Monitoring Emissions Greenhouse Gases by using sciamachY

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MEGGY

•  Monitoring Emissions Greenhouse Gases by using
  sciamachY
•   
•  
• Michiel Roemer
• Maarten van Loon
• Peter Builtjes
• Peter Zandveld
• Michiel van Weele
• Peter van Velthoven
• Guus Velders

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• Objective Can we use satellite data (sciamachy,
  ), and other data to calculate source strengths?
• Tools
• TM3 model, coarse mode
• Extended Kalman filter
• Synthetic data set of methane columns (produced
  by TM3)
• Approach
• Base run (model results with normal
  emissions)
• Measurement run (model results with adjusted
  emissions)
• Assimilation run (result of the Kalman filter to
  detect adjusted emissions)
• Emissions in one or more continents were changed
  (measurement run). Is this detectable by the
  assimilation?
• Problems
• Emission estimate depends on modeled OH
• Assimilation becomes very costly if it depends on
  atmospheric lifetime of methane

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Global scale

• Methane observations at ground-level background
  stations
• (NOAA-CMDL)
•  
• Approximately 70 stations
• Sampling frequency 2 x week
• Status Proven technique
•  
• Methane column observations by SCIAMACHY
•  
• Global coverage each 3 days
• Pixel size 320 x 25 km (25 x 25 km)
• Status to be validated

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Data-assimilation global scale

• A global dispersion model (TM3) with methane only
• ( chemical degradation)
•  
• Minimising the difference between model output
  and
• measurements by adjusting emissions (or other
  model
• parameters)

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Emission adjustment factors (averaged over
Jan.-June period) calculated in assimilation mode.
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Conclusions

• It is possible to retrieve (sub)-continental
  scale emissions from
• satellite column observations if the measurements
  have an
• uncertainty of less than 2.
•  
• It is recommended to set up an emission retrieval
  system that
• consists of two elements
•  
• 1) emissions on (sub)-continental scale by means
  of satellite
• measurements and a global model
•  
• 2) information from (1) to provide boundary
  conditions for
• smaller scale models and use smaller scale
  models and local
• measurements to retrieve emissions on a national
  and
• smaller scale

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• MEGGY-2 to continue MEGGY-1 but now with real
  data, and to expand to smaller scales
• Accurate methane columns are needed which are not
  produced yet
• Alternative to bridge gap between MEGGY-1 and 2
  NO2 (NOx) emission estimate through GOME
• Tools
• TM3 model, finer mode and LOTOS model
• Extended Kalman filter
• GOME NO2 data, ground level data
• Use GOME and TM3 to make continental scale
  estimates of NOx emissions
• Use the results to constrain LOTOS concentrations
• Use GOME and ground-level NO2 to make national
  scale estimates of NOx emissions
• Disadvantage more chemistry no Kyoto species
• Advantage much larger gradients, experience with
  real data, start now