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Training Data assimilation and Satellite Data – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry

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Presentation on theme: "Training Data assimilation and Satellite Data – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry"— Presentation transcript:

1 Training Data assimilation and Satellite Data – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry

2 Training Data assimilation and Satellite Data – Johannes Flemming Overview Motivation Basic concepts of atmospheric chemistry modelling Data assimilation of trace gases Observations Chemical data assimilation at ECMWF (GEMS & MACC) Examples for O3 and SO2 Summary

3 Training Data assimilation and Satellite Data – Johannes Flemming Why Atmospheric Chemistry at NWP centres ? - or in a NWP Training Course? Environmental concern Air pollution Ozone hole Climate change Expertise in data assimilation of satellite, profile and surface obs. Best meteorological data for chemical transport modelling Interaction between trace gases & aerosol and NWP radiation triggered heating and cooling precipitation (condensation nuclei) Satellite data retrievals improved with information on aerosol Hydrocarbon (Methane) oxidation is water vapour source

4 Training Data assimilation and Satellite Data – Johannes Flemming Change in Aerosol Optical Thickness Climatologies New: reduction in Saharan sand dust & increased sand dust over Horn of Africa Old aerosol dominated by Saharan sand dust 26r3: New aerosol (June) Tegen et. al ): 26r1: Old aerosol (Tanre et al. 84 annually fixed) Thickness at 550nm Example: Impact of Aerosol Climatology on SW Radiation J.-J. Morcrette A. Tompkins

5 Training Data assimilation and Satellite Data – Johannes Flemming Surface Sensible heat flux differences 20 W m -2 ~ 20-30% Boundary layer height increases >1km Example: Impact of Aerosol Climatology on SW Radiation old new New-old

6 Training Data assimilation and Satellite Data – Johannes Flemming Improved Predictability with improved Aerosol Climatology Published in Quart. J. Roy. Meteorol. Soc., 134, (2008) Rodwell and Jung Figure 3: Average anomaly correlation coefficients (see main text for details) for forecasts of meridional wind variations at 700 hPa with the `old' (solid) and the `new' (dashed) aerosol climatology for (a) the African easterly jet region (15oW.35oE, 5oN.20oN) and (b) the eastern tropical Atlantic (40oW.15oW, 5oN.20oN). Forecast lead-times for which the score with the `new' aerosol is signicantly better (at the 5% level) are marked with circles. Results are based on the weather forecasts (see main text for details) started at 12 UTC on each day between 26 June to 26 July 2004.

7 Training Data assimilation and Satellite Data – Johannes Flemming An other motivation …

8 Training Data assimilation and Satellite Data – Johannes Flemming Modelling atmospheric composition Gas phase Transport Source an Sinks Chemical conversion Emissions Deposition

9 Training Data assimilation and Satellite Data – Johannes Flemming N2N2 O2O2 H 2 O Argon 20% 78% 1% N2ON2O 310 H2H2 CO Ozone ppb 1:10 9 CO 2 CH 4 (1.8) ppm 1: Ne 18 He (5) HCHO 300 Ethane SO 2 NO x ppt 1:10 12 NH CH 3 OOH 700 H2O2H2O2 500 HNO others Atmospheric Composition The small concentrations do matter because chemical conversion is non-linear small concentrations could mean high turn-over, i.e. high reactivity

10 Training Data assimilation and Satellite Data – Johannes Flemming Emissions Chemical Reactions Atmospheric Reservoir wet & dry Deposition Transport catalytical Cycles Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg Atmospheric Chemistry Photolysis

11 Training Data assimilation and Satellite Data – Johannes Flemming Modelling atmospheric composition Mass balance equation for chemical species ( up to 150 in state-of-the-art Chemical Transport Models) Source and Sinks Transport

12 Training Data assimilation and Satellite Data – Johannes Flemming Examples of Global Mass Budget Global transport contribution is zero if model conserves mass

13 Training Data assimilation and Satellite Data – Johannes Flemming Nitrogen Oxides - sources and sinks Total Columns Concentrations Surface Emissions Chemical Production and Loss & Lightning Vincent Huijnen, KNMI MOZART-3 CTM Note: High Loss is related to high concentrations

14 Training Data assimilation and Satellite Data – Johannes Flemming Some very general remarks about gas phase chemistry in the Atmosphere … Under atmospheric conditions (p and T) but no sunlight atmospheric chemistry of the gas phase would be slow Sun radiation (UV) splits (photolysis) even very stable molecules such as O 2 (but also O 3 or NO 2 ) in to very reactive molecules These fast reacting molecules are called radicals and the most prominent examples are O mainly in stratosphere and above, but also in troposphere OH (Hydroxyl radical) and HO 2 (peroxy radical) in troposphere Reaction with OH is the most important loss mechanism in the troposphere for very common species such as CO, NO 2, O 3 and Hydrocarbons We need to quantify the concentration change due to chemistry

15 Training Data assimilation and Satellite Data – Johannes Flemming Chemical Kinetics (I) Gas-phase reactions A + B C + D A + B C C A + B A + B + M C + D + M Photolytic reactions if < limit A + h B + C heterogeneous (aerosol-, liquid-phase) reactions surface reactions often A+B C

16 Training Data assimilation and Satellite Data – Johannes Flemming Chemical Kinetics (II) Reaction speed (= concentration change per time) is proportional to product of concentration of reacting species Example: A + B C Example: Photolysis A + h B + C Chemical loss of A is proportional to concentration of A

17 Training Data assimilation and Satellite Data – Johannes Flemming Detour … Sub-grid scale chemistry parameterisation?? The non-linear nature of the chemical Kineticis leads to a potential influence of the spatially unresolved sub-scale variability on the model resolved scale (turbulent reynolds averaging) There is no solution for sub-grid scale variability of chemistry yet No solution to this problem yet Well mixed – reaction occurs Not mixed – no reaction

18 Training Data assimilation and Satellite Data – Johannes Flemming Chemical loss and production - lifetime Chemistry is budget of loss ( L ) and production ( P ) rate As loss rate is always proportional ( l ) to concentration of A Usually mag( L ) similar mag( P ) Loss rate coefficient l is often proportional to [OH] in troposphere Chemical lifetime τ determines transport scale Only a subset of species is transported ( lifetime > 10 min)

19 Training Data assimilation and Satellite Data – Johannes Flemming into stratosphere No transport modelled Chemical Lifetime vs. Spatial Scale

20 Training Data assimilation and Satellite Data – Johannes Flemming Ozone cycle in Stratosphere (Chapman, 1930)

21 Training Data assimilation and Satellite Data – Johannes Flemming Ozone Production in Stratosphere (Chapman 1932) The shape of the ozone profile can be qualitatively explained by the derived ozone production (a) is most efficient in upper atmosphere (more UV input) (b) is most efficient in lower atmosphere (higher density) Chemical EquationsKinetics equations Assumptions

22 Training Data assimilation and Satellite Data – Johannes Flemming Ozone profile and life time predicted with Chapmann Cycle theory observed Catalytic reactions with NOx, HOx, ClOx and BrOx

23 Training Data assimilation and Satellite Data – Johannes Flemming Data assimilation of atmospheric composition Observations Assimilation System Examples

24 Training Data assimilation and Satellite Data – Johannes Flemming Special Characteristics of Atmospheric Chemistry data (vs NWP) assimilation Quality of NWP depends predominantly on Initial conditions whereas Atmospheric Chemistry modelling depends on initial state (lifetime) and emissions Emissions data are uncertain and difficult to measure and biased Chemical atmospheric fields have strong horizontal and vertical gradients for atmospheric composition small scale emission variability Heterogonous reactions on surface Observation Limited representativeness Sparse Poor near real time availability

25 Training Data assimilation and Satellite Data – Johannes Flemming Air quality observations at surface (… biased toward polluted areas ) NO2 annual mean in Berlin Regional model (25 km) vs. Air quality observations Large variability of observations Within GRID box Is the model result good ? Could data assimilation improve model result?

26 Training Data assimilation and Satellite Data – Johannes Flemming Profile Observations ( … far to few) Ozone sondes- GAW stations MOZAIC flight observations

27 Training Data assimilation and Satellite Data – Johannes Flemming Satellite observations Assimilation of retrievals vs. radiances retrievals analyses Radiance assimilation

28 Training Data assimilation and Satellite Data – Johannes Flemming DOAS analysis Total Slant Column Tropospheric Slant Column Tropospheric Vertical Column SCIATRAN RTM (airmass factor) A priori information needed: aerosol loading and profile vertical profile of the observed species surface reflectance at time of measurement Obtained from climatologies or Models Problems: spatial resolution loss of independence of measurement and model clouds NO2 retrievals from satellite observations Joana Leitao. Uni Bremen Retrieval of trop. NO2 from SCIAMACHY measurements

29 Training Data assimilation and Satellite Data – Johannes Flemming Special Characteristics of Atmospheric Chemistry satellite observations Total or partial column retrieved from radiation measurements No or only low vertical resolution Ozone (and NO2) dominated by stratosphere Weak or no signal from planetary boundary layer Global coverage in a couple of days (LEO) Limited to cloud free conditions Fixed overpass time (LEO) – no daily maximum Retrieval algorithms are ongoing research

30 Training Data assimilation and Satellite Data – Johannes Flemming MOPITT CO (TC) Data count Example April 2003 Very few observations in tropical regions (clouds) Only Land Points were assimilated Data have been thinned to 1°x1° grid

31 Training Data assimilation and Satellite Data – Johannes Flemming CO total column retrievals from different instruments/ retrievals MOPITT- retrieval IASI - retrieval A IASI – retrieval B Different retrievals tend to differ …

32 Training Data assimilation and Satellite Data – Johannes Flemming Trace gas assimilation system at ECMWF Stratospheric Ozone with linearized ozone chemistry since 1999 GEMS-project ( ) / MACC-project ( ) Ozone in troposphere and stratosphere CO, SO2, Formaldehyde, NOx, Aerosol and CO and CH4 Full Chemistry (CTM MOZART-3)

33 Training Data assimilation and Satellite Data – Johannes Flemming GEMS / MACC Global Production

34 Training Data assimilation and Satellite Data – Johannes Flemming The IFS is coupled to a CTM for data assimilation of atmospheric composition Meteorology Tracer Concentrations Production/Loss Initial Conditions Concentration feedback

35 Training Data assimilation and Satellite Data – Johannes Flemming ECMWF 4D-VAR Data Assimilation Scheme Assimilation of Reactive Gases and Aerosol transport + chemistry advection only transport + chemistry

36 Training Data assimilation and Satellite Data – Johannes Flemming Reactive gases assimilation – approach in GEMS and MACC Include NO x, SO 2, O 3, CO and HCHO species in IFS (Transport and Assimilation) Introduce source and sinks by coupling with Chemical Transport Models MOZART (MPI-Hamburg), TM5 (KNMI), Mocage (Meteo France) 1. Assimilate species in IFS with existing 4D-VAR implementation developed for meteorological fields Apply coupled system in out loops (forward trajectory run) only NMC method (i.e. differences to different meteorological forecasts) to obtain background error statistic Implement and test feedback mechanism to coupled CTM Implement diagnostic NO2 (fast chemistry, observed) to NOX (slow chemistry, modelled) observation operator

37 Training Data assimilation and Satellite Data – Johannes Flemming Assimilation of Ozone Dominated by the stratosphere Assimilated Ozone retrievals Total Columns from UB-VIS instruments (OMI, SCHIMACHY and SBUV) Low-resolution stratospheric profiles from Microwave-Limb-Sounder (MLS)

38 Training Data assimilation and Satellite Data – Johannes Flemming Ozone Total Columns – Inter-annual variability in GEMS re-analysis

39 Training Data assimilation and Satellite Data – Johannes Flemming Ozone Hole Development Winter – no sun light: Cold stable Vortex -> formation of Polar Stratospheric Clouds Accumulation of Chlorine/Bromine compounds on PSC surface Spring – gradually more sun-light Rapid release of CLO on PSC surfaces Quick catalytic destruction of ozone –> ozone hole Vortex becomes more permeable – closure of ozone hole

40 Training Data assimilation and Satellite Data – Johannes Flemming Temperature and O3 over South Pole – Sonde observations Temp O3 Ozone Hole Closure by transport PSC FormationChlorine Activation

41 Training Data assimilation and Satellite Data – Johannes Flemming Antarctic Ozone Hole 2008 Different Modelling Schemes for the Chemistry Operational ECMWF ozone with O3 chemistry parameterisation NRT coupled-system IFS-MOZART with full stratospheric chemistry: coupled-system IFS-TM5 with stratospheric O3 climatology Each scheme was run with (AN) and without (FC) data assimilation of O3 satellite observations at 0 UTC to provide initial ozone conditions Assimilated Observations Total columns (OMI, SCHIAMACHY, SBUV) Stratospheric partial Columns (MLS) Questions: Inter-instrument biases Impact of different chemistry schemes

42 Training Data assimilation and Satellite Data – Johannes Flemming Instument - Biases over Antarctica MLS can observe during polar night Biases small MLS can observe during polar night Large differences due to different sampling Actual Biases are small (2-3%)

43 Training Data assimilation and Satellite Data – Johannes Flemming Total Columns vs. Ozone Sondes Without assimilationWith assimilation

44 Training Data assimilation and Satellite Data – Johannes Flemming Ozone hole size with different CTMs and assimilation No assimilation Assimilation Forecast initialised by analyses every 15 days

45 Training Data assimilation and Satellite Data – Johannes Flemming Vertical Profiles at Neumayer Station IFS Linear Chemistry MOZART Full Chemistry TM5 Climatology No MLS

46 Training Data assimilation and Satellite Data – Johannes Flemming Assimilation of Volcanic SO2

47 Training Data assimilation and Satellite Data – Johannes Flemming Volcanic eruptions are a major natural SO2 source Volcanic eruptions can penetrate the tropopause SO2 is a aerosol precursor (SO4) SO2/SO4 is long-lived in stratosphere Volcano ash emission are an aviation hazard Volcanic Eruptions

48 Training Data assimilation and Satellite Data – Johannes Flemming Volcanic SO 2 assimilation experiment Questions: What is the Volcano SO 2 / ash flux What is injection height and plume height Can assimilation of satellite data pick up plume or do we need to model the dispersion of the SO 2 / ash emissions in the assimilating model Case study of Nyamuragira eruption 27/11/06- 4/12/06 Observations: SCIAMACHY SO 2 total column (BIRA) - Assimilated OMI SO 2 total column (plots) for comparison – Not Assimilated

49 Training Data assimilation and Satellite Data – Johannes Flemming Iceland Volcano Plume Forecast Injection height 3, 5 and 10 km

50 Training Data assimilation and Satellite Data – Johannes Flemming OMI SO2 columns [DU] from SO 2 total column observations SCIAMACHY SO2 columns 1.12 OMI plots to estimate Volcano flux and injection height Increase in total column – loss = flux estimate Test runs with variable injection height to match with observed plume Background error profile constructed according to estimated plume height SO2 background error stdv profile

51 Training Data assimilation and Satellite Data – Johannes Flemming SO2 forecast assimilation: Total column SO2 1 Dec Dobson Units 3 Dec5 Dec7 Dec Control run – no assimilation) Tracer run injection height 14 km – resembles OMI SO2 SO 2 Forecast SO 2 tracer emission estimated from OMI SO 2 day to day total column change

52 Training Data assimilation and Satellite Data – Johannes Flemming SO2 assimilation: Total column SO2 1 Dec, 0z Dobson Units 3 Dec, 12z5 Dec, 12z7 Dec, 12z Assimilation SO2 (no source) Assimilation with SO2 volcano emission SO 2 Assimilation Assimilation without SO 2 volcano emissions fluxes provides reasonable plume forecast if injection height is correctly guess (Background error statistics)

53 Training Data assimilation and Satellite Data – Johannes Flemming Summary Atmospheric composition and weather interact Chemical lifetimes determine to what extent species are distributed in the atmosphere Emissions are often uncertain but greatly determine concentrations Chemical data assimilation has to deal with very heterogeneous fields Chemical data assimilation should also help to improve emissions MACC forecast system produces useful forecast and robust data assimilation products Re-analysis of Ozone, CO and Aerosol ( ) and NRT forecast up to present are available at

54 Training Data assimilation and Satellite Data – Johannes Flemming Thank You !

55 Training Data assimilation and Satellite Data – Johannes Flemming Data from Zugspitze, Schneefernerhaus, 2650 m a.s.l. SO 2 mixing ratio (contact: paticle > 10nm (UBA measurements, (contact: SO SO 2 mean 2000 – 2007 particle > 10 nm Data from Schneefernerhaus/Zugspitze 2650m a.s.l. Source: Anja Werner-DWD

56 Training Data assimilation and Satellite Data – Johannes Flemming Improvements in the IFS stratosphere and mesosphere by using improved (assimilated) CO, CH4 and O3 climatology old new Temperature ErrorZonal Wind error New: Non-Orographic gravity wave scheme and GEMS climatology for Radiation P. Bechtold


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