State-of-the art models are used to simulate meteorological and air quality indicators and climate change scenarios using high performance computing systems. We deliver short-, mid- and long-term forecasts and historical profiles on LES, local and regional scales. We also perform emission reduction scenarios, that help to understand which emissions to reduce and on which activity source sector one should act and to which extent, in order to improve air quality.
We use very high resolution land cover and topographic datasets that improve our forecasting skill significantly.
The models:
  • WRF
  • WRF-Chem
  • EMEP
  • CALMET, statistical meteorological downscaling

Data Accuracy

We offer accurate high-resolution assessments of meteorological and air quality indicators using state-of-the-art Numerical Weather Prediction (NWP) models.

Evaluation of MetClim forecasting meteorological and air quality indicators is published in peer-reviewed publications, which can be found here:


In summary:


Wind forecasting *

Bias = < ± 3 % Coastal site; < ± 2 % Offshore;  < ± 1 % Land site 

·       Excellent statistics over land when height increases.

·       Excellent representation of the wind rose at all sites.

·       Strong evidence that higher resolution reduces biases.


Global Horizontal Irradiation (GHI) forecasting **

rRMSE = 15.1%, Bias -2.1% 


PM10 forecasting ***

Bias = 3.1% 


*     Participated in the European Wind Energy Association (EWEA) benchmarking exercise. MetClim’s results were third ranked (out of 14 participating international organizations).

**    Compared to ground-based observations.

***   Compared to CAMS re-analysis PM10 concentrations.