THE USE OF IASI AND GOME-2 ATMOSPHERIC COMPOSITION DATA IN THE MACC-II DATA ASSIMILATION SYSTEM

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1 THE USE OF IASI AND GOME-2 ATMOSPHERIC COMPOSITION DATA IN THE MACC-II DATA ASSIMILATION SYSTEM Antje Inness, Johannes Flemming, Sebastien Massart, Martin Suttie, and Marijana Crepulja ECMWF, Shinfield Park, Reading, UK Abstract MACC-II (Monitoring Atmospheric Composition and Climate) is the current pre-operational atmospheric service of the European Copernicus/ GMES programme funded by FP7. The service combines a state-of-the art transport and chemistry model with satellite data from various sensors to provide consistent analyses of 3-dimensional fields of atmospheric composition including ozone, carbon monoxide, nitrogen oxides, methane, and aerosols. The MACC-II system is run routinely and provides daily 5-day forecasts of atmospheric composition data. The MACC-II delayed mode system monitors greenhouse gases and aerosols six months behind real time, and MACC also provided a 10- year reanalysis of atmospheric composition covering the years More details about MACC- II can be found on The use of data from several EUMETSAT and ESA satellites is being explored within MACC-II. In this paper we present results from the assimilation of IASI CO, GOME-2 O 3 and SO 2, as well as SCIAMACHY and IASI CH 4 data. Some of these data, for example IASI CO, are already used routinely in the NRT MACC-II data assimilation system, while the use of others, e.g. GOME-2 SO 2, are being tested and has shown encouraging first results. MACC NEAR-REAL TIME, DELAYED MODE AND RETROSPECTIVE DATA PROVISION MACC (Monitoring Atmospheric Composition and Climate) and MACC-II (Monitoring Atmospheric Composition and Climate Interim Inplementation) (from now on both are referred to as MACC) are research projects with the aim of establishing the core global and regional atmospheric environmental services for the European Copernicus/ GMES (Global Monitoring for Environment and Security) initiative. MACC combines state-of-the-art atmospheric modelling with Earth observation data to provide information services covering European Air Quality, Global Atmospheric Composition, Climate, and UV and Solar Energy. The global model and data assimilation system used in MACC is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). MACC provides daily near-real time (NRT) services and retrospective services. The global MACC system is run daily to produce analyses and 5-day global forecasts of reactive gases and aerosols. These global fields serve as boundary conditions for an ensemble of European air quality models that provide higher resolution air quality forecats. MACC also provides fire emission data with the Global Fire Assimilation System (GFAS) (Kaiser et al. 2012). The retrospective services include data records of atmospheric composition for recent years, including ozone records and a reanalysis of atmospheric composition data covering the years (Inness et al. 2013). In this reanalysis satellite retrievals of reactive gases, greenhouse gases and aerosol optical depth were assimilated into the IFS to provide global analysis fields at a resolution of about 80 km for the troposphere and the stratosphere. Furthermore, the MACC system is run in delayed-mode six months behind NRT to provide analyses of CH 4, CO 2 and aerosols. The six months lag is nedded in order to produce the assimilated data, because the production process depends on auxiliary data not available in near real time.

2 EUMETSAT DATA USED IN THE MACC REANALYSIS In the MACC reanalysis reactive gases retrievals from various instruments including several from EUMETSAT platforms were assimilated for the period ( Table 1). An overview of the quality of the resulting analysis fields can be found in Inness et al. (2013). More detailed validation reports are available from Sensor Satellite Provider Version Type Reference GOME ERS-2 RAL O 3 PROF Siddans et al MIPAS ENVISAT ESA O 3 PROF Carli et al MLS AURA NASA V02 O 3 PROF Waters et al OMI AURA NASA V003 O 3 TC Bhartia et al. 2002; Levelt et al SBUV/2 NOAA-16, 17, 18,19 NOAA V8 O 3 PC Bhartia et al SCIAMACHY ENVISAT KNMI O 3 TC Eskes et al IASI METOP-A LATMOS/ULB CO TC George et al. 2009, Clerbaux et al MOPITT TERRA NCAR V4 CO TC Deeter et al SCIAMACHY ENVISAT KNMI V1.04. V1.1 NO 2 TRC Boersma et al., Wang et al Table 1: Reactive gas retrievals used in the MACC reanalysis. Data from EUMETSAT or ESA platforms are marked in bold. PROF denotes profile data, TC total columns, TRC tropospheric columns, PC partial columns. Figure 1: Time series ( ) of monthly mean CO concentrations (ppbv) from the MACC reanalysis (red), a control run (blue), and from NOAA/GMD ground-based measurements (black) over Mace-Head (top left), Key Biscayne (top right), Tenerif (bottom left), and South Pole (bottom right) stations.

3 Figure 1 shows a comparison of CO concentrations from the reanalysis with NOAA/GMD groundbased measurements (Novelli et al., 2010) over Mace-Head, Tenerife, Key Biscayne and South Pole stations for the period Also shown are results from a control run without the assimilation of reactive gases retrievals, MOPITT and IASI. The figure shows that the magnitude and seasonal variability of surface CO is generally well captured by the reanalysis over most stations and improved compared to the control run, apart at South Pole from After the assimilation of IASI CO data began in April 2008, the analysis agrees better with the observations over South Pole. EUMETSAT DATA USED IN THE MACC NRT ANALYSIS The MACC NRT system is run daily to provide analyses and 5-day forecasts of reactive gases and aerosols. Table 2 lists the reactive gases retrievals assimilated or monitored in the MACC NRT analysis. Sensor Satellite Provider Version Type Reference GOME-2 Metop-A EUMETSAT O 3 TC, NO 2 Trop Col, SO 2, HCHO Loyola et al IASI METOP-A, METOP-B LATMOS/ULB CO TC George et al. 2009, Clerbaux et al MLS AURA NASA V3.4 O 3 PROF Livesey et al MOPITT TERRA NCAR V4/ V5 CO TC Deeter et al OMI AURA NASA V883 O 3 TC, NO 2 Trop Col Bhartia et al. 2002; Levelt et al SBUV/2 NOAA- 16,17,18,19 NOAA V8 O 3 PC Bhartia et al SCIAMACHY ENVISAT KNMI O 3 TC Eskes et al SCIAMACHY ENVISAT KNMI V1.04/ V1.1 NO 2 TRC Boersma et al., et al Wang Table 2: Reactive gas retrievals used in the MACC NRT analysis. Data from EUMETSAT or ESA platforms are marked in bold. PROF denotes profile data, TC total columns, TRC tropospheric columns, PC partial columns. Note that SCIAMACHY data were only used before April IASI CO DATA FROM METOP-B IASI CO data from METOP-B (produced by LATMOS/ULB) have been monitored (but not assimilated) in the MACC NRT analysis since the end of July The data show good agreement with the METOP-A IASI CO retrievals that are assimilated in the NRT MACC analysis. Figure 2 shows that the departures and the standard deviation of the departures from both IASI instruments are very similar. The standard deviations of the analysis departures are reduced compared to the first-guess departures for both instruments. This illustrates that the fit to IASI-B data is improved by assimilating IASI-A data.

4 Figure 2: Top panel: Timeseries of first-guess and analysis departures from IASI-A (blue and red) and IASI-B (black and magenta) CO columns (in molec/cm 2 ) averaged over the area between 20-60N. Bottom panel: Standard deviations of the departures. FROM MONITORING TO ASSIMILATION Before a new satellite retrieval product is assimilated in the MACC system the data are monitored passively. This means that the data are included in the MACC system and first-guess and analysis departures of the data are calculated, but that the data are not used actively in the analysis. This procedure allows us to assess the quality of the data and to establish if there are biases between the data and the model or between different instruments. If the data quality looks good initial assimilation tests are performed. If these are successful the routine assimilation of the data set in the MACC NRT analysis can begin. Figure 3 shows this progression from monitoring to assimilation for GOME-2 TCO3 data from Metop-A (produced by DLR). The left panels show timeseries of departures and number of observations from the MACC NRT analysis which included the GOME-2 TCO3 data passively at that time. The right panels show the same fields for a MACC test experiment where the data were actively assimilated. The top left plot shows a change to larger departures in early January This was the result of a change in the assimilated MLS data from V2 to V3.4). Another discontinuity can be seen in July 2013 when Metop-A changed to half width swath mode and the GOME-2 retrieval to version GDP4.7. At the same time the number of observations is reduced. This reduction is a result of the pre-thinning that is applied to the data in IFS and thins the data to 0.5x0.5 degrees. Because the data in half width swath mode are closer together than before, more data are now removed by the thinning. The right panel shows that when GOME-2 ozone data are assimilated the variational bias correction (black curve) absorbs those changes and that the first-guess and analysis departures are stable as the data are assimilated successfully. After the version change in July 2013 the magnitude of the bias correction is reduced because the data now agree better with the

5 analysis. It is now similar to the one applied to OMI TCO3 data (not shown). The assimilation tests with the GOME-2 TCO3 data were successful and the data have been actively assimilated in the NRT MACC system since 7 October Figure 3: The top plot shows the timeseries of first-guess and analysis departures from GOME-2 total column ozone first-guess (red) and analysis departures (blue) from the MACC NRT analysis (left) which included the data passively and the MACC NRT test suite (right) in which the data were assimilated. The bottom panels show the number of observations. USE OF GOME-2 DATA FOR SO 2 PLUME FORECASTS FOR THE 2011 GRÍMSVÖTN AND 2010 EYJAFJALLAJÖKULL ERUPTIONS A method has been developed to estimate injection height and emission rate of volcanic SO 2 using UV-VIS satellite observations of SO 2 and to assimilate SO 2 retrievals into the MACC system (Flemming and Inness, 2013) and applied to GOME-2 SO 2 retrievals from Metop-A. The method is based on the comparison of test tracers injected at different heights and observed SO 2 total columns. It can estimate the emission parameters for a period of 24 hours before the time of the observations. These parameters can then be used in subsequent forecasts. Initial conditions for SO 2 forecasts can also be obtained by assimilating SO 2 observations into the MACC system. The estimated injection heights are used by the SO 2 assimilation system to place the plume at the assumed model level and there is the option to use the estimated emission rates in the subsequent forecasts. Figure 4: GOME-2 observations (left) and the 24h TCSO 2 forecasts using only initialisation with SO 2 analysis (INI), only the estimated emission parameters (EMI) and a combination of both (INIEMI) for 8 May (top) and 12 May (bottom) 2010 in DU.

6 The SO 2 analyses represent the plume location and the maximum values very well but have a tendency to exaggerate the extension of the plume. IFS forecasts without assimilation do not maintain the high maximum values recorded by the observations over long periods, possibly because of too large diffusion. Overall, the combination (INIEMI) of using emission data (EMI) and assimilation (INI) leads to the best forecasts. This is demonstrated in Figure 4, which shows 24 h forecast for two days during the 2010 eruption. The EMI forecast provide a good forecast close to the volcano. The INI forecasts provide a better forecast for the older plume. The INIEMI forecasts combine both properties. More information about this method can be found in Flemming and Inness (2013). CH4 MONITORING USING SCIAMACHY, IASI AND TANSO DATA Unlike the assimilated data in the MACC-II NRT analysis, CH 4 data are not available in near real time. A version of the global MACC-II assimilation system runs with a 6 months lag in a so-called "delayed mode" (DM) in order to wait for the availability of the data. The MACC-II DM system started producing a CH 4 analysis from 1 June 2009 onwards. This analysis was initially based on the CH 4 products from SCIAMACHY and from TANSO. The SCIAMACHY data were provided by the SRON Netherlands Institute for Space Research in collaboration with the Jet Propulsion Laboratory (JPL). TANSO (Thermal And Near-infrared Sensor for carbon Observation) is carried by the Greenhouse gases Observing Satellite (GOSAT) that was launched on 23 January The first TANSO data used in the DM system were provided by JAXA/NIES. But at this time, this product was not mature enough and its assimilation increased the bias and the variability of the analysis to a too large extent (Fig. 5). This product was blacklisted and no more assimilated. In the meantime, it is well known that SCIAMACHY NIR channels suffered from severe radiation damage, thereby affecting its performance. This became worse towards the end of the mission (April 2012). In January 2012 a new TANSO CH4 product started to be assimilated instead of the SCIAMACHY CH 4 product to tackle the SCIAMACHY issues. This product results from a joint development between SRON and the Karlsruhe Institute of Technology (KIT). It allowed to significantly reducing the bias and the error in the analysis (Fig. 5). In July 2012 the CH 4 IASI product from the Laboratoire de Météorologie Dynamique (LMD) was added in the analysis. The IASI data add interesting features in the tropical region where they are retrieved. They are much denser than the TANSO data and IASI provides measurements during day and night while TANSO provides only daytime measurements. Moreover IASI provides information on the middle atmosphere while the TANSO product is total columns that are sensitive to the lower troposphere. SUMMARY At present a wide range of data from EUMETSAT platforms are assimilated in the MACC-II system. Additional data (e.g. SO 2, NO 2 from GOME-2, O 3 profiles from IASI) are being monitored and tested in assimilation experiments. More data from Metop-B will be included in the MACC NRT analysis soon. The MACC forecasts and analyses are freely available to users. For more information about MACC and how to access the data please go to ACKNOWLEDMENTS: We would like to thank all the data producers. D. Hurtmans, P. Coheur (ULB, Belgium) and Cathy Clerbaux, M. George J. Hadji-Lazaro (LATMOS, France) are acknowledged for scientific development, maintenance and distribution of the CO products from IASI, available from the Ether French atmospheric database ( The GOME-2 data come were produced by DLR for EUMETSAT/O3M-SAF and are available from TCCON data were obtained from the TCCON Data Archive, operated by the California Institute of Technology from the website at tccon.ipac.caltech.edu.

7 Figure 5: Comparison between the CH 4 delayed mode analysis and the TCCON CH 4 column-averaged measurements at Lamont (Oklahoma, USA). Top panel: Timeseries of the analysis (cyan triangles) and of the measurements (black dots). Bottom panel: Timeseries of the monthly differences between the analysis and the measurements (cyan triangles) and standard deviation of the differences (error bars). Changes in the observing system are added in vertical boxes between the two panels. REFERENCES Bhartia, P. K. et al. (1996): Algorithm for the estimation of vertical ozone profiles from the backscattered ultraviolet technique, J. Geophys. Res., 101(D13), 18,793 18,806. Bhartia, P. K. and Wellemeyer, C.: TOMS-V8 Total O 3 algorithm, in: OMI ozone product ATBD Volume II, NASA Goddard Space Flight Center, Greenbelt, MD, USA, Boersma, K.F. et al. (2004): Error analysis for tropospheric NO 2 retrieval from space.j. Geophys. Res., 109, D04311, doi: /2003jd Carli, B. et al. (2004): First results from MIPAS/ENVISAT with operational Level 2 code. Adv. Space Res., 33, 7, , doi: /s (03) Clerbaux, C et al. (2009): Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, Deeter, M. N., et al. (2010): The MOPITT version 4 CO product: Algorithm enhancements, validation, and long-term stability, J. Geophys. Res., 115, D07306, doi: /2009jd H. J. Eskes et al. (2005): Retrieval and validation of ozone columns derived from measurements of SCIAMACHY on Envisat, Atmos. Chem. Phys. Discuss, 5, Flemming, J., and A. Inness (2013), Volcanic sulfur dioxide plume forecasts based on UV satellite retrievals for the 2011 Grímsvötn and the 2010 Eyjafjallajökull eruption, J. Geophys. Res. Atmos., 118, doi: /jgrd

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