Physics Maths Engineering
Use of CAMS near Real-Time Aerosols in the HARMONIE-AROME NWP Model
Daniel Martín Pérez,
Emily Gleeson,
Panu Maalampi,
Laura Rontu
Peer Reviewed
Related Subjects
Abstract
Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration.
Key Questions and Answers
1. What is the focus of the study on the use of CAMS near real-time aerosols?
The study investigates the use of near real-time aerosol data from Copernicus Atmospheric Monitoring Services (CAMS) in the HARMONIE-AROME NWP model, examining its impact on radiation, precipitation patterns, and cloud droplet number concentration.
2. How does the CAMS data contribute to the HARMONIE-AROME model?
CAMS aerosol mass mixing ratios are introduced into the model's first guess and lateral boundary conditions, and are advected by the model dynamics, influencing cloud microphysics and radiative transfer processes.
3. What are the key outcomes of the study?
The study found improvements in radiation, especially during desert dust events, altered precipitation patterns, reduced spurious fog, and enhanced shortwave radiation forecasts due to better cloud droplet number concentration estimation.
4. What impact did the use of aerosol data have on fog prediction?
The integration of CAMS aerosol data led to improvements in fog forecasting by reducing spurious fog predictions in the HARMONIE-AROME model.
5. How does the study contribute to aerosol-related NWP models?
The study introduces a practical approach to using real-time aerosol data in weather forecasting models, highlighting its effect on cloud-precipitation microphysics, radiation, and fog prediction, thereby enhancing the accuracy of forecasts.
