Post on 08-Jan-2018
description
Atmospheric Composition Data Assimilation
Richard Engelen Thanks to Angela Benedetti, Johannes Flemming,
Antje Inness, Luke Jones, Johannes Kaiser, Jean-Jacques Morcrette ,
Anna Agusti-Panareda, Miha Razinger, Martin Suttie, and the many
data providers. Outline Why a talk about atmospheric composition?
Observations
Modelling The art of assimilating L2 retrievals Whats the problem
with aerosol? The link with NWP Whats the difference? Quality of
NWP depends largely on initial conditions whereas atmospheric
composition modelling depends on initial state and boundary
conditions (e.g., emissions) Chemistry is complicated, non-linear,
and not fully known Most processes take place in the boundary
layer, which is not well-observed from space In short: we have a
relatively poorly constrained system with significant model errors
observations Satellite observations
O3, SCIAMACHY, KNMI/ESA O3, OMI, KNMI/NASA AOD, MODIS, NASA NO2,
OMI, KNMI Satellite observations
GOME-2, SACS, BIRA IASI, Univ. of Brussels Atmospheric composition
observations traditionally come from UV/VIS measurements. This
limits the coverage to day-time only. Infrared is now adding more
and more to this spectrum of observations (MOPITT, AIRS, IASI, )
NRT data coverage Ozone CO SO2 NO2 SCIA SBUV/2 NOAA-17 SBUV/2
NOAA-18
MOPITT IASI OMI MLS NO2 SO2 OMI GOME-2 OMI SCIA GOME-2 Future
(example of ozone) Importance of adequate observations
Limb-sounding ozone data assimilated in 2003 (MIPAS) and (MLS)
These data, especially MLS, are clearly beneficial OMI data are
used from July 2007 No LS data modelling Chemical data
assimilation
Based on the 4D-Var scheme of ECMWFs IFS CO2 , CH4 and aerosols are
incorporated in the IFS IFS also carries O3, CO, NO2, SO2 and HCHO
IFS is coupled to chemistry transport model providing the chemical
production and loss terms Chemistry modules are being built fully
into IFS Importance of emissions: Zonal mean total column CO
MOPITT Model run latitude 2003 2007 Assimilation run 2003 2007
Boundary condition problem is easily illustrated on long time
scales, but also applies to more synoptic time scales CO emissions
are too low and model run loses the information from the initial
state. Data assimilation can (partly) correct this problem. [1018
molec/cm2] MACC CH4 inversion system
biased Less biased unbiased Daily time scale Prior fluxes Satellite
data Data Assimilation (ECMWF) In-situ data Flux inversion (JRC)
Optimized fluxes Yearly time scale CH4 bias problems Mean
increments RMS increments It is clear where the work is done over
land. But, a systematic bias over land only, because either the
model is biased or the observations, is not tolerated in a 4D-Var.
Bias correction is not straightforward, because we know we have
errors in the surface fluxes, which are not easy to correct in a
12-hour 4D-Var. Potential solution is currently topic of research.
Chemistry into stratosphere
No transport modelled O3, CO, SO2, NO2, CH4, CO2, aerosol are
currently routinely observed from space in near-real-time NO2 and
NOx at model level 10 (4.2 hPa) 2008-06-05, 0z
Assimilation of NO2 NO2 and NOx at model level 10 (4.2 hPa) , 0z
Fast diurnal NO2 - NO inter-conversion can not be handled with
coupling frequency of 1 hour problems at the day/night boundary Use
NOx=NO2+NO field and inter-conversion operator. NO2 NOx Day time:
NO2 + h -> NO + O Unit: 109 kg/kg NO2/NOx observation
operator
Use NOx as state variable (NO2 observations) Less spatial
variability NOx is not so strongly influenced by solar radiation
Chemical development of NOx can be better simulated by coupled
system More complex observation operator needed, based on simple
photochemical equilibrium between NO2 and NO JNO2 (photolysis freq)
depends on: SZA, surf. albedo, O3 concentration,slant O3 column,
temperature, clouds JNO2 parameterization based on TM5 routine k(T)
rate coefficient ofO3 +NO -> NO2 + O2 Does it work? Using the
NOx/NO2 observation operator ensures the NO2 fields are drawn
towards the satellite data. Benefit of chemical coupling
Background NOx levels determine O3 production/loss Assimilation of
NO2 has an impact on ozone field(through chemical feedbacks in the
CTM) Assimilation of NO2 can improve O3 field Validation with
MOZAIC ozone data CO & NO2 assimilation Control (no CO or NO2
assimilation) NO2 assimilation Observations Assimilating Retrievals
Effect of a priori on retrieval product
MOPITT CO shows changes from one version to another. Part of these
changes are caused by changing prior information. V4 V4-V3 Jan 2003
V3 How to deal with retrievals
Take profile retrieval xr as measurement y: With a-priori xa ,
error covariance matrix Sr and averaging kernel A: If we
assimilatexr with covariance Sr , we mix in both the a priori
profile and the a priori covariance matrix, which is likely to be
inconsistent with the model background of the assimilation system.
Sy: observation error covariance matrix Sa: prior error covariance
matrix K: weighting function Using the Averaging Kernel
We can make use of the averaging kernel A in the observation
operator by using the following: We remove the influence of the a
priori profile if we use the averaging kernel to sample the model
profile according to the assumptions made in the retrieval.
However, the a priori error assumptions are still in there and we
assume everything is linear within the bounds of these a priori
assumptions. (And we still need to know xa and A in the observation
operator calculations). Example MOPITT CO Averaging Kernels
day night From: Deeter et al. (2003) JGR Diurnal variations of
Tsurf affect retrieval over land. CO near surface more detectable
during day, AKs shift downwards Diurnal variability of AKs largest
over e.g. deserts, smallest over sea If AKs are not used this can
introduce an artificial diurnal CO cycle in analysis Issues Total
column retrievals come with integrated averaging kernels; some
information is lost Profile retrievals with full averaging kernels
and retrieval errors become easily difficult to handle Not all
retrieval methods allow the estimation of an averaging kernel;
e.g., neural networks Not all data providers use the same
definition of averaging kernel in their data files Many different
versions of the observation operator needed to deal with all
variations IASI & MOPITT combined IASI: LATMOS/ULB MOPITT: NASA
Reanalysis
Model run MOPITT IASI Aerosol complications Aerosols, whats the
problem?
Aerosol assimilation is difficult because: There are numerous
unknowns (depending on the aerosol model) and very little
observations to constrain them. The concentrations vary hugely,
with for instance strong plumes of desert dust in areas with very
little background aerosol. This makes it difficult to model the
background error covariances properly 4D-Var for aerosols Aerosol
prognostic variables include 3 bins for desert dust, 3 bins for
sea-salt, hydrophobic and hydrophilic organic matter, hydrophobic
and hydrophilic black carbon, and sulphate. The control variable is
formulated in terms of the total aerosol mixing ratio Assimilated
observations:MODIS Aerosol Optical Depths (AODs) at 550 nm over
land and ocean Observation operator converts total aerosol mixing
ratio into AOD The observed AODs are spread out over the various
aerosol types and bins based on the modelled ratios Aerosol optical
depth for desert dust: monthly
Sydney dust storm, H+72 H+48 Aerosol optical depth for desert dust:
monthly average for September 2008 H+24 Example for wrong aerosol
attribution
Eruption of the Nabro volcano put a lot of fine ash into the
stratosphere. This was observed by AERONET stations and the MODIS
instrument. sulphate biomass ICIPE-Mbita - AERONET dust sea salt
The MACC aerosol model does not contain stratospheric aerosol yet,
so the observed AOD was wrongly attributed to the available aerosol
types. MACC AOD analysis AERONET total AOD AERONET fine mode AOD
Interaction with NWP Examples of connection between NWP and
composition
Meteorological and atmospheric composition data assimilation are
closely coupled. Both can benefit from each other. Aerosol direct
and indirect effects Trace gas climatologies in radiation models
Trace gases in radiance assimilation Reduction of AIRS/IASI bias
correction with realistic CO2
Bias correction using fixed CO2 of 377 ppm, the value prescribed in
RTTOV Bias correction using variable CO2 modelled with MACC system
Mean bias correction (K) for August 2009 for AIRS channel 175
(699.7 cm-1; maximum temperature sensitivity at ~ 200 hPa) Engelen
and Bauer (2011) Positive effects on temperature analysis
Using more realistic CO2 values in radiative transfer model causes
changes in temperature analysis. Bias correction of AMSU-A channels
is reduced as well. Thank you. Ozone hole example Ozone Satellite
Retrievals
OMI TC SCIA TC SBUV TC MLS 1 August 2008, 0 12 UTC UV-VIS: SBUV,
SCIAMACHY, OMI Total Columns at high resolution No observation in
polar night Micro Wave Limb sounder (MLS) Profiles in Stratosphere
Instrument - Biases over Antarctica
MLS observes in polar night Large area-averaged differences due to
different sampling Actual biases are small (2-3%) Ozone hole
predictions with without assimilation
Free Running Model Initialized every 15 days MOZ Stratospheric
Chemistry scheme IFS Linear Chemistry Scheme (Cariolle) TM
Climatology ANA Analysis Ozone hole assimilation with different
chemistry schemes
MOZ Stratospheric Chemistry scheme IFS Linear Chemistry Scheme
(Cariolle) TM Climatology MOZ-NRTAssimilation, but withoutMLS