higher resolution visualisation and access to native grid data.

- Regional customisation (e.g. Central Asia) identified as important for some users.

Conclusion:

The discussion highlighted a shift from data provision towards intelligent, user-centric products. Key priorities include improving explainability of AI models, reducing data overload through better summarisation and guidance, and developing flexible tools that align with real forecasting workflows.

"Land Surface Processes and 2m Temperature"

Introductions & Roles:  

• Participants introduced themselves, representing research, forecasting, technical, and agricultural sectors from various countries. Key areas included surface observations, turbulence and boundary layer physics, land surface modeling, snow/ice/glacier processes, and operational forecasting.

Model Performance & Issues:  

• Multiple participants discussed persistent challenges with weather models, especially biases in surface temperature, convection, and cloud cover.
• Dust events in Algeria were highlighted, with models overestimating surface temperature and convection due to poor dust/radiation representation. Feedback was noted for future model improvements.
• Overestimation of low cloud cover in winter/high-pressure situations in England and Sweden was discussed, with possible causes including turbulent mixing and cloud phase transition issues. Task teams are working on these biases.
• Challenges in modeling lakes, snow cover, and their impact on 2-metre temperature were raised, noting limitations in current model handling of frozen lakes and snow-covered surfaces.

Agricultural Applications:  

• Discussion on using weather data for crop modeling and agricultural forecasting, including issues with station representativity, irrigation, and bias correction.
• Machine learning and post-processing approaches were mentioned as potential solutions for capturing temperature extremes and improving forecasts in agricultural contexts.

Extreme Temperatures & Diurnal Cycle:  

• Underestimation of daily maximum temperatures and issues with diurnal cycle representation were noted, especially in dry and desert areas.
• Soil heat transfer and memory effects were discussed as contributing factors, with ongoing work to improve model initialization and snow data assimilation.

Apparent Temperature & Regional Adaptation:  

• Concerns about the apparent temperature calculation in low humidity regions (e.g., Central Asia) were raised, suggesting the need for regional adaptation of equations.

Model Development & Future Plans:  

• Ongoing efforts to improve winter temperature forecasts, snow data assimilation, and adaptive parameter tuning for turbulence and cloud representation.
• Plans to make parameter tuning spatially adaptive to address regional biases, especially in northern latitudes.

Verification & Observations:  

• Discussion on verification challenges, especially with non-representative stations (e.g., airports, cities) and the need for improved assimilation of agricultural and rural observations.

Collaboration & Projects:  

• Mention of the LIAISE project focused on land-atmosphere interactions in irrigated and non-irrigated crop areas, with recent publications and ongoing research.

Summary:  

• The meeting focused on model biases, technical challenges in surface and boundary layer processes, agricultural applications, and ongoing improvements in data assimilation and parameter tuning. Collaboration and feedback were encouraged for future model cycles and regional adaptation.

Action Items from the Meeting

• Dust and Convection Model Biases – Team to investigate and provide feedback on model overestimation of surface temperature and convection during dust events, especially in Algeria.
• Low Cloud Representation in Winter – Task team to continue work on improving cloud and turbulence schemes, particularly for high latitude and winter conditions.
• Agricultural Forecast Verification – Team to compare model outputs with agricultural field data and consider the impact of non-representative meteorological stations (e.g., airports) on verification.
• Parameter Tuning for Turbulence – Plan to implement spatially adaptive parameter tuning for turbulence schemes to improve temperature forecasts, especially at northern latitudes.
• Snow Data Assimilation Improvements – Ongoing work to enhance snow data assimilation and its effect on temperature forecasts, with recent improvements noted and further changes planned for future cycles.