On utilization of OMI products in the atmospheric composition monitoring and evaluation of dispersion models
M. Sofiev, J.Vira, M.Prank,
J.Tamminen, J.Hakkarainen,
J.Soares, R.Vankevich, M.Lotjonen
Outlook
• Introduction
• Example 1: Eyjafjallajokull
� Means of the plume monitoring
� Models, satellites, and in-situ: who does what
• Example 2: air quality model verification with OMI
• Example 3: CALIPSO aerosol profiles
• Example 4: MODIS/AATSR/SEVIRI wild-land fires
• Summary
� user requirements
Eruption of Eyjafjallajokull (Iceland), 14.4→→→→c.m.
• 15.4: first runs, emergency-type setup� Relative source strength, 3, 8km height
• 16.4: first source calibration, results open at SILAM Web site� Source height up to 8km (IMO report)� Strength: left relative
• 18.4: second source calibration, first ensemble, first hemispheric forecast� Source strength: 2 tons/sec of ash� ECMWF 00 and 12 UTC, 120 hours fcst� HIRLAM 00, 06, 12, 18 UTC, 54 hours fcst
• 18.4 – c.m. operational ensemble forecast, output at http://silam.fmi.fi, daily source recalibration, periodic hemispheric forecast
• 19.5 – c.m. operational hemispheric forecast
How to monitor the plume?• Dispersion models
� YES!– immediately available via rapid-response national emergency systems
– provide detailed 4-D information about the plume dispersion– can (roughly) estimate uncertainties of the predictions
� BUT…– source term is unknown, thus absolute concentrations are unknown
… Release is strongly irregular, which will be missed by the models
– injection height is poorly known, thus dispersion pattern is unreliable
• In-situ observations: practically useless for high plumes• In-situ 3D (lidars, baloons, aircrafts, …)
� YES!– arguably the most accurate observations
� BUT…– need the plume to reach the site
– can mix it up with e.g. anthropogenic pollution– Essentially point-wise, do not show the plume as a whole
How to monitor the plume? (2)
• Satellites
� YES!
– quickly available after the satellite overpass
– can provide a good map of the plume
� BUT…
– GEO satellites have problems at high latitudes, LEO satellites provide just a couple of shots per day
…clouds obscure large areas
– cannot distinguish the plume from e.g. anthropogenic pollution
– complicated algorithms, many assumptions that can be wrong in a specific case
• Conclusion: no golden key, ALL sources of information must be used in a complementary manner
Source calibration: manual, daily
SILAM AOD→
MODIS AOD OMI AAI↓ ↓→
15.4.2010
Plume dispersion: SILAM-EC hemispheric forecast
Observations
• Lidars in Switzerland, Germany, UK
• Sun photometers network
• Balloons in Finland, aircrafts in Germany, Iceland, and Finland
• OMI AOD, SO2, and aerosol indices
• MODIS AOD
• AATSR, SEVIRI, MISR, ....
MODIS, 16.4
OMI, SO2, 19.4
OMI + SILAM, jointly
Example 2: OMI for AQ model evaluation
• Setup of the experiment
� 8 regional + 2 global AQ models compute atmospheric pollution including NO2 for 2008-2009 over Europe
� OMI NO2 in column is taken for the same period
� maps are compared for winter and summer seasons
Outcome
Individual models vseach other and OMIAugust 2008
Outcome (2): SILAM sensitivity study
• Conclusion: the trick is in chemistry. SILAM own combined gas-phase and heterogeneous chemistry seem to do better job with regard to NO2 background
� Not for ozone, though
a) b) c)
50km emission 50km emission + low diff CB4 chemistry (!)
Example 3: overdoing may cause confusion
• A model & satellite experiment of detecting the height of the wild-land fire smoke
• Preparatory task: identify the fires whose smoke was observed by CALIPSO
• Model setup
� Time and region: August 2006 and 2007, Europe, ECMWF meteo data, resolution 20 km; 26 vertical layers up to >6 km
� Observations: CALIPSO measurements AOD(location, heigth)
� Modelling analysis: source apportionment via footprint computation
� Looking for: point sources of particulate matter
August 2006
August 2007
Example 4: satellite observations of wild-land fires
On integration of Fire Assimilation System and chemical transport model for monitoring the impact of wild-land fires on atmospheric composition and air quality
M. Sofiev1, R. Vankevich2, J.Soares1, M.Lotjoinen1, J. Koskinen1, J. Kukkonen1
1 Finnish Meteorological Institute2 Russian State Hydrometeorological University
Russian State Hydrometeorological University
Summary
• A non-trivial task usually requires a combination of all tools to monitor the development
� Eyjafjallajokul: the SILAM dispersion model, lidars and satellite products from several instruments
• Source calibration became possible from AOD products and, in principle, can be performed in real-time
� data assimilation in emergency case is an expensive tool but methodologies exist that reduce its costs
Summary: user requirements
• More, more, more, better, better, better…
• By far the most widely used instrument is MODIS – who comes after ?
• User’s thoughts aloud
� For modelling purposes “mean” pictures are of little use due to short model memory. The information comes from individual time-labeled frames, via data assimilation
� No need to solve inverse problems if forward ones are in reach!
– Models are to mimic what satellites measure, NOT the other way round
� The information should come with uncertainty estimation – the real one, not just precision of devices
� If something is unknown, that should be stated. Unsupported guesses bring only noise and damage the results
Acknowledgements
• Projects
� EU-FP 7 GEMS and MACC
� ESA PROMOTE, Ozone SAF
� Academy of Finland IS4FIRES
• Observational data producers
� MODIS, OMI, and CALIPSO teams
� MeteoSwiss
� AERONET sun photometer network
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