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Single is unperturbed, based on minimising an RMSE metric. AIFS-ENS Control is based on a different error (CRPS) minimization metric, and an unperturbed analysis, but has random "diffusion" perturbations applied during the run; it is therefore much less like a typical Control run, in the classical sense.
Q: Precipitation has improved with this cycle because now convective precip is moving. There are still some unrealistic bullseye blobs; is it possible that they those will now "move"?
Yes blobs may still appear, e.g. over Finland. That aspect should have been improved a bit in 50r1, but there is a change that will do more in 51r1.
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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.
* Reference Material relevant for queries raised by participants (& passed to them):
*1) How far in advance can we predict changes in large-scale flow leading 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
*2) Euro-Atlantic Weather Regimes and Their Modulation by Tropospheric and Stratospheric and Stratospheric Teleconnection Pathways in ECMWF Reforecasts
CHRISTOPHER D. ROBERTS, MAGDALENA A. BALMASEDA, LAURA FERRANTI, AND FREDERIC VITART
DOI: 10.1175/MWR-D-22-0346.1
*3) Year-roundsub-seasonalforecastskillfor Atlantic–Europeanweatherregimes
DominikBüeler1 LauraFerranti2 LinusMagnusson2, JulianF.Quinting1 ChristianM.Grams1,
Q J R Meteorol Soc 147(741), 4283–43094283–4309.
Available from: https://doi.org/10.1002/qj.4178
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*5) Evaluation and Improvement of the ECMWF Aviation Turbulence Aviation Turbulence Forecasts=
Han‐Chang Ko1, Hye‐Yeong Chun1, and Peter Bechtold2
JGR: AtmospheresAtmospheres, 130.
https://doi.org/10.1029/2024JD043158
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- Demand for on-demand computation (e.g. clustering, regional focus, aggregation over differenttime different time windows).
- Need for flexible meteograms and probability-based visualisations tailored to user thresholds.
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Model Development & Future Plans:
- Ongoing efforts Responding to user needs, there are ongoing efforts at ECMWF to improve winter temperature forecasts, snow data assimilation, and adaptive parameter tuning for turbulence and cloud representation.
- Plans ECMWF also has plans to make parameter tuning spatially adaptive, to address regional biases, especially biases found in northern latitudes.
Verification & Observations:
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- 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 IFS cycles at ECMWF.