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Forecasting Air quality in China Using
CAMS Boundary Conditions:
the PANDA Project
Guy P. Brasseur
and Idir Bouarar
June 206
The PANDA ProjectCoordinator: Guy Brasseur
Deputy Coordinator: Prof. Xuemei Wang
Period: Jan 2014 - Dec. 2016
Budget: 2 Millions Euros
Some Elements of the AQ
Downscaling System
• Boundary and Initial Conditions (CAMS C-IFS))
• Emissions (natural, anthropogenic)
• Weather forecasts (IFS, NCEP, etc.)
• Representation of PBL processes
• Learning from daily diurnal predictions
Assimilation
in CAMS
WRF-Chem prediction
20x20km
MACC forecast/reanalysis as IC & BC
WRF-Chem 60x60km
20x20km
Downscaling to Regional Scale in Asia
7 x 7 km
Satellite data
Air Quality Index (AQI)
WRF-Chem prediction
AQI
PANDA Methodology
Global Model
Ensemble of Regional
60 km
Ensemble of Regional
Models
60 km
Local AQ Model Local AQ Model Local AQ Model
Ensemble of Sub-
20 km
Ensemble of Sub-
regional Models
20 km
Satellite
Observations
AssimilationCAMS
operational
validation
Dissemination of AQ predictions
validationvalidation
validation
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS
Do initial and boundary conditions matter?
Monthly mean surface O3 concentrations for January 2010 simulated by
WRF-Chem using MOZART (left) and MACC (right) initial and boundary
conditions.
MOZART ECMWF
Idir Bouarar
Do emissions matter?
CO
NOx
WRF-Chem simulations
(Jan. 2010) with:
-HTAPv2 emissions (HTP)
-REASv2 emissions (RAS)
-MACCity emissions
(MCT)
With HTAPv2
Absolute differences with
RAS and MCT simulations
Formulation of the PBL
• Surface concentrations depend critically on adopted PBL parameters.
• Height of the PBL (which decreases abruptly in early evening)
• Vertical mixing, specifically in the nighttime PBL.
• Needs adjustment in urban areas to account for heat island effects and mechanical turbulence generated by the buildings
• How well are the models doing?
PBL Height
Diffusion Coef. At 18:00 Diffusion Coef. At 19:00
Below:
Nightime Vertical
exchange coefficient in
rural areas
In urban areas, this
coefficient is set equal to
2 m2 s-1
Rural nighttime PBL
Daytime PBL
Effect of increasing diffusion in the urban area of Beijing
CNTRL: Control run (2 m2 s-1)
TED: run with increased vertical diffusion from 2 to 10 m2 s-2
Conclusions
• CAMS predictions provide unique data used as boundary conditions for regional predictions in Asia.
• CAMS assimilates data and accounts for highly variable sources such as fire emissions and global meteorology.
• This allows regional predictions to be “relaxed” to space observations and to account for long-range influences.
Towards a Constellation of Similar Initiatives?
• North America
• South America
• Africa
• Europe
• Asia
• Russia
• Driven by the same global models and space observations, using similar methodologies, data bases, models, and providing the same type of products and services.
• MAP-AQ Initiative in support of WMO/GAW project
MAP-AQ
Modeling, Analysis and Prediction of Air Quality
To develop and implement a global air pollution monitoring, analysis and prediction system with downscaling capability in
regions of the world (e.g., Asia, Latin America, Africa) that are affected by high levels of atmospheric pollutants.