Investigating the Value of Adding Diverse ML Models to the IMPROVER Blend
Reference ocds-h6vhtk-078a0f · Met Office
Awarded to UNIVERSITY OF EXETER for £297,014.
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Amounts and figures
Description
The central research question is: Does incorporating a broader and more diverse range of machine learning weather forecasting models into the IMPROVER blend deliver sufficient forecast improvement and operational value to justify the additional complexity of implementation and operation? The Met Office is particularly interested in understanding: • The value of model diversity within forecast blends. • Whether AI models provide additional skill when blended with existing NWP systems. • The marginal benefit of adding successive AI models. Whether a point of diminishing returns exists beyond which additional models provide little operational value. • Which combinations of models deliver the greatest forecast benefit. • Where benefits are largest? For example, at what lead times and for what parameters? • Whether complementary behaviour between models is more important than individual model skill alone. • Whether increasing the number of MLWP members in the blend could compensate for fewer NWP ensemble members in the blend The work should focus on operationally relevant questions rather than purely theoretical investigations. While forecast skill improvements are important,…
Awards
| Lot | Awarded to | Amount | Date |
|---|---|---|---|
| 1 | UNIVERSITY OF EXETER | £297,014 | — |
Official publications
| Medium | Reference | Date |
|---|---|---|
| FTS | 098875-2026 | 20 October 2026 |
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