Validation of a 3D-Fgat assimilation of MIPAS ozone profiles in a global
chemistry and transport model
Sébastien Massart1,*, Hélène Manzoni2, Daniel Cariolle1,2, Vincent-Henri Peuch2, Andréa Piacentini1
1Centre
Européen de Recherche et Formation Avancée en Calcul Scientifique
Météo-France et CNRS
* Corresponding author
CERFACS
42, Avenue Gaspard Coriolis.
31057 Toulouse Cedex 01. France.
massart@cerfacs.fr
2CNRM-GAME,
Due to its environmental importance ozone is one of the most observed and modelled trace species of the
atmosphere. Its concentration is now often a prognostic variable in global numerical weather prediction and
general circulation models and its comprehensive chemistry is introduced in chemistry and transport
models. Data assimilation is a very efficient way to combine the information provided by various
measurements (satellite, airborne, in-situ) and the numerical models, with applications such as providing
initial conditions for forecasts or fields reanalyse and help in the validation of models. This paper presents
a variational assimilation of ozone profiles from MIPAS space-based remote sensing instrument into the
CTM MOCAGE. Specific diagnostics are presented in order to estimate the quality of the assimilation
system and to evaluate biases and shortcomings of the CTM. In particular, the comparison of the analysed
ozone fields with three independent datasets shows that the present assimilation system produces very
consistent ozone fields.
1. Introduction
Because of its role in the radiation budget,
as well as its contribution to tropospheric pollution
and to the greenhouse effect, ozone is one of the
most important trace species in the atmosphere.
Many global numerical weather prediction models
and general circulation models (GCMs) include
simplified ozone photochemistry coupled with
radiation and dynamics, while 3D chemistry and
transport models (CTMs) have been developed to
accurately represent the chemical evolution of
ozone and other related trace species. In the
context of the availability of large datasets from an
increasing number of space-based remote sensing
instruments, data assimilation is one of the most
appropriate ways to combine the information
provided by numerical models and by observations
(Fisher and Lary 1995). In recent studies ozone
assimilation have been performed using 2D
transport models (Eskes et al., 1999), GCMs
(Struthers et al., 2002) and 3D CTMs (Hanea et
al., 2004). Depending on the model accuracy, the
chemistry scheme involved and the nature of the
observations to be analysed, the assimilation is
performed in the troposphere (Elbern and Schmidt,
2001; Lamarque et al., 2002), in the Upper
Troposphere and Lower Stratosphere (UTLS;
Cathala et al., 2003) or in the stratosphere (Errera
and Fonteyn, 2001; Fierli et al., 2002). The
analysis can be obtained using different
techniques such as Kalman filter (Hanea et al.,
2004) or variational systems (Eskes et al., 2003).
In Massart et al. (2005), we have
assimilated GOME (Global Ozone Monitoring
Experiment) ozone profiles from the KNMI
retrievals with the CTM MOCAGE using a 3D-First
Guess at Appropriate Time (3D-Fgat) method
(Fisher and Andersson, 2001). In the present
paper we show that our assimilation system can be
equally successful in assimilating the ozone
profiles from the Michelson Interferometer for
Passive Atmospheric Sounding (MIPAS).
The various parts of the assimilation
system, namely the forecast model, the
characterization
of
the
background
error
correlation, and the assimilated observations are
described in section 2. The description of the
assimilation experience, the diagnostics as well as
the comparison of the analysed ozone fields with
the assimilated data are given in section 3. In
section 4 the analysed fields are compared to
several independent datasets. Discussion of the
results and plans for future work are given in
section 5. Some of the limitations and the possible
improvements of our system are highlighted in the
conclusion.
2. Assimilation system
The technique used for the assimilation of
ozone profiles is a 3D-Fgat method producing
analysis of the global ozone content of the
atmosphere. The main components of this system
are described below.
2.1.
Forecast model
The numerical model used to provide 3D
forecasts of atmospheric chemistry fields is the
chemistry transport model MOCAGE developed by
Météo-France. This model disposes of several
configurations with varying horizontal and vertical
geometries, and various chemical and physical
parameterization packages. The choice of the
configuration must be adapted to the model
application with recent examples in chemical
weather forecasting (Dufour et al. 2004) and data
assimilation (Cathala et al. 2003; Pradier et al.
2005; Massart et al. 2005).
In the present study, MOCAGE is used in
a configuration covering the globe with a
2°x2horizontal resolution. It has 47 hybrid (,P)
vertical levels from the surface up to 5 hPa, with a
resolution of approximately 800 m in the UTLS.
Meteorological forcing fields (horizontal winds,
temperature, humidity and surface pressure) are
Météo-France operational analyses (ARPEGE
numerical weather prediction model, 4D-var
assimilation suite). Various chemical schemes are
available within MOCAGE. Among all of them the
linear parameterization by Cariolle and Déqué
(1986) is used in its latest version (v2.1, Cariolle
personal communication 2005). It is well adapted
to evaluate the production and loss rates of
stratospheric and upper tropospheric ozone, and it
needs for less computing time than more
comprehensive chemical schemes.
Agency, 2000a, 2000b). With around 600 profiles
per day the MIPAS data has a relatively good
global coverage. The main data product obtained
with MIPAS includes inverted vertical profiles of
atmospheric pressure, temperature and volume
mixing ratio profiles of the primary target species
O3, H2O, CH4, N2O, HNO3 and NO2 as well as
variance / covariance matrices for the retrieved
profile data.
For the purpose of intercomparison it was
agreed among the participants of the European
ASSET (Assimilation of Envisat data) project to
use the MIPAS ozone products from the version
4.61 delivered by the D-PAC (German Processing
and Archiving Center) of ESA. The observations
errors both include variance errors and vertical
correlation errors. This information was included
into the observation covariance matrix error. In the
present work, errors between profiles are assumed
to be uncorrelated.
To compare observations with the model
(observation operator), the model outputs are
mapped on the observation grid (same longitude,
latitude and pressure levels) using a bi-cubic
interpolation in all three directions, consistent with
the scheme employed in the semi-lagrangian
advection scheme of the model.
2.2.
Background
correlation
3. Results and discussion
and
its
error
In our method the background of the
ozone state is obtained by the integration of the
model from the ozone field analysed during the
previous step. The relative distance between
background and observations should slightly
depend on the pressure level. However, in a first
approximation, we have simply chosen to take the
background-error standard deviations proportional
to the background ozone field. The unknown
background-error covariance matrix is fundamental
for determining how observational information is
spread spatially. It is accounted for using a
correlation model based on a generalized diffusion
operator (Weaver and Courtier 2001) with a lengthscale of 4. Due to our specific implementation of
the operator, the observations beyond 80 latitude
south and north are not assimilated.
2.3.
MIPAS observations
The Michelson Interferometer for Passive
Atmospheric Sounding (MIPAS) instrument was
launched as part of Europe's environmental
monitoring satellite, ENVISAT, in 2002. This
Fourier transform spectrometer provides a number
of geophysical parameters relevant for the study of
atmospheric
chemistry,
climatology
and
tropospheric/stratospheric exchange processes,
measuring high-resolution gaseous emission
spectra at the Earth's limb (Wargan et al. 2005). A
typical complete limb sequence consists of 17
spectra with tangent points from 68 km downward
to 6 km in 3 km to 8 km steps (European Space
3.1.
Overview
Within the European project ASSET, a test
period was chosen to compare the MIPAS data
assimilations obtained using different models with
different types of assimilation algorithms. The test
period spans from July 1st to November 30th 2003,
a period marked by important ozone depletion over
Antarctica. This study reports ozone analysis
performed with our assimilation system over this
period.
In order to start the assimilation with an
ozone field well balanced with the atmospheric
dynamics, a one-month free run of the forecast
model from the model climatology for the month of
June has been performed to provide the ozone
initial conditions for July 1st 2003. As a control
simulation, this forecast run was extended up to
the end of the assimilating period. Comparison
between ozone fields from the control simulation
and the assimilation allows a straightforward
evaluation of the impact of the assimilation.
The choice of the 3D-FGAT assimilation
technique limits the size of the assimilation
window, since it has to be short enough compared
to chemistry and transport timescales. For this
reason we have limited the assimilation window to
3 hours.
The assimilation algorithm was built using
the PALM software (www.cerfacs.fr/~palm). Since
this algorithm can be viewed as a sequence of
elementary operations, or elementary components
that exchange data (Lagarde et al. 2001), PALM
manages the dynamic launching of the coupled
components (forecast model, algebra operators,
I/O of observational data, ...) and the parallel data
exchanges.
3.2.
analyses, an activation of the ozone destruction for
zenith angles larger than 87°, and the fact that
ozone profiles are not assimilated beyond 80°, all
contribute to the ozone under evaluation at polar
latitudes.
Diagnostics
Several
diagnostics
are
routinely
implemented to validate and evaluate the data
assimilation results. In particular, the temporal
sequence of increment vectors (analysis minus
background) is used to check the optimality of the
assimilation scheme by checking that the bias
(temporal mean of the increments) is close to zero.
Time average of the zonal mean increments (figure
1), can reveal systematic biases of the forecast
model. Our results show that the increments are
close to zero all over the domain except for two
regions. The first one is located near the equator
and the second one at high latitudes in the
southern hemisphere where the increment values
reach about 2.10–2 ppmv near 25 hPa (at this
pressure level the average ozone concentration is
around 5 ppmv). The positive values indicate that
the background always underestimate the ozone
content in these two regions.
Figure 2: Ozonesonde measurements (diamonds joined
by dotted line) and analysis (solid line) at 30 hPa over
the South Pole.
In addition to the simple diagnostics
discussed above, other global consistency checks
must be performed. For instance, Talagrand
(1998) has developed a very simple diagnostic
based on the statistical expectation of the objective
function J taken at its minimum xa
E[J(xa)] = p/2 ,
where p is the number of assimilated observations.
This diagnostic was implemented in our system
and the ratio 2J(xa)/p, which should statistically be
equal to 1, fluctuates around an average of 1.09.
This means that a reasonable consistency of the
assimilation algorithm has been reached.
3.3.
Comparison with the assimilated
data
Figure 1: Mean zonal increment (analysis minus
background) in 10-3 ppmv as a function of pressure.
Solid line contours represent positive values. Dotted line
contours represent negative values.
At the equator the chemical ozone lifetime
is rather long in this altitude range, of the order of
100 days. Thus the ozone underestimation must
reflect deficiencies in the ozone transport. It is the
likely consequence of an overestimation of the
vertical wind velocities that bring ozone-poor air
masses from the upper troposphere into the lower
stratosphere.
At the South Pole, the bias probably
comes from the modelled ozone destruction which
appears too early in the spring season when the
light comes back. Figure 2 shows an ozone
depletion starting from the end of August while it is
measured by ozonesondes only towards midSeptember. Ozone destruction due to PSCs
chemistry is activated within the model when the
temperature drops below 195K in sunlight
conditions. A cold bias in the meteorological
A key step in the validation of the
assimilation set-up is the comparison of analyses
with the original data that has been assimilated.
(a) bias
(b) std. dev.
.
Figure 3: (a) bias and (b) standard deviation between
MIPAS observations and ozone analyses (solid line) and
between MIPAS observations and ozone from the
control run (dashed line). Averaged MIPAS standard
deviation is added as dotted line in the right panel.
The ozone fields from the analysis and the
control run are interpolated to MIPAS observation
points to compute the observations minus analysis
(OMA) and the observation minus control (OMC)
diagnostics. Figure 3 shows the bias and the
standard deviation of the OMA and OMC. In
agreement with the positive average increment
seen in the previous section, the ozone fields from
the control run are underestimated compared to
the observations (positive bias of the OMC). Due
to the effectiveness of the assimilation the bias of
the OMA is reduced to a very small value and in
average the difference between analysis and
observations is close to zero. If we assume that
observations are unbiased, it means that in
average the analysis approaches very well the true
state.
One important objective of the assimilation
is to minimize the standard deviation of the error
between the analysis and the true state. This is
difficult to verify since the true state is unknown.
Nevertheless, the fact that under 20 hPa the
standard deviation between analysis and
observations is lower than the MIPAS standard
deviation (figure 3) indicates a good agreement
between them up to 20 hPa.
Above 20 hPa, the remaining model levels
are influenced by the model climatology and
boundary conditions. Less quality is expected in
this altitude range and this explains the increase of
the standard deviation.
4. Comparison with independent data
According to Talagrand (1998), the
comparison of the assimilated fields with
independent data is the only objective way to
assess the quality of an assimilation algorithm. The
analysed ozone profiles have been compared to
three
sets
of
independent
observations:
ozonesondes, HALOE (Halogen Occultation
Experiment) and TOMS (Total Ozone Mapping
Spectrometer) instruments onboard satellites.
HALOE gives vertical ozone profiles similar to
those of MIPAS. However, HALOE supplies a
smaller number of daily profiles, but with a slightly
more accurate vertical resolution. In contrast to
satellites, the ozonesondes provide vertical profiles
with very high resolution but with the very limited
coverage of the ground-based stations. They give
reliable ozone profiles in the troposphere and the
lower stratosphere up to 10 hPa. TOMS measures
total ozone columns during daytime, so the
coverage is limited at high latitudes in winter-spring
conditions. The comparisons between analysis and
observations are described below. To this end the
OMA and the OMC diagnostics for each
observational set are computed.
4.1.
HALOE
The Halogen Occultation Experiment
(HALOE) (Russell et al., 1993) uses solar
occultation to measure vertical profiles of O 3, HCl,
HF, CH4, H2O, NO, NO2, aerosols, and
temperature versus pressure with about 15
observations per day. The altitude range of the
measurements extends from about 15 km to 60130 km, depending on the species, with a 1.6 km
instantaneous field of view at the Earth's limb.
(a) global bias
(b) bias –60 to –30
Figure 4: (a) global bias and (b) bias in the latitude band
60S to 30S between respectively 2262 and 523
HALOE observations and the ozone analyses (solid
line), and between HALOE observations and the control
run (dashed line).
Figure 4 shows the global bias and the
bias in the latitude band 60S to 30S between
HALOE observations (available from the HALOE
web site: http://haloedata.larc.nasa.gov) and ozone
values from the analyses or from the control run.
The assimilation has slightly reduced the global
bias. This reduction is latitude dependant. The
assimilation has significantly reduced the bias in
the latitude band from 60S to 30S where the
initial bias of the control run was more important
compared to other regions. In this latitude band the
bias between HALOE observations and the
analyses is mainly located in the lower
stratosphere above 100 hPa, a region where the
improvement is the most significant. Since there is
very little bias between the analyses and MIPAS
observations (figure 3), the bias observed here
comes from a bias between the two instruments,
MIPAS overestimating ozone compared to
HALOE.
The assimilation globally improves the
standard deviation (figure 5a) between HALOE
and simulated ozone fields, but its values remain
larger than the averaged HALOE standard
deviation. Compared to the reference simulation,
the improvement occurs above 200 hPa with a
peak value near 20 hPa. The largest standard
deviation is located near the South Pole in the
latitude band from 90S to 60S. In this region, the
assimilation does not improve the ozone fields
compared to HALOE. Further analysis should be
performed to assess the possible causes of the
discrepancy between analysis, HALOE and MIPAS
data. It might be due to the systematic bias of the
CTM (figure 1) and equally to the presence of high
altitude clouds that makes the satellite retrievals
difficult.
(a) global Std. Dev.
(b) std. dev. –90 to –60
Figure 5: (a) global standard deviation and (b) standard
deviation in the latitude band 90S to 60S between
respectively 2262 and 237 HALOE observations and the
ozone analyses (solid line) and between HALOE
observations and the control run (dashed line). Averaged
HALOE standard deviation is added as dotted line in the
left panel.
Figure 6 shows the global bias and the
bias in the latitude band 30S to 30N between
observations and ozone profiles from the analysis
or the control run. From the surface up to about
150 hPa, the bias of the OMC is close to zero and
the assimilation does not change those statistics.
Above this pressure level and up to 80 hPa the
ozone underestimation of the control run compared
to ozonesondes is improved by the assimilation.
The ozone values from the analyses are larger
than the soundings in the pressure range 150 hPa
to 15 hPa, with a maximum located near 30 hPa.
This bias maximizes in tropical latitudes. In the
latitude band from 30S to 30N, the assimilation
seems to increase the difference with the
ozonesondes compared to the control run. This
might come from the discrepancies between the
forecast model and MIPAS data in this region
around 30 hPa (figure 1) which are not solved by
the assimilation, but it might equally be caused by
systematic
differences
between
MIPAS
occultations in the IR solar spectrum and
soundings using chemical techniques.
.
(a) global std. dev.
4.2.
(b) Std. Dev. –30 to –30
Sondes
The second set of independent data used
to validate the analysis is given by ozonesondes.
Profiles produced by the various sondes come
from the World Ozone and Ultraviolet Radiation
Data Centre (WOUDC, http://woudc.org), Southern
Hemisphere Additional Ozonesondes project
(SHADOZ,
http://croc.gsfc.nasa.gov/shadoz/,
Thompson et al., 2003a, b) and the Network for the
Detection of Stratospheric Change (NDSC,
http://www.ndsc.ncep.noaa.gov/).
During
their
ascent, sondes measure the ozone concentration
from the surface up to around 10 hPa level. About
900 sondes are involved in the comparison with
the analyses, and half of the ozonesonde profiles
are located in the South Pole region (25%) and in
the equatorial region (25%).
(a) global bias
(b) bias –30 to 30
Figure 7: (a) global standard deviation and (b) standard
deviation in the latitude band 30S to 30N between
respectively 880 and 230 sondes observations and
ozone analyses (solid line) and between sondes and
control run (dashed line). Averaged sondes standard
deviation is added as dotted line in the left panel.
The global standard deviation (figure 7)
compares well with the statistics from the sondes
up to 200 hPa. At higher levels, the difference
increases both for the OMA and the OMC with, as
expected, smaller values for the analysis.
Nevertheless, the standard deviation is three times
larger than the standard deviation from
measurements. The highest values are again
observed at low latitudes (figure 7), the
assimilation giving much less improvement than at
other latitudes.
4.3.
Figure 6: (a) global bias and (b) bias in the latitude band
30S to 30N between respectively 880 and 230 sonde
observations and analysed ozone profiles (solid line) and
between sondes and control run (dashed line).
TOMS
The Total Ozone Mapping Spectrometer
(TOMS) onboard several satellites, provides a
complete data set of daily total ozone columns
since November 1978. Data is available on the
web site http://toms.gsfc.nasa.gov/ and is widely
used as a reference to test chemical models. The
comparison described beyond is based on the
TOMS data from the version 8 algorithm. The
absolute error of the instrument is of the order of
3%, except at high latitudes for high zenith angles
where it can suffer from calibration errors. By 50°
latitude, an error of the order of -2% to -4% is
reported, which is slightly larger in the northern
hemisphere than in the southern hemisphere.
The total ozone columns before and after
the assimilation step are computed, so the ozone
fields from the TOMS, the assimilation and the
control simulation can be compared directly.
In the Tropics the correlation coefficients
decrease and vary from 0.3 to 0.6. At the equator
the analysis and the TOMS data seem to be
completely uncorrelated (correlation between 0
and 0.1). The high cloud cover in the equatorial
region that introduces large correction in the
TOMS measurements might explain this low
correlation coefficient. The fact that meteorological
analyses do not reflect very well the vertical
motions associated with convective activity might
equally play a role in the low correlation between
modelled
ozone
columns
and
TOMS
measurements.
Figure 8: Correlation between total ozone from TOMS
measurements and total ozone from the control run.
Figure 10: Bias of analysisTOMS total column ozone
(solid line) and of control runTOMS total column ozone
(dashed line).
Figure 9: Correlation between total ozone from TOMS
measurements and total ozone from the analysis.
The
correlation
between
TOMS
measurements and total ozone columns from the
control run and the analyses over the whole
assimilation period are presented on figures 8 and
9 respectively. The assimilation process globally
improves the representation of the ozone
structures as seen by the TOMS instrument. The
analyses and the satellite independent data are
relatively well correlated at middle and high
latitudes within both hemispheres (between 0.7
and 0.9). As expected, the control simulation and
the TOMS data show less correlation in these
regions (between 0.3 and 0.5 in the northern
hemisphere, between 0.5 and 0.9 in the south).
Figure 11: Standard deviation of analysisTOMS total
column ozone (solid line) and of control runTOMS total
column ozone (dashed line).
Figure 10 shows the bias between the
TOMS data and the total ozone column from the
control run and the analysis. In both cases, the
biases are very dependent upon latitude. The
minimum deviation between the assimilation and
TOMS is located near the equator. It increases
with latitude to reach values around 40 Dobson
Units (DU) at the South Pole, and 50 DU at the
North Pole. The bias can be partly explained by
the way total ozone is computed within the
MOCAGE model above 10 hPa. In the uppermost
model level the ozone concentration is relaxed
towards climatology and the ozone column above
is computed from extrapolation of the mixing ratio.
This introduces a possible bias of about 5 DU in
the total column evaluation.
In addition, the simplified ozone scheme
used by the model is adequate for the
stratosphere, but it is not aimed to well describe
the troposphere. In particular the present version
of the scheme tends to overestimate the low
latitude ozone content in the troposphere. As a
consequence, the standard deviation (figure 11) is
more meaningful than the bias. Compared to the
control run the assimilation improves significantly
the standard deviation that remains lower than 10 15 DU at all latitudes, except in the 50-70 ° latitude
band in the SH where it reaches 20 DU.
5. Conclusions and future work
A MIPAS ozone profiles assimilation
system based on a 3D-Fgat technique and the
chemistry and transport model MOCAGE has been
evaluated. The ozone vertical profiles have been
assimilated from the beginning of July to the end of
November 2003. Statistical diagnostics show a
good consistency of the assimilation algorithm, and
reveals systematic biases of the CTM. Those
biases can be related to deficiencies in the
parametrization of ozone destruction by PSCs
chemistry in the SH polar vortex and to
weaknesses in the equatorial analyses of the
vertical velocities in the lower stratosphere.
(a) global bias
(b) global std. dev.
analysis, we conclude that MIPAS data are larger
by 5-10% than HALOE and sonde measurements
in the lower stratosphere between 100 and 20 hPa.
At this stage we do not know if these differences
are seasonally dependant and we cannot conclude
on the absolute accuracy of the various
measurements.
This aspect has to be further studied. In
particular a large part of the deviation between
MIPAS and the other instruments occur at low
latitudes in region with the presence of high clouds
that can introduce contamination in the retrievals.
Future work should be focussed on the impact of
the choices made for screening the cloudy profiles
on the ozone assimilation.
Chemical data assimilation suites are often
settled for a specific environment that is often
strongly model dependent. Flexibility in the applied
methodologies is however required if ones want to
deal with the assimilation of minor trace species
with different chemical lifetimes and sources. In
this paper we have addressed the problem of
assimilation of a single species, but in the future
we should use multivariate assimilation schemes
than can cope with many species measured from
several instruments having very different
characteristics (limb scanning, in-situ, nadir
viewing, ...). All those points have advocated for
the use of the flexible coupler PALM to build our
assimilation system. The PALM software treats the
model, the observations and the algebra in a
generic form and is very well adapted to implement
various assimilation techniques, observation and
model type. It can also facilitate the integration of
specific developments made by various specialists
in assimilation techniques, chemical models and
observations. We shall take advantage of the
flexibility of our system to implement in the near
future more comprehensive chemical schemes, a
choice of several meteorological analyses for
driving the CTM, the assimilation of other trace
species than ozone, other observational datasets,
and an incremental 4D-Var.
6. References
Figure 12: (a) global bias and (b) global standard
deviation in percent of the observation between HALOE
observations and the analysis (solid line) and between
ozonesondes measurements and the analysis (dashed
line).
Over the period studied, the ozone
assimilation consistently improves the model
results in comparison with three independent
observed datasets (ozonesondes, HALOE and
TOMS instruments). Compared to ozonesondes
and HALOE (figure 12) the ozone bias decreases
from -10 % at 100 hPa to values close to zero near
the top of the model (10 hPa). Since there is a
smaller bias between MIPAS observation and the
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