The meteoblue Learning MultiModel (mLM) is a new technique of post-processing the output from numerical weather prediction (NWP) models using current weather measurements. The mLM gets current weather measurement data and, among a set of models, selects the one with the most accurate simulation, to then run the forecast.

Currently, the mLM is validated and implemented for air temperature, dewpoint temperature and wind speed. The development of the mLM for further other weather variables is planned.

Temperature-mLM

For temperature, the model accuracy of the mLM was tested for one year before introduction in August 2018.

The operational model accuracy of the mLM was verified for a period of 2 months (September and October 2018) on more than 30.000 meteorological stations worldwide. This analysis showed a model accuracy of 1.2 K for the 24-hour (24h) forecast and a model accuracy of 2.0 K for the 6-day forecast. The model accuracy of the mLM 24h forecast is significantly better than established standards:

• 0.8 K better than using 'stand-alone' numerical weather forecast models (24h forecast).
• 0.3 K better than models simulations using MOS .
• 0.3 K better than the reanalysis model ERA5 (which uses measurements for model correction).

We could demonstrate that the 6-day air temperature forecast of the mLM is as good as the 24h forecast of 'stand-alone' numerical weather forecast models. This improvement corresponds to the average improvement achieved by weather forecast every 10 years over the past decades.

Validation results for the further variables will be added.

For further information, please refer to the technical documentation.