1. Use of Near-Real-Time Data for the Global System

2. GMES Atmospheric Environment Service
MACC – Monitoring Atmospheric Composition and Climate
Integrates space-based and in-situ observations of atmospheric composition with state-of-the art atmospheric modelling
Provides monitoring and forecasting services for atmospheric composition
Helps Europe to respond to climate change and poor air quality

3. provide data & information on
GMES Atmosphere
Services related to the chemical and particulate content of the atmosphere
Weather services
Atmospheric
environmental services
Climate forcing by gases and aerosols
Long-range pollutant transport
European air quality
Dust outbreaks
Solar energy
UV radiation
• • •
Environmental agencies
provide data & information on

4. MACC Users MACC mostly provides services targeted on specialized users
Downstream service providers
Environmental Agencies
Scientists
Policy Makers
Space Agencies

5. MACC Project Structure

6. MACC Daily Service Provision
Air quality
Global Pollution
Aerosol
UV index

7. Computational approach
Input data
Weather,
constituents, emissions,
land and ocean conditions

8. Computational approach
Input data
Weather,
constituents, emissions,
land and ocean conditions
Model

9. Computational approach
Input data
Data assimilation
Model

10. Computational approach
Model E
Model D
Model C
Model B
Model A
Model G
Input data
Data assimilation
Model
Forecast

11. Global data assimilation
Based on ECMWF’s “Integrated Forecasting System” - IFS
CO2 , CH4 and aerosols have been incorporated in the IFS
and data assimilation has been developed for AIRS and IASI radiances, SCIAMACHY retrievals, MODIS aerosol optical depth, … GOSAT …
IFS also carries O3, CO, NO2,
SO2 and HCHO
Chemical production and loss come
from a coupled CTM, either MOCAGE,
MOZART or TM5
Data for assimilation come from GOME,
GOME-2, IASI, MIPAS, MLS, MOPITT,
OMI, SBUV/2, SCIAMACHY, …
Chemistry modules are being built fully into IFS

12. NRT meteorological observations
Meteorological (ECMWF operations)
Radiances
Atmospheric motion winds
Bending angles
Radar backscatter
GPS bending angles
Ozone

13. NRT composition observations
Ozone (O3)
EUMETSAT MSG SEVIRI
EUMETSAT METOP GOME-2
ESA ERS-2 GOME (KNMI)
ESA ENVISAT SCIAMACHY (KNMI)
NOAA SBUV-2
NASA AURA OMI/MLS
Carbon monoxide (CO)
EUMETSAT/CNES METOP IASI (EUMETSAT and CNRS)
NASA TERRA MOPITT (NCAR)

14. NRT composition observations
Nitrogen dioxide (NO2)
EUMETSAT METOP GOME-2 (EUMETSAT and KNMI)
NASA AURA OMI (KNMI)
Sulphur dioxide (SO2)
ESA ENVISAT SCIAMACHY (BIRA)
Aerosol optical depth
NASA AQUA/TERRA MODIS

15. Forecasting desert dust events
MACC successfully forecasted high desert dust aerosol loads over the Mediterranean 2 days ahead of time.
MACC’s desert dust warning index will be a helpful tool for forecast and health authorities.
(Image credit: Jeff Schmaltz, MODIS Rapid Response Team, NASA GSFC)

16. Real-time aerosol forecast
MACC dust aerosol optical depth

17. Science Support
MACC provides support to scientific aircraft observation campaigns by forecasting constituent concentrations around the proposed flight track.
This way, scientists can anticipate what they will likely measure.
Carbon monoxide plume from East Asia reaching the Arctic region for a proposed HIPPO campaign flight (red line).

18. Estimating Greenhouse Gas Fluxes
MACC assimilates observations from both satellites and in-situ networks to monitor atmospheric concentrations of CO2 and CH4.
Atmospheric CO2 analysis
Inferred changes to CO2 fluxes
These atmospheric analyses are then used to infer information about the underlying surface fluxes relative to the prior assumptions.

19. Validation of aerosol against AERONET data

20. Site dominated by biomass burning
Validation of aerosol against AERONET data
Site dominated by dust
Banizoumbou, Niger
Site dominated by biomass burning
Alta Floresta, Brazil
Without assimilation of aerosol data
AERONET
AERONET
MACC total
MACC total
MACC dust
MACC organic PM
Aerosol
Optical Depth at
550nm
With assimilation of MODIS AOD
July Aug Sept
2003
July Aug Sept
2003

21. Towards full operations
Consolidate and improve analysis and forecasting systems
establish basic operational processes for near-real-time running
increase horizontal resolution to 80 km global, 25 km or less regional
refine other aspects of the analysis and forecasting systems
Monitor the quality of products on a systematic basis
Supplement migrated production lines with new services
Liaise with agencies to obtain the observations we need
Liaise with users to supply the products they need
Establish funding and governance for full operations from 2012