Stephanie Guedj
email: stephanie.guedj@meteo.fr

Vincent Guidard

Benjamin Ménétrier

Jean-Francois Mahfouf

Florence Rabier

Future benefits of high-density radiance data from MTG-IRS in the AROME fine-scale forecast model Final Report

Introduction

The accuracy of Numerical Weather Prediction (NWP) models depends largely on the availability and quality of meteorological observations. Conventional data from networks of weather stations, radio-sounding data as well as radar reflectivity and aircraft measurements provide the largest amount for convective-scale data assimilation systems. But, this observing system is supplemented by an increasingly important set of satellite observations. The next generation of EUMETSAT geostationary meteorological satellites, Meteosat Third Generation (MTG), will bring in orbit for the first time, an hyperspectral Infrared (IR) instrument, called Infrared Sounder (IRS). This instrument will provide unprecedented high-resolution geophysical information over Europe [START_REF] Tjemkes | Capabilities of the mtg-irs candidate candidate mission to depict horizontal moisture structures[END_REF]. Radiances will be measured inside the water vapour and CO 2 absorption bands with extremely high spectral resolution (0.625cm -1 ) and accuracy (∼ 2 km resolution with 10% accuracy for humidity and ∼ 1 km with 0.5 o -1.5 o K accuracy for temperature). The IRS spectrometer will be based on an imaging Fourier-transform interferometer, measuring radiances in two bands, 800 spectral channels in the Long-Wave InfraRed (LWIR) and 920 channels in the Mid-Wave InfraRed (MWIR), with a spatial resolution of 4 km and a basic repeat cycle of 60 min.

The purpose of this study is to evaluate the potential benefits of high-density radiance data assimilation from MTG-IRS in a fine-scale forecast model. The standard way to undertake such a study is to carry out an Observing System Simulation Experiment (OSSE) using a known artificial "truth" referred to the Nature Run (NR). It is usually defined by a global free-run model integration or a reanalysis (figure 1, [START_REF] Masutani | Observing system simulation experiments[END_REF]). The French global Action de Recherche Petite Echelle Grande Echelle (ARPEGE) system is used to construct the NR in an especially designed high resolution configuration (7 km, 115 vertical levels). The full dataset of available observations over Europe is simulated from the NR as well as the new IRS observing system. Because observations extracted from the NR, are "perfect", various sources of error must be simulated and added to form synthetic observations [START_REF] Errico | Development and validation of observingsystem simulation experiments at nasa's global modeling and assimilation office[END_REF]. Then, synthetic observations are assimilated in the same way as real observations in the 3D-Var Applications of Research to Operations at MEsoscale (AROME) system ( [START_REF] Seity | The arome-france convective scale operational model[END_REF] and [START_REF] Brousseau | A prototype convectivescale data assimilation system for operation : the Arome-RUC[END_REF]). This system has been operational since 2008. It is used to avoid the fraternal twin ("incest") problems, as shown in [START_REF] Arnold | Observing-systems simulation experiments: Past, present, and future[END_REF]. Indeed, both models do not use the same physic package : Schemes for deep convection, micro-physical species, shallow convection, radiative transfer and turbulence are different as well as the surface treatments). Even if some even if part of the code is common, the forecast model used for the NR is not used afterwards for the data assimilation experiment in the full OSSE. That way, the OSSE results should not show an unrealistic observation impact when compared to the "truth" when producing analyse and forecast fields.

The second purpose of this work is to tackle problems related to the general underused of satellite radiances in data assimilation systems. It is expected that NWP centres should be ready to treat the very large amount of observations produced by Figure 1: Observing System Simulation Experiment (OSSE) methodology MTG-IRS to wit, (∼ 3 Gigabytes/second at instrument output, [START_REF] Donny | Meteosat third generation (mtg), space segment status and its technological challenges[END_REF]). In addition to operational time constraints and computational processing power limitations, variational assimilation schemes also require a drastic reduction of data density. In fact, it was shown that assimilating high-density observation may result in a degradation of the analysis rather than in an improvement if the system is sub-optimal, i.e. observation/background errors are not well specified ( [START_REF] Liu | The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study[END_REF] and [START_REF] Dando | The optimal density of atmospheric sounder observations in the met office nwp system[END_REF]). Firstly, most of NWP centres still use a diagonal observation covariance error matrix (R), which assumes uncorrelated observational errors. This assumption is no longer valid for satellite observations which may be spectrally or spatialy correlated [START_REF] Stewart | Correlated observation errors in data assimilation[END_REF], [START_REF] Bormann | Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: Application to AIRS ans IASI data[END_REF], [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF] among many others). Secondly, sub-optimality may arise from the use of a static, homogeneous and isotropic background covariance error matrix (B). Background-error covariances have a deep impact on the analysis, since they are used to filter and propagate information provided by observations [Daley, 1991]. Recently, the use of correlated observation errors, as diagnosed by the Desroziers diagnostic [START_REF] Desroziers | Objective validation of data assimilation systems: Diagnosing sub-optimality[END_REF] for Infrared Atmospheric Sounding Interferometer (IASI) was tested in assimilation trials at ECMWF and the MetOffice. The treatment of the correlated errors for IASI in 4D-Var leads to an improvement in forecast accuracy [START_REF] Weston | Accounting for correlated error in the assimilation of high-resolution sounder data[END_REF], providing that diagnosed variance errors are sufficiently inflated [START_REF] Bormann | Accounting for inter-channel observation error correlations in the ecmwf system[END_REF]. This mean that sources of observational error correlation are not fully understood yet.

At Météo-France, OSSEs were implemented using both operational NWP models: ARPEGE for the NR and AROME for the data assimilation scheme (section 2). The full dataset of atmospheric observations, as well as MTG-IRS radiances were simulated from the NR with scaled observation errors (section 3). The section 4 presents validation and calibration exercises against real world data assimilation systems to ensure a realistic impact of the new observing system. Finally, several assimilation trials were undertaken to evaluate the the potential benefits of different-density radiance data from MTG-IRS (section 5). Potential effect of error correlations on NWP performances are discussed in section 6.

2 The Nature Run and the assimilation system used for the OSSE

The global ARPEGE forecast system

The ARPEGE system is used in operations at Météo-France for NWP since 1992. The code is partly common with the Integrated Forecasting System (IFS) software and it is used to create the NR which is a long, uninterrupted forecast representing the "true" state of the atmosphere. ARPEGE is a global spectral model, with a Gaussian grid for the grid-point calculations. The vertical discretisation is done according a followingterrain pressure hybrid coordinate over 115 vertical levels (from 0.1hPa to 10m). The version used to generate the ARPEGE-NR has a variable horizontal resolution with truncation Tl1198 with a 2.2 stretching factor (around 7.5km over France and 36km over antipodes). This NWP model was used because 1) it is a mature NWP system with a proven forecast skill and 2) the atmospheric simulation resolves scales compatible with the future MTG-IRS observing system.

The simulated fields from the ARPEGE-NR are hourly available for both prognostic and diagnostic model fields. The period covers 2 months during winter 2013 (February to March) and 4 months during summer 2013 (June to September). The first guess is based on one atmospheric analysis produced by the operational ARPEGE-NWP system. A few weeks after the starting date, the atmospheric state of the ARPEGE-NR diverges from the one of ARPEGE NWP version. The influence of assimilated observations held in the First-Guess (FG) is progressively eradicated and the system converges toward climatologies, evolving continuously in a dynamically consistent way. Figure 2 and3 give an example of specific humidity and temperature fields produced by the Nature Run.

The ARPEGE-NR proved its usefulness in many applications within the OSSE framework. One can extract fields of the "true" atmospheric state to evaluate the quality of the produced analysis and the forecast skills whether the new observing system is included or not. The ARPEGE-Nature Run (NR) is also a source of simulated observations.

1 0 °S 1 0 °S 1 0 °S 1 0 °S 0°1 0°N 20°N 30°N 40°N 50°N 60°N 70°N 80°N 180°160°W 140°W 120°W 100°W 80°W 60°W 40°W 20°W 0°20°E 40°E 60°E 80°E 100°E 120°E

The 3D-VAR AROME Meso-scale system

Since 2008, the AROME system is the operational convective-permitting Limited Area Model (LAM) of Météo-France with a 2.5 km-grid covering the western part of Europe and part of the Mediterranean Sea ( [START_REF] Seity | The arome-france convective scale operational model[END_REF]). With regard to the ARPEGE system, the AROME physical package is inherited from the physical parametrisations of the Meso-NH research model [START_REF] Lafore | The meso-nh atmospheric simulation system. part i: Adiabatic formulation and control simulations[END_REF]. The high horizontal resolution permits to explicitly resolve deep convection and the micro-physical scheme makes use of prognostic equations to describe the six water species. The shallow convection and turbulence are parametrised according to the Eddy Diffusivity Kain Fritsch (EDKF) scheme and the radiative scheme. Surface parameters are computed through a two-way coupling with the externalised surface model SURFEX.

The AROME system has its own data assimilation system, based on a 3D-Var scheme with an incremental formulation, observations operators, minimization technique and data flow [START_REF] Courtier | The ecmwf implementation of three-dimensional variational assimilation (3d-var). i: Formulation[END_REF]] inherited from IFS/ARPEGE and Aire Limitée Adaptation Dynamique Développement International (ALADIN) ( [START_REF] Fischer | An overview of the variational assimilation in the aladin/france numerical weather-prediction system[END_REF], [START_REF] Brousseau | A prototype convectivescale data assimilation system for operation : the Arome-RUC[END_REF]). The 3D-Var data assimilation system is carried out using a 3-hour forward intermittent cycle. A cycle consists in computing the analysis using observations within a ±1h30 assimilation window and a 3-hour forecast to serve as FG for the next cycle. The two wind components, temperature, specific humidity and surface pressure are analysed. Other model fields are directly cycled from the previous guess. 48-h forecast can be run from each cycle.

The AROME-NWP observation error covariances are tuned to account 2 distinct contributions: instrument errors and representativeness errors. Observation errors are assumed to be uncorrelated. To compensate for this omission, 1) observation errors are inflated between 2 to 4 times that of the standard deviation of the true error covariance matrix and 2) data are dramatically thinned to insure the best observation independence. The background error covariances are based on a multivariate formulation and have been estimated using an ensemble-based method ( [Berre, 2000] and [START_REF] Berre | The representation of the analysis effect in three error simulation techniques[END_REF]). In comparison with larger-scale background errors, they have smaller horizontal correlations and increased standard deviations for model parameters representing small scale structures, such as temperature in the low layers [START_REF] Brousseau | A prototype convectivescale data assimilation system for operation : the Arome-RUC[END_REF].

Simulation of observations

The reason for simulating observations which are to used in OSSE, is to extract observed quantities at the closest coordinate (in space and time) from the "true" background field (NR). The observation operator includes a) the interpolation process and b) the radiative transfer model RTTOV v10 [START_REF] Saunders | RTTOV-8 science and validation report[END_REF]) for the direct assimilation of radiances. All observations, including MTG-IRS, are simulated (every 3 hours), from the NR, over the AROME domain.

Simulation of the operational observing systems

The averaged amount of available observation points for each assimilation cycle is :

• surface : 5000 measurements from surface stations (SYNOP), ships and buyos, 15000 observations from ground GPS (GPS-SOL) and 1000 from wind profilers.

• altitude : 950 observations from radiosondes (TEMP, PILOT, standard levels), 5000 measurements from aircraft during ascent/descent phases (AIREP, AM-DAR), 3000 wind vector observations from Geostationary Orbit (GEO) and low Earth orbit (LEO) satellites, 2500 observations from LEO scatterometers (SCATT, full coverage: 9UTC, 21UTC), 1500 HIRS radiances (3UTC, 9UTC, 12UTC, 21UTC), 2000 AMSU-A and AMSU-B radiances (6UTC, 12UTC, 15UTC, 21UTC), 500 SSMI/S radiances (6UTC, 18UTC), 4000 IASI radiances, (9UTC and 21UTC), 1000 CriS radiances (3UTC, 12UTC) et 8000 Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiances.

Figures 4, 5 and 6 show a comparison between simulated and real observations for conventional and radiance data. Distributions of real observations values (circles) and simulated observations over the vertical are relatively close to each other, especially for temperature. Main differences between real and simulated data occur for humidity (increase toward the surface, up to 0.77%) and U-wind profiles (increase toward the top of the atmosphere, up to 12 m/s at 200 hPa) versus real radiosounding data.

On the overall, simulated observations give satisfactory results relatively to real observations. Simulated and real observations were not expected to be exactly similar since they are not using the same atmospheric states. However, the comparison was helpful to evaluate the quality of the observation operator and the ability of the NR to describe realistically the atmosphere.

Simulation of MTG-IRS radiances

IRS will constitute spectra at a moderately high spectral resolution of 0.754 cm -1 with a spectral sampling of 0.625 cm -1 in two bands, one in the long-wave infra-red region (700 to 1210 cm -1 ) and the other in the midwave infra-red (1600 to 2175 cm -1 ) providing a total of 1738 channels. For operational NWP, the main focus will be on the assimilation of temperature and humidity. This work focussed on humidity channels because very MTG-IRS radiances were also simulated from the NR. A coefficient file for IRS was generated by Météo-France/CMS (Pascal Brunel), based on the existing IASI coefficients (more details in [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF]). In addition, most of the instrument specifications, such as observation geometry, horizontal/spatial resolution, are inherited from SEVIRI. The full IRS spectrum was simulated every 3 hours over the AROME domain.

Figure 7 shows the averaged Brightness temperature (Bt) spectrum for simulated IRS measurements. 3 channels were selected (red dots) in 1) the CO 2 absorption band (temperature sounding), 2) window band and 3) H 2 O absorption band (water vapour sounding). Maps of simulated Bt for this 3 channels respectively are presented in figure 9.

It is impossible to assimilate the full spectrum, its dimensionality has to be much reduced for operational data assimilation schemes. Amidst the 640 humidity channels, we singled out 1 channel out of 5, to generate a first subset of 128 channels. Then we selected a subset of channels (50, 25 and 15) which provide information on the entire vertical (figure 9 and table 1). Then we have selected a subset of channels (50, 25 and 15) in a way that they give information on the entire vertical (figure 9, table 1). Additionally, a group of closely-separated channels circa 300 hPa were selected to evaluate in which way the system deals with redundant data. This methodology, far from optimal, strays from the original subject. This simple method was assumed to be

Assignment of observation errors

When creating simulated observations for an OSSE, it is useful to distinguish between the modelling of observational signal and noise. The signal provides information which the data assimilation system can interpret and store, whereas any noise will be a source of error that the data assimilation system will tend to lessen.

In practice, the real observations (y) are the sum of the true atmospheric value (y t ) and of an error ε m incurred during the measurement or subsequent data processing. y t is also affected by an error of representativity ε r related to the mismatch between the AROME model grid volume and the volume sampled by the instrument, as well as from a mismatch between the observed and predicted variables. In the real data assimilation system, y t is unknown and ε m is combined with ε r . The final observation error is finally tuned up or down by the scaling factor α 1 to take into account the residual errors and correlation.

Real Observation :

y o = y t + ((ε m + ε r ) × α 1 )
In the OSSE data assimilation system, y t is known (extracted from the NR) to which is added a random contribution ε x to the model output, which stands for the observation error (ε x ∼ ε m + ε r ). The random vector (actually a specific realization) is obtained from a multivariate normal distribution having the error statistics of the real observations (y o ) and similarly scaled by the α 2 factor. Simulated Observation :

y s = y t + (ε x × α 2 )
We can apply this methodology to the full operational observing system that needs to be assimilated in the OSSE assimilation system to construct the Control (CTL) run. But no estimate are available for MTG-IRS. Estimates from IASI and SEVIRI data were useful to specify the "correct" observation error for IRS. Three methods were used to diagnose observation errors and their correlation : the Hollingsworth and Lönnberg (H/L) method, the Desroziers (Dz) diagnostic and the Background error method. Details on methods and some results can be found in [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF]]. Here we focus on both the Hollingsworth and Lönnberg (H/L) method and the Desroziers (Dz) diagnostic.

Figure 10 indicates that the observation error should be quite close to the instrument error for temperature channels. However, both methods do not show a good agreement for humidity channels. Thus, the averaged diagnosed values were interpolated following IRS expected noise function. As mentioned in [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF], IRS is expected to carry strong inter-channel/horizontal error correlation since it was diagnosed from real IASI and SEVIRI data. As our system is not sufficiently mature to take into account this error correlation/these error correlations, we have over-estimated MTG-IRS observation error on purpose, as it is done for the full operational observing system.

OSSE calibration/validation

The statistics of analysis errors are partly determined by the statistics of observation errors, as revealed by the fundamental DAS equations and as examined further by [START_REF] Daley | Spectral characteristics of kalman filter systems for atmospheric data assimilation[END_REF]. Here, the impact of simulated data in the OSSE system is verified relatively to that of real observation data in the operational system (AROME-NWP). Each observing system should ideally describe comparable impacts in the analysis and forecast skill in both systems.

Their are many methods to perform the evaluation: data denial (or adding) experiments, forecast anomaly correlation and RMS analysis/forecast error verified against the NR. In the following, results obtained from the last method are presented. Because the assumed observation error covariances, together with background error covariances, play an important role in determining the weight of a given observation in data assimilation, several calibration/validation experiments were run to find the best possible match between the OSSE and AROME-NWP statistics (13 days, July 2013). The Operational (OPER) experiment is similar to the AROME-NWP system. Real observations were assimilated and the observation errors were not modified relatively to the operational configuration. In the CAL experiment series, observations were simulated and perturbed using scaled values of observation errors. Then, simulated observations were assimilated with the same observation errors used to simulate the observation. All assimilation experiments are summarized in table 2.

Calibration steps using analysis departures to observations/simulations of radiosonde data are presented in figure 11.

Statistics properties for the OPER run (real world) should match one of CAL experiments (OSSE world). In fact, the simulated observations should force the OSSE model state in the same way that the real observations do. 11 indicates that the standard deviation of observation error, as specified in the real system, should be reduced by about 50% to improve the fit between statistical properties of OSSE and the real system. Manual adjustments has been applied to low atmospheric levels. This exercise was done for each observation type independently. Except for AMSU observations, the observational errors were/error was reduced by about 50% Figure 11: Analysis departures to specific humidity observations/simulations for radiosounding data following 5 configurations : 1) The CTL run assimilates real observations with the same observational error as the operational system (α 1 ); 2) CAL-1.0 assimilates simulated observations with the same observational error as the operational system (α 1 = α 2 = 1.0); 3) CAL-0.8 assimilates simulated observations with reduced observational error with regard to the operational system (α 2 = 0.8); 4) CAL-0.5 assimilates simulated observations with reduced observational error with regard to the operational system (α 2 = 0.5); 5) CAL-0.5sc assimilates simulated observations with reduced observational error with regard to the operational system and manual adaptations (α 2 = 0.5sc); 6) CAL-0.2 assimilates simulated observations with reduced observational error with regard to the operational system and manual adaptations (α 2 = 0.2)

Results : Assimilation experiments

A set of assimilation experiments was tested by the OSSE, to detect future benefits of MTG-IRS in the AROME mesoscale model.

As mentioned earlier, the full operational observing system as well as IRS radiances were simulated (section 3) with calibrated errors (section 4) during 15 days (July 2013). This set of simulated observations were assimilated with the same error used to construct the CTL run. In this experiment, simulated observations were subjected to various quality control checks such as selection of stations, levels and channels, flow-dependent background departure check, redundancy check, horizontal and vertical thinning of data denser than the model grid... Observations are also bias corrected in the operational model using the variational bias correction scheme (VarBC, [START_REF] Auligné | Adaptative bias correction for satellite data in a numerical weather prediction system[END_REF]). However, since synthetic observations are assumed to be unbiased in the OSSE, the VarBC scheme was disconnected: no bias correction was applied.

25 IRS humidity channels were assimilated with different thinning distances (20, 40 and 80 km), as shown in table 3. Additional experiments were also carried out using different IRS humidity channel selection (15, 25, 50 channels, not shown)

Impacts on atmospheric analysis

In the OSSE framework, the NR can be used to verify the quality of the analysis while comparing it to the true state of the atmosphere. The full set of atmospheric specific humidity profiles (516152 profiles) were extracted every 3h, from all assimilation experiments and compared to the NR. Standard Deviation (STD), Root Mean Square (RMS) error and bias were computed over a set of analysis. Figure 12 shows analysis scores versus the NR for the CTL run (no IRS, red line), the IRS80km run (back line), IRS40km (blue line) and IRS20km (green line). The assimilation of IRS do improve specific humidity modelling from the surface to the top of the atmosphere. Mean analysis reduction errors are about 27.2% for IRS80km, 31.5% for IRS40km 27.7% for IRS20km over whole vertical levels. The maximum impact is obtained for high atmospheric levels (800-400 hPa) where few observations are used.The IRS impact is lower outside this region because the information brought can be redundant with SEVIRI! ( SEVIRI!) water vapour channels at the top and with conventional observations at the bottom.

Figure 13 shows maps of specific humidity RMS error at 700 hPa, computed against the NR. Maps of IRS observation location are also presented to illustrate used data density in each experiment. As shown in figure 13, IRS data improve the smaller spatial scales.

However, figures 12 and 13 indicate also that the system is not able to take full advantage of an increase in assimilated data density. There is a threshold where increasing assimilated data amount do not add any information to the analysis. Theoretically, this finding is relevant when real observations are assimilated at a too high density because they may carry error correlation ( [START_REF] Liu | The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study[END_REF] and [START_REF] Dando | The optimal density of atmospheric sounder observations in the met office nwp system[END_REF], [START_REF] Bormann | Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: Application to AIRS ans IASI data[END_REF]). However, in this work, errors were added to perfect observation values while ignoring correlations (section 3).

Impacts on forecast scores

The ultimate goal of data assimilation in the meso-scale NWP context is to improve short-range forecast (3-12h), mid-range forecasts (12-24h) and longer range forecasts (24-48h). The evolution of data impact with forecasts is shown in figure 14. Time series of specific humidity forecast error (RMS) versus the NR are presented as a function of forecast ranges. Results are averaged from the surface to the top of the atmosphere for each forecast range.

Positive impacts are demonstrated on short-range forecast fields of specific humidity when IRS are assimilated. Best results are obtained using the IRS80 experiment since the improvement is visible up to 48h. However, IRS20 and IRS40 experiments show a degradation of the forecast quality from 9h and 12h respectively.

Profiles of forecast scores (STD) were computed for IRS80, IRS40 and IRS20 experiments, normalised by the CTL run (NoIRS) at 8 forecast ranges (6h, 12h, 18h, 24h, 30h, 36h,42h and 48h, figure 15). Negative (positive) values, indicate that the assimilation of IRS decrease (increase) the forecast error with regard to the NR. Error changes are expressed in %. Overall, the assimilation of IRS data induces changes in humidity profiles at all forecast ranges. IRS80 scores demonstrates neutral to positive impacts whereas IRS40 and IRS20 show negative to neutral impacts. Quantitatively, even if changes seem over-estimated, maximum changes affect short-ranges forecast (up to -33.74% of STD error, 500 hPa) and can be sensitive up to 48h-forecast (up to +8.35% of STD error, 700 hPa).

Discussion

To interpret OSSE results is quite challenging. The many causes of inconsistent results must be investigated carefully and explained for OSSEs to be more creditable. In our case, the assimilation system cannot extract useful information if the amount of assimilated data is too dense. The best analysis and forecast improvements of humidity fields are assigned to IRS80 whereas IRS20 get the worst results.

In this work, observations were simulated and assimilated assuming uncorrelated errors. Following studies presented in [START_REF] Liu | The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study[END_REF], [START_REF] Desroziers | Objective validation of data assimilation systems: Diagnosing sub-optimality[END_REF] and [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF], a gradual increase in data density should monotonically improve the quality of the analyses if 1) observations errors are uncorrelated and 2) the analysis is optimal. In our system, this is not verified (section 5). In order to better understand the origin of theses results, the a posteriori Desroziers diagnostic was applied on the analysis and first-guess departures to simulated observations. Unexpected and significant inter-channel error correlations were diagnosed even if they were assumed uncorrelated during the simulation/assimilation process (figure 16).

Several failures in the assimilation and diagnostic schemes can explain these unexpected results. Indeed, the Desroziers diagnostics are exact under the following conditions:

• the specified error covariance matrices (B and R) are exact,

• the analysis is optimal (true minimum of the cost function),

• the statistical expectations in Desroziers diagnostics should be computed over independent realizations.

If these conditions were met in our case where uncorrelated observation errors were explicitely specified, then the diagnosed observation error correlation matrix should be diagonal, and the first-guess departure and background error correlation matrices should be equal except for diagonal coefficients. This is not the case here, probably because:

• the specified background error covariance matrix is a climatologic, homogeneous and isotropic model, which is a crude representation of the true background error covariance matrix,

• the analysis is determined after a limited number of iterations, so that the minimum of the cost function is not always reached,

• the statistical expectations in Desroziers' diagnostics are approximated with an homogeneous and isotropic ergodicity assumption: observation couples are sorted according to thier separation and covariances are computed inside each distance class.

Several studies have shown the limitations of Desroziers diagnostics, especially for the computation of off-diagonal coefficients. Some attempts to correct this issue with iterative schemes are currently developed. In any case, these findings should be studied further to improve our understanding in observation error correlation and its effect on NWP systems ... An Observing System Simulation Experiment (OSSE) was implemented to investigate the potential impact of the future MTG-IRS in the convective-scale AROME forecast system. In OSSE, simulated rather than real observations are the input to the 3D-Var data assimilation system. Hence, all observation types (conventional and satellite measurements) as well as IRS radiances were simulated from the true state of the atmosphere: the Nature Run (NR) (section 3). To make them more realistic, these values were explicitly increased by an appropriate amount of noise, sized on observation error (section 4). The resulting values were ingested into the data assimilation system, in the same way real observation would be in the operational system. Several configurations for the assimilation of water vapour MTG-IRS channels were evaluated as well as the CTL run in which all simulated observations are used except IRS (section 5).

An important work was undertaken for the preparation of MTG-IRS data assimilation. First, the full IRS spectrum was simulated using the RTTOV radiative transfer model. Then, an intensive inter-comparison exercise and error estimation/calibration with existing satellite measures, was conducted to ensure good quality simulations with realistic errors. Instruments such as the SEVIRI radiometer (similar time/spatial resolution) and the IASI hyper-spectral sounder (similar spectral resolution), were primarily used for these exercises. The main results show that MTG-IRS observation errors amplitude for water vapour channels, diagnosed from IASI data, should be increased by a factor of 2.5, starting from the level of radiometric noise. This result is consistent with [START_REF] Bormann | Accounting for inter-channel observation error correlations in the ecmwf system[END_REF]. This factor should be even higher if one wants to take into account account the presence of inter-channel error correlation for real observations. In addition, SEVIRI were useful to evaluate the best thinning distance to use for MTG-IRS. Horizontal error correlations were diagnosed for this task and it was shown that the distance between two observations should not be higher than 40km. More details can be found in [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF] and [START_REF] Guedj | Future benefits of high-density radiance data from mtg-irs in the arome fine-scale forecast model[END_REF].

Then, simulated radiances of MTG-IRS were included into the system with preliminary diagnosed observation error. In practice, effective horizontal and vertical data density of IRS were modified in each experiment, playing on thinning distance and channel selection. Since this work was carried out in an OSSE framework, it was possible to evaluate the realism of produced analysed and forecast fields against the NR and the impact of MTG-IRS against the CTL run. Quantitatively, the future observing system may be able to significantly improve the specific humidity modelling, especially when few observations are available. However, forecast impact studies indicate that the density (in term of thinning distance and channel selection) should be carefully specified. The analysis improvement do not well propagated into the forecast if observations are too densely assimilated. Large Root Mean Square (RMS) error may occurs on humidity, temperature and wind fields, up to 48h forecast. Experiments simulating only the impact of observations from polar-orbiting satellites are oversimplified compared with the impacts expected from the complete global observing system. Nevertheless, they are useful as they contribute toward the forecast error reduction.

Preliminary studies on diagnosed error correlation were presented in section 6 to explain briefly potential sources of forecast changes. Recently, [START_REF] Bormann | Accounting for inter-channel observation error correlations in the ecmwf system[END_REF] showed that accounting for inter-channel error correlations allows the use of an R consistent with diagnostics and slight inflation gives a small benefit. [START_REF] Weston | Accounting for correlated error in the assimilation of high-resolution sounder data[END_REF] shows similar results and demonstrates that accounting for inter-channel error correlations allows a more aggressive use of observations which leads to an improved forecast accuracy, as well as an adaptive weighting of the observations based on the relationship. But, does a non-diagonal R will allow the use of more water vapour channels for the future hyperspectral MTG-IRS sounders? What about the temporal error correlation ( [START_REF] Eyre | The impact of the temporal spacing of observations on analysis errors in an idealised data assimilation system[END_REF])?

This report, as well as the litterature demonstrate current operationalNWP assimilation schemes make use of only a small fraction of the instrument potential (horizontal thinning, channel selection ...). In the same time, new generation of hyperspectral instruments are planed to deliver more and more observations : 2738 channels for MTG-IRS every hour, 16921 spectral channels for IASI-NG (New Generation). In order to reduce the spectrum into a small number of measurements, one proposed method is Principal Component (PC) compression. This method should be a greate advantage for data dissemination but, at the present time, the direct assimilation of PC scores is not recommended for operational use [START_REF] Hilton | Recommendations for the use of principal component-compressed observations from infrared hyperspectral sounders[END_REF]. However, the use of reconstructed radiance spectrum from PC scores is an area of active research.

Figure 3 :

 3 Figure 3: Maps of Temperature ( o C) from ARPEGE the Nature Run over the Northern Hemisphere, at 850 hPa,2013/07/23-00UTC, 33 days after the start run

Figure 4 :

 4 Figure 4: Simulated versus real radiosounding observation data of a) temperature , b) specific humidity and c) U and V wind components

Figure 5 :

 5 Figure 5: Simulated versus real satellite observation data of a)AMSU-A, b)AMSU-B, c) MHS, d) SSMI/S

Figure 10 :

 10 Figure 10: Estimates of observation errors (standard-deviation) for a selection of IASI T (left panel) and Q (right panel) observations : assumed observation errors (as specified in the AROME system, blue line), diagnosed values computed using the Hollingsworth and Lönnberg (H/L) method (black thick line) and using the Desroziers (Dz) diagnostic (red line). The in-flight instrument error (dashed black line) for IASI is shown as well as the recommended values for the future MTG-IRS (dotted green line).

Figure 12 :

 12 Figure 12: Analysis scores (bias and STD) of specific humidity versus the Nature Run for 4 assimilation experiments on the 25th of July -12H

Figure 13 :

 13 Figure 13: Analysis scores (RMS) of specific humidity (700 hPa) versus the Nature Run for 4 assimilation experiments on the 25th of July -12H

Figure 14 :Figure 15 :

 1415 Figure 14: Forecast scores (RMSE) time series of specific humidity for 4 assimilation experiments. Forecasts are computed from the analyse fields of the 25th of July 2013.The CTL run (red line) do not assimilate IRS observations. IRS80 (blue line), IRS40 (black line) and IRS20 (green line) experiments assimilate IRS observations with a distance thinning of 80, 40 and 20 km respectively. RMS is computed against the Nature Run at corresponding time

Figure 16 :

 16 Figure 16: Error correlation matrix for a) First-guess departures to simulated IRS data, b) observations error and c) background errors (in observation space). Error correlation were diagnosed over a period of 15 days (July 2013), over the AROME domain

Table 1 :

 1 IRS Channel selection

			2
	Brigthness Temperature	1			3
		200	400	600	800	1000	1200	1400	1600	1800
					Channel Index
	Figure 7: Averaged spectrum of simulated IRS radiances on the 25th of July 2013 -12
	UTC, over the AROME domain : 1) Temperature channel 83, 2) Window channel 421
	and 3) Water Vapour channel 867

  The reduction of observation errors

	Exp Name Observations	Obs. error
			scaling factor (α)
	OPER	Real	1.0
	CAL-1.0	Simulated	1.0
	CAL-0.8	Simulated	0.8
	CAL-0.5	Simulated	0.5
	CAL-0.5sc	Simulated	0.5 + manual
	CAL-0.2	Simulated	0.2
	Table 2: Calibration experiments
	improves the fit of the OPER run down to a threshold that depends on the observation
	type. Figure

Table 3 :

 3 or a selection of temperature channels ...All assimilation experiments ran during 15 days (July 2013), with a 48h forecast. Since OSSEs are very labour intensive, we present here the result for the 25th of July at 12UTC with a focus on humidity impacts. Assimilation experiments

	Exp Name	Distance	Channel
		Thinning (km) number
	CTL (no IRS)
	CTL+IRS80km	80	25
	CTL+IRS40km	40	25
	CTL+IRS20km	20	25

This research is funded by EUMETSAT. The authors would like to thank Pascal Brunel from the CMS for RTTOV and the IRS coefficient file. Jean Maziejewski for his help to revise the manuscript.


J A Barrat
email: barrat@univ-brest.fr

O Rouxel

K Wang

F Moynier

A Yamaguchi

A Bischoff

J Langlade

Early stages of core segregation recorded by Fe isotopes in an asteroidal mantle By

Keywords: iron isotopes, achondrite, ureilite, core

Highlights:

-Ureilites displays  56 Fe values higher than average chondrite.

-Segregation of Fe-sulfide melts explains the high  56 Fe values in ureilites.

-Formation of a core can begin at very low degrees of melting through the circulation of a Fe-S melt through a silicate mantle.

Introduction

One of the most significant issues for understanding the differentiation of telluric bodies is when and how their cores formed. Trace element abundances and W isotopic compositions obtained on a series of iron meteorites indicate that the cores in the first differentiated planetesimals aggregated metallic melts with various S contents, and formed from ca. 0.6 to 3 Myrs after the first solids condensed in the Solar System (e.g., [START_REF] Blichert-Toft | The early formation of the IVA iron meteorite parent body[END_REF]Kruijer et al., 2014 and references therein). The mechanisms of core formation are still highly debated, especially for the early stages of the process. The percolation of Fe-S melts into an olivine matrix has been suggested and experimentally investigated, but the contribution of such melts to the cores of the planetary embryos or of larger bodies, is a matter of debate [START_REF] Bruhn | An interconnected network of core-forming melts produced by shear deformation[END_REF][START_REF] Yoshino | Core formation in planetesimals triggered by permeable flow[END_REF][START_REF] Bagdassarov | Constraints on the Fe-S melt connectivity in mantle silicates from electrical impedance measurements[END_REF][START_REF] Rushmer | Microsegregation rates of liquid Fe-Ni-S metal in natural silicate-metal systems: A combined experimental and numerical study[END_REF][START_REF] Watson | Connectivity of core forming melts: Experimental constraints from electrical conductivity and X-ray tomography[END_REF]. However, important constraints on the early stages of the differentiation of some small bodies are potentially recorded in their mantles, which can be investigated using meteorite samples.

Ureilites are remnants of the mantle of a now-disrupted carbon-rich body [START_REF] Downes | Evidence from polymict ureilite meteorites for a disrupted and re-accreted single ureilite parent asteroid gardened by several distinct impactors[END_REF], and are among the most common achondrites in the meteorite collections (about 400 ureilites are currently reported in the Meteoritical Bulletin Database). They are ultramafic achondrites (e.g., [START_REF] Mittlefehldt | Non-chondritic meteorites from asteroidal bodies[END_REF] which are widely considered as mantle restites [START_REF] Scott | Origin of ureilite meteorites and implications for planetary accretion[END_REF] formed after extraction of magmas and S-rich metallic melts. The vast majority of them are unbrecciated.

They are coarse-grained peridotites consisting chiefly of olivine and pyroxenes (pigeonite, and more rarely augite and orthopyroxene), abundant carbon (< 7 wt%, graphite and diamond), with accessory metal and sulfides. Olivine grains classically have distinctive iron-depleted rims and veins (Fig. 1).

They were formed by local reduction reactions with adjacent carbon. These rims give important constraints on the Ureilite Parent Body's (UPB) history. The Fe-Mg zonings of the olivines indicate that ureilites equilibrated at high temperatures (1200-1300°C), and subsequently rapidly cooled down (~2-6°C/h), consistent with impact-excavation or disruption of the UPB (e.g., [START_REF] Myiamoto | Cooling history of some Antarctic ureilites[END_REF].

Except these rims and veins in olivine, silicates (i.e., olivine cores and pyroxenes) are quite uniform in Mg# number [= 100 Mg/(Mg+Fe), atomic] within any given ureilite. However, their compositions among samples show a huge variation, as shown by the olivine-core Mg# (= forsterite content), which range from 74 to 97. The Fe/Mn ratios (ranging from 3 to 57) and  17 O values (= -0.2 to -2.5 ‰) are correlated with this parameter (e.g., [START_REF] Clayton | Oxygen isotope studies of achondrites[END_REF][START_REF] Mittlefehldt | Non-chondritic meteorites from asteroidal bodies[END_REF]Fig. 2). The origin of these variations is a matter of discussions, but no consensus emerges at present. Certainly, they cannot be explained by igneous fractionation (e.g., [START_REF] Mittlefehldt | Non-chondritic meteorites from asteroidal bodies[END_REF]. Instead, it has been often argued that the Mg# range was ascribed to redox processes during the differentiation of the UPB or alternatively is inherited from the pre-igneous (nebular) history of the accreted materials (see [START_REF] Warren | Parent body depth-pressure-temperature relationships and the style of the ureilite anatexis[END_REF] and Goodrich et al. (2013a) for extensive discussions of the current models on its origin).

A score of ureilites are polymict breccias consisting of debris of these peridotites, various chondritic clasts , and some feldspathic clasts which are remnants of the UPB's lavas (e.g., [START_REF] Cohen | Feldspathic clast populations in polymict ureilites: Stalking the missing basalts from the ureilite parent body[END_REF][START_REF] Downes | Evidence from polymict ureilite meteorites for a disrupted and re-accreted single ureilite parent asteroid gardened by several distinct impactors[END_REF]. A large clast (ALM-A) has been recently discovered among the numerous stones of the Almahata Sitta fall, and demonstrate that at least part of these melts was trachyandesitic [START_REF] Bischoff | Trachyandesitic magmatism in the early Solar System[END_REF].

Unlike asteroids such as Vesta whose O isotopic compositions were homogenized by a magma ocean [START_REF] Greenwood | Widespread magma oceans on asteroidal bodies in the early solar system[END_REF], the wide range of  17 O values displayed by the ureilites demonstrates that the UPB was never totally melted [START_REF] Clayton | Oxygen isotope studies of achondrites[END_REF]. Nevertheless, it was heated enough to generate magmas (e.g. [START_REF] Cohen | Feldspathic clast populations in polymict ureilites: Stalking the missing basalts from the ureilite parent body[END_REF][START_REF] Bischoff | Trachyandesitic magmatism in the early Solar System[END_REF] and a small S-rich core [START_REF] Warren | Siderophile geochemistry of ureilite: a record of early stages of planetesimal core formation[END_REF][START_REF] Rankenburg | Highly siderophile elements in ureilites[END_REF]Goodrich et al., 2013b).

Iron isotope ratios can track the involvement of iron sulfides during the early stages of the formation of the asteroidal cores. Marked enrichments in the lighter iron isotope are found in iron sulfides formed at ca. 1000°C or below [START_REF] Schuessler | The experimental calibration of the iron isotope fractionation factor between pyrrhotite and peralkaline rhyolitic melt[END_REF][START_REF] Polyakov | New data on equilibrium iron isotope fractionation among sulfides: constraints on mechanisms of sulfide formation in hydrothermal and igneous systems[END_REF][START_REF] Wang | Iron isotope fractionation during sulfide-rich felsic partial melting in early planetesimals[END_REF], whereas no iron isotope fractionation has been detected between iron sulfide, metal and silicate melts at ca. 1250°C (e.g., [START_REF] Hin | Experimental evidence for the absence of iron isotope fractionation between metal and silicate liquids at 1 GPa and 1250-1300°C and its cosmochemical consequences[END_REF]. Here, we report high-precision iron isotope compositions of 30 meteorites from the UPB, and show that its mantle displays iron isotopic compositions heavier than average chondrite. This indicates that a significant fraction of the sulfides was efficiently segregated during the early stages of the melting, possibly before the melting of the silicates. Therefore, core formation in an asteroid can begin at very low degrees of partial melting through the circulation of a Fe-S melt through a silicate mantle.

Samples and analytical procedures

We analyzed 30 meteorites from the UPB (29 unbrecciated ureilites and a unique lava sample, Table 1). Samples were kindly provided by the NASA meteorite working group (MWG), the National Institute of Polar Research (NIPR), the Ecole Normale Supérieure de Lyon (ENSL), the Westfälische Wilhelms-Universität Münster, the Washington University in Saint Louis, and finally from the first author's collection. The samples from Antarctica have been extensively studied by different teams.

The compositions of their phases have been repeatedly determined and their chemical features are well known (the references are too numerous to be cited here). Our meteorites from Sahara have been less studied, and for most of them, only their descriptions in the Meteoritical Bulletin are available. They are all regular ureilites. We have examined polished sections of all these samples to confirm their main petrographical features, and determine the compositions of their silicates. The Saharan samples were selected in order to complement the range of compositions displayed by the Antarctic ones. Although the terrestrial weathering is pervasive in hot desert samples, the samples we selected were not severely weathered, and display partly preserved metal and sulfides.

The major-element compositions of the phases were determined by electron microprobe analysis using a Cameca SX100 at Service Commun de Microsonde Ouest (SCMO), Plouzané, or a JEOL JXA8200 at NIPR (Table 2). Minerals and metal standards were used for calibration. All analyses used wavelength dispersive spectrometers at 15 kV accelerating voltage, 10-30 nA beam current at NIPR, and 20 kV accelerating voltage, 40 nA beam current with the same counting time as Pierre Hudon and David Mittlefehdt (in [START_REF] Downes | Evidence from polymict ureilite meteorites for a disrupted and re-accreted single ureilite parent asteroid gardened by several distinct impactors[END_REF] at Plouzané .

Fragments (each of 300-1,000 mg) were crushed to a homogeneous fine powder using a boron carbide mortar and pestle. Twenty mg of crushed material was digested by sequential mixtures of HF/HNO 3 , HNO 3 and HCl for the samples analyzed in Brest and in HF/HNO 3 and HCl for the samples analyzed in St Louis. These procedures allow a perfect dissolution of all the phases except graphite and diamond, which are Fe-free, have no effect for our study.

Aliquots of the solutions were used for the determination of the Fe and Mg concentrations of in the solutions were determined by ICP-AES (inductively coupled plasma-atomic emission spectrometry) using a Horiba Jobin Yvon Ultima 2 spectrometer in Plouzané, and the Mg# numbers are given in Table 1. Iron in the dissolved samples was then purified by ion-exchange chromatography procedure previously described [START_REF] Wang | Iron isotope fractionation in planetary crusts[END_REF]. The purification was repeated twice, thus ensuring the removal of all the matrix elements. A majority of the Fe isotopic compositions were measured on the Thermo Electron Neptune multi collector-inductively coupled plasma-mass spectrometer housed at Ifremer, Plouzané, while the rest was analyzed in Saint Louis. In both Saint Louis and Brest the instrument was operated in high resolution mode to resolve isobaric interferences, such as ArO on 56 Fe, ArOH on 57 Fe, and ArN on 54 Fe. We also used the sample-standard bracketing technique to correct for instrumental mass discrimination by normalizing Fe-isotope ratios to the average measured composition of the standard that was run before and after the sample. In Plouzané, we performed an additional correction using Ni as an internal standard for mass bias correction [START_REF] Dauphas | Mass spectrometry and natural variations of iron isotopes[END_REF]. Fe isotope values are reported relative to the standard IRMM-14 using the -notation:  x Fe = 1000 [ ( x Fe/ 54 Fe) sample /( x Fe/ 54 Fe) IRMM-14 -1] where x=56 or 57.

Results for several georeference materials are similar to previously reported analyses (Table 3).

Errors are reported as 2 standard errors, which are calculated as the standard deviation divided by the square root of the total number of analyses and multiplied by the Student's t-value for the relevant degree of freedom at the 95% confidence level. Results for the samples are listed in Table 4.

Results

The  56 Fe values obtained for 30 meteorites from the UPB, fall within a restricted range, from 0.01 to 0.11‰ (Fig. 3, Table 4). The mean of all these samples is significantly heavier ( 56 Fe = 0.056 ± 0.008 ‰, n=30) than that of primitive meteorites, such as carbonaceous, ordinary and enstatite chondrites ( 56 Fe = 0.005 ± 0.008 ‰, n=42, [START_REF] Craddock | Abyssal peridotites reveal the near-chondritic Fe isotopic composition of the Earth[END_REF]. The heavy Fe isotope composition displayed by the ureilites is striking because the early Solar Nebula was well homogenized for Fe isotopes [START_REF] Schoenberg | Modes of planetary-scale Fe isotope fractionation[END_REF][START_REF] Wang | Homogeneous distribution of Fe isotopes in the early solar nebula[END_REF], and because the Fe isotope compositions of the bulk silicate portions of most differentiated bodies investigated so far are chondritic [e.g., the Earth [START_REF] Craddock | Abyssal peridotites reveal the near-chondritic Fe isotopic composition of the Earth[END_REF], Mars and Vesta [START_REF] Poitrasson | Iron isotope differences between Earth, Moon, Mars and Vesta as possible records of contrasted accretion mechanisms[END_REF][START_REF] Weyer | Iron isotope fractionation during planetary differentiation[END_REF][START_REF] Schoenberg | Modes of planetary-scale Fe isotope fractionation[END_REF][START_REF] Wang | Iron isotope fractionation in planetary crusts[END_REF], and probably the Moon [START_REF] Weyer | Iron isotope fractionation during planetary differentiation[END_REF][START_REF] Halliday | Small differences in sameness[END_REF]]. One noteworthy exception is the angrite parent body whose lavas display high  56 Fe ratios (≈ 0.12 ‰, [START_REF] Wang | Iron isotope fractionation in planetary crusts[END_REF].

Discussion

Iron inventory in ureilites

Iron in ureilites is hosted by many phases: silicates (olivine and pyroxenes), metal, sulfides, phosphides, carbide (cohenite), and their weathering products (Fig. 1). It is very difficult to obtain a reliable picture of the iron budget in ureilites from modal analyses. [START_REF] Goodrich | Petrology of chromite in ureilites: Deconvolution of primary oxidation states and secondary reduction processes[END_REF] estimated that ureilites contain ~1-3 vol.% of grain boundary metal, and less sulfides, in agreement with the abundances of metal and sulfur determined by chemistry (Vdovykin, 1970;[START_REF] Wänke | The chemistry of , 572-590Haverö ureilite[END_REF][START_REF] Hintenberger | The antarctic meteorite Yamato 74123a new ureilite[END_REF][START_REF] Yanai | Catalog of the Antarctic meteorites[END_REF]. We used the standard wet chemical analyses compiled by [START_REF] Yanai | Catalog of the Antarctic meteorites[END_REF] to estimate the proportions of Fe hosted by silicates, metal and troilite in ureilites (Fig. 4). Our calculations indicate that silicates are the main hosts of Fe, and represent more than 70 % of the budget of this element. Metal (14-29 % of the Fe budget) and sulfides (< 8 % of the Fe budget) are clearly subordinate hosts. These conclusions are strengthened by the striking correlation between the Mg# numbers (=100 x Mg/(Mg + Fe), atomic) of our samples and the composition of their olivine cores, which are representative of the Mg# numbers of their silicates (Fig. 5).

Origin of the high  56 Fe values in ureilites

The high  56 Fe values in ureilites cannot be the result of terrestrial weathering. Alteration of sulfides in meteorites is accompanied by the release of light Fe. Heavier Fe isotopic compositions have been measured in weathered ordinary chondrite finds compared to fresh falls. However, the increase is rather limited, about +0.07 ‰ of the  56 Fe were observed only for the most weathered L and H chondrites [START_REF] Saunier | Effect of hot desert weathering on the bulk-rock iron isotope composition of L6 and H5 ordinary chondrites[END_REF]. Ureilites are less prone to such an effect because sulfides control here a much lesser proportion of Fe than in L and H chondrites (Fig. 4). Although the alteration of ureilite finds is pervasive, our samples were not extensively weathered, and still displayed metal and sulfides. Indeed, the fresh fall samples (Novo Urey and Almahata Sitta) display the same isotopic compositions as find samples (Tables 1 and4).

Because the Fe isotopic compositions are certainly pristine, they could be inherited from the materials which accreted to form the UPB, or alternatively they could result of the differentiation of the UPB. A heavy Fe isotopic composition could be observed in bodies that suffered evaporative loss of volatile elements following high-energy impacts during accretion. This explanation was initially proposed for the Earth and the Moon by [START_REF] Poitrasson | Iron isotope differences between Earth, Moon, Mars and Vesta as possible records of contrasted accretion mechanisms[END_REF] but is controversial for these bodies (e.g., [START_REF] Halliday | Small differences in sameness[END_REF]. It is probably valid for the angrite parent body (APB), where a strong depletion in volatile elements such as the alkalis, is accompanied by heavy Fe and Si isotopic compositions [START_REF] Wang | Iron isotope fractionation in planetary crusts[END_REF][START_REF] Pringle | Silicon isotopes in angrites and volatile loss in planetesimals[END_REF]. The UPB did not accrete from volatile depleted materials, because the magmas generated by the melting of its mantle were alkali and volatile-rich as shown by the compositions of the plagioclases in the feldspathic clasts found in polymict ureilites and the geochemistry of ALM-A, a trachyandesitic lava from the UPB (e.g., [START_REF] Cohen | Feldspathic clast populations in polymict ureilites: Stalking the missing basalts from the ureilite parent body[END_REF][START_REF] Bischoff | Trachyandesitic magmatism in the early Solar System[END_REF]. Nevertheless, losses of volatile elements were possible during the disruption of the UPB, as shown previously for Zn [START_REF] Moynier | Volatilization induced by impacts recorded in Zn isotope composition of ureilites[END_REF]. However, the lack of correlation between  56 Fe values and abundances of volatile elements (e.g., Zn, Rb, Li, not shown) rules out this possibility for Fe.

The high  56 Fe values in ureilites cannot be created by either the extraction of silicate melts, or by possible high temperature redox processes. Partial melting does not generate residues with  56 Fe values significantly distinct than those of the initial mantle [START_REF] Weyer | Iron isotope fractionation during planetary differentiation[END_REF][START_REF] Craddock | Abyssal peridotites reveal the near-chondritic Fe isotopic composition of the Earth[END_REF].

High  56 Fe values in bulk rocks are certainly not the result of the reduction reactions that produced the Fe-depleted rims of the olivine during the disruption of the body. Although, the possibility of local isotopic heterogeneities in olivine have not yet been investigated, the possibility of a detectable effect is unlikely: 1/ the relative volume of the Fe-depleted rims is always limited (Fig. 1), and consequently the impact of these rims on the Fe budget is probably negligeable; 2/ the formation of the Fe-depleted olivine rims was a very fast process (e.g., [START_REF] Myiamoto | Cooling history of some Antarctic ureilites[END_REF], and a noticeable metal loss during the post-disruption cooling of the ureilites is not plausible; 3/ ALM-A, the ureilitic trachyandesite, displays a high  56 Fe value which indicates that this feature predates the disruption of the body.

Moreover, the high  56 Fe values displayed by the ureilites are certainly not generated by the processes that produced the striking range of olivine-core compositions. The lack of correlation between the Fe isotope compositions of ureilites and their olivine-core compositions demonstrates that both features are unrelated (Fig. 2).

Alternatively, removal of S-rich metallic melts could explain the high  56 Fe values in ureilites.

When a chondritic assemblage is heated, melting begins with Fe-sulfide (troilite) and metal at a temperature close to 980°C defined by the eutectic in the Fe-FeS system [START_REF] Keil | Thermal alteration of asteroids: evidence from meteorites[END_REF]. Since at this temperature FeS displays a marked enrichment in the lighter Fe isotope (Schuessler et al., 2007, Polyakov and[START_REF] Polyakov | New data on equilibrium iron isotope fractionation among sulfides: constraints on mechanisms of sulfide formation in hydrothermal and igneous systems[END_REF][START_REF] Wang | Iron isotope fractionation during sulfide-rich felsic partial melting in early planetesimals[END_REF], an increase of the  56 Fe value of the solid residue is anticipated. If the metallic melt is efficiently removed from the solid residue (fractional melting), all melting happens at the eutectic until either metal or troilite is exhausted. The effect on the isotopic compositions of the residues can be easily estimated.

The eutectic in the Fe-FeS system contains about 31.6 wt% S and 68.4 wt% Fe (e.g., [START_REF] Kullerud | The Fe-Ni-S system[END_REF], and is equivalent to about 85 wt% troilite +15 wt% metal. We calculate the  56 Fe metallic melt-silicate using these proportions, the  56 Fe troilite-silicate value (-0.21 ‰) selected by [START_REF] Wang | Iron isotope fractionation during sulfide-rich felsic partial melting in early planetesimals[END_REF] and the  56 Fe metal-troilite at ca. 1250 K (+0.1 -+0.2 ‰) estimated from theoretical calculations [START_REF] Polyakov | Equilibrium iron isotope fractionation factors of minerals: reevaluation from the data of nuclear inelastic resonant X-ray scattering and Mössbauer spectroscopy[END_REF][START_REF] Polyakov | New data on equilibrium iron isotope fractionation among sulfides: constraints on mechanisms of sulfide formation in hydrothermal and igneous systems[END_REF]. For the calculations presented in Figure 6, we used a  56 Fe metallic melt-silicate = -0.18 ‰, and assumed that  56 Fe metallic melt-silicate ≈ 56 Fe metallic melt-residue .

The composition of a source after extraction of a S-rich metallic melt at the eutectic were calculated using an isotope mass balance:

 56 Fe source =  56 Fe metallic melt . fFe metallic melt +  56 Fe residue . fFe residue (1) where f is the mass fraction of 54 Fe in the metallic melt or in the solid residue. This equation can be recast in terms of the residue:

 56 Fe source = ( 56 Fe metallic melt-residue +  56 Fe residue ) (1 -fFe residue ) +  56 Fe residue . fFe residue (2) And consequently:

 56 Fe residue =  56 Fe source - 56 Fe metallic melt-residue +  56 Fe metallic melt-residue . fFe residue (3) Calculations show that detectable shift of the Fe isotopic compositions is possible, depending on the initial Fe and S contents of the starting assemblage (Fig. 6). If the starting assemblage is Fe-rich and displays moderate S contents (e.g., like a H chondrite), segregation of the metallic melt produces at best a marginal shift of the isotopic composition of the residue. In contrast, for S-rich chondritic assemblages, an increase of ca. 0.1 ‰ of the  56 Fe can be achieved by this process.

If the metallic melt is not removed from the residue (batch melting), the degree of melting increases with the temperature. The composition of the metallic melt is consequently variable, and the situation becomes much more complex (melting of the silicates, involvement of carbon…). More importantly, fractionation of Fe isotopes between S-rich metallic melts and silicates becomes much less detectable with increasing temperature, and is usually supposed negligible considering our present level of precision, above 1250°C as determined through experimental studies [START_REF] Poitrasson | No iron isotope fractionation between molten alloys and silicate melt to 2000 degrees C and 7.7 GPa: experimental evidence and implications for planetary differentiation and accretion[END_REF][START_REF] Hin | Experimental evidence for the absence of iron isotope fractionation between metal and silicate liquids at 1 GPa and 1250-1300°C and its cosmochemical consequences[END_REF]. [Notice that this view has been recently challenged by [START_REF] Shahar | Sulfur-controlled iron isotope fractionation experiments of core formation in planetary bodies[END_REF] whose results indicate significant fractionations between metallic melts and melted silicates at 1600°C. However, the magnitude and direction of these isotopic fractionations are not consistent with the available analyses on magmatic irons (bulk samples) and the results presented here. A discussion of these results and previous ones is beyond the scope of this paper. Additional works are necessary to confirm these experimental results and to evaluate the effects of B or Sn used in previous experiments.] Siderophile element systematics (e.g. platinum group elements) demonstrate that ureilites experienced separation of S-rich metallic melts, with possibly entrainment of metal [START_REF] Warren | Siderophile geochemistry of ureilite: a record of early stages of planetesimal core formation[END_REF][START_REF] Rankenburg | Highly siderophile elements in ureilites[END_REF]Goodrich et al., 2013b), but the process (batch or fractional) and temperatures of segregation are not well constrained by these data. The heavy Fe isotopic compositions and the low S contents of the ureilites are consistent with the separation of S-rich metallic melts (Fig. 6), and indicate that the removal of these melts was efficient probably at a temperature close to the eutectic, and certainly before the onset of the melting of the silicates (< 1100 °C). Indeed, ALM-A, the sole ureilitic lava sample available at present [START_REF] Bischoff | Trachyandesitic magmatism in the early Solar System[END_REF], displays a high  56 Fe value (Fig. 3), similar to the highest values measured on ureilites. Although partial melting can generate melts with Fe isotopic compositions heavier than their sources as exemplified by terrestrial basalts (e.g., [START_REF] Teng | Iron isotopic systematics of oceanic basalts[END_REF], this effect is negligible for mantles with low Fe 3+ /Fe total ratios [START_REF] Dauphas | Magma redox and structural controls on iron isotope variations in the Earth's mantle and crust[END_REF], like ureilites (Goodrich et al., 2013c). Consequently, ALM-A strongly suggests that the UPB's mantle acquired its specific isotopic composition early, before it was heated enough to generate magmas.

Comparison with mantles of other small bodies.

The heavy Fe isotopic compositions displayed by ureilites contrast with those of the brachinites and the olivines from the Main Group Pallasites, the samples of the other asteroidal mantles for which data are available (Fig. 3). Brachinites were possibly derived from multiple parent bodies. They are mantle restites similar to ureilites, but are more ferroan than the latter [START_REF] Keil | Brachinite meteorites: Partial melt residues from an FeO-rich asteroid[END_REF].

GRA 06128/29, a brachinitic melt shows a light Fe isotopic composition ( 56 Fe = -0.08 ± 0.06 ‰), which was ascribed to the involvement of Fe-sulfides during magma genesis [START_REF] Wang | Iron isotope fractionation during sulfide-rich felsic partial melting in early planetesimals[END_REF].

Probably because of the high Fe content of their protoliths, the average composition of the brachinites does not deviate from the chondritic value ( 56 Fe = 0.01 ± 0.02 ‰, n=7, [START_REF] Wang | Iron isotope fractionation during sulfide-rich felsic partial melting in early planetesimals[END_REF], although S-rich metallic melts and sulfides were certainly involved during their differentiation [START_REF] Day | Origin of felsic achondrites Graves Nunataks 06128 and 06129, and ultramafic brachinites and brachinite-like achondrites by partial melting of volatile-rich primitive parent bodies[END_REF]. Main Group Pallasites (MGPs) are mixtures of mantle-derived olivines and core-derived metal from a single disrupted asteroid [START_REF] Greenwood | Oxygen isotope variation in stony-iron meteorites[END_REF]. The homogeneous O isotopic composition of these pallasites provides a strong evidence for an early extensive melting event on their parent body, possibly resulting in the formation of a global magma ocean [START_REF] Greenwood | Oxygen isotope variation in stony-iron meteorites[END_REF]. Fe isotopes in MGPs cannot record the early stages of core segregation, because the temperatures reached during the global melting event were too high (>> 1300°C) to fractionate these isotopes between silicates and metallic melts. Indeed, the average Fe isotopic composition of olivines ( 56 Fe = 0.009 ± 0.014 ‰, n=11, [START_REF] Weyer | Iron isotope fractionation during planetary differentiation[END_REF] is indistinguishable from the chondritic average.

Conclusions

The marked S depletions, the siderophile element abundances and the high  56 Fe values displayed by ureilites point to an efficient segregation of S-rich metallic melts in the body, more likely before the onset of silicate melting. Although the UPB has been disrupted, samples of its core have not yet been recovered. The lack of such samples is not surprising because the number of meteorites from the UPB is still limited. The fact that only one large clast of ureilitic lava is actually known demonstrates that our ureilite meteorites provide only a partial sampling of the whole parent body.

The percolation of S-rich metallic melts has been extensively investigated in a solid matrix rich in olivine, analogous to an asteroidal mantle. Metallic melts over a percolation threshold ranging from about 6 vol% to 18 vol% [START_REF] Yoshino | Core formation in planetesimals triggered by permeable flow[END_REF][START_REF] Bagdassarov | Constraints on the Fe-S melt connectivity in mantle silicates from electrical impedance measurements[END_REF][START_REF] Watson | Connectivity of core forming melts: Experimental constraints from electrical conductivity and X-ray tomography[END_REF] can potentially create a stable interconnected network in an olivine-rich solid matrix that could have allowed the early segregation of some core material. Yet the permeability of the core forming Fe-FeS melts within a silicate matrix is very low, and it was inferred that percolation of these melts cannot be a major process of core formation in planetesimals [START_REF] Watson | Connectivity of core forming melts: Experimental constraints from electrical conductivity and X-ray tomography[END_REF]. The geochemical data on ureilites seem at odds with previous experiments. Certainly other factors can contribute to the segregation of Fe-FeS melts and to fast core formation in planetesimals, such as shear deformation of the body [START_REF] Bruhn | An interconnected network of core-forming melts produced by shear deformation[END_REF][START_REF] Rushmer | Microsegregation rates of liquid Fe-Ni-S metal in natural silicate-metal systems: A combined experimental and numerical study[END_REF].

Table 1. Details of meteorite samples studied. Olivine compositions are from this study except D [START_REF] Downes | Evidence from polymict ureilite meteorites for a disrupted and re-accreted single ureilite parent asteroid gardened by several distinct impactors[END_REF], SG (Singletary and Grove, 2004), G [START_REF] Goodrich | NWA 1500: plagioclase-bearing monomict ureilite or ungrouped achondrite? Meteorit[END_REF], and WR [START_REF] Warren | Pyroxene-selective impact smelting in ureilites[END_REF] [START_REF] Singletary | Early petrologic processes on the ureilite parent body[END_REF], [START_REF] Goodrich | NWA 1500: plagioclase-bearing monomict ureilite or ungrouped achondrite? Meteorit[END_REF][START_REF] Goodrich | Petrology of chromite in ureilites: Deconvolution of primary oxidation states and secondary reduction processes[END_REF] and references therein).

Figure 3. Iron isotope frequency distributions of chondrites [START_REF] Craddock | Abyssal peridotites reveal the near-chondritic Fe isotopic composition of the Earth[END_REF], ureilites and ALM-A, a ureilitic lava (this work), brachinites and GRA 06128/9, a brachinitic melt [START_REF] Wang | Iron isotope fractionation during sulfide-rich felsic partial melting in early planetesimals[END_REF], and olivines from main group pallasites [START_REF] Weyer | Iron isotope fractionation during planetary differentiation[END_REF]. [START_REF] Wasson | Compositions of chondrites[END_REF]. The S abundance in the average ureilite is from [START_REF] Warren | Siderophile geochemistry of ureilite: a record of early stages of planetesimal core formation[END_REF]. A protolith with S and Fe abundances similar to average CM can account for the ureilite features, but this solution is not unique. Notice that the protolith of the ureilites is not a known carbonaceous chondrite [START_REF] Warren | Stable isotopes and the noncarbonaceous derivation of ureilites, in common with nearly all differentiated planetary materials[END_REF].

Figure 1 .

 1 Figure 1. Maps of Mg, Fe and S of Northwest Africa 8049, a typical ureilite, showing the zoning of the olivine grains (Ol), the homogeneity of the pigeonite (Pg), and the repartition of metal (Mt -mainly interstitial and in tiny inclusions into olivine) and sulfides (Sulf.).

Figure 2 .

 2 Figure 2. Plots of molar Fe/Mn ratios in olivine cores,  17 O (Clayton and Mayeda, 1996) and  56 Fe in the bulk rocks vs. the composition of the olivine cores (data from Table 2, and mainly from Downes et al. (2008),

Figure 4 .

 4 Figure 4. Ternary diagram showing the proportions of Fe hosted in the silicates, in metal and in sulfides for ureilites and non-carbonaceous chondrites (the proportions were estimated from the analyses compiled by Yanai et al.,1995).

Figure 6 .

 6 Figure 6. Evolution of the isotopic compositions of residual lithologies as a function of the extraction of a Fe-FeS melt with a eutectic composition. Starting assemblages have a  56 Fe = 0 ‰ and the Fe and S contents of selected chondritic types[START_REF] Wasson | Compositions of chondrites[END_REF]. The S abundance in the average ureilite is from

  .

		Source	# or split	Ol. core	bulk rock
				Fo %	Mg#
	Fall
	Novo Urei	Saint Louis		78.5 SG
	Hot desert finds
	El Gouanem	ENS Lyon		80.7 SG
	NWA 2236	NIPR	,41	96.8	91.8
	NWA 4471	JAB		78.1	70.2
	NWA 4509	ENS Lyon		78.5
	NWA 4511	ENS Lyon		77.8	77.2
	NWA 4512	ENS Lyon		78.3
	NWA 4513	ENS Lyon		90.5
	NWA 4516	ENS Lyon		81.3	76.9
	NWA 5555	JAB		90.8	87.3
	NWA 5602	JAB		79.0	76.5
	NWA 5884	JAB		78.6	75.0
	NWA 6056	JAB		84.8	81.4
	NWA 7349	JAB		76.5	75.4
	NWA 7630	JAB		79.1	73.4
	NWA 7686	JAB		91.0	84.3
	NWA 7880	JAB		78.6	77.7
	NWA 8049	JAB		84.3	81.3
	Antarctic finds
	A-881931	NIPR	,65	78.7	76.5
	ALHA 77257	NIPR	,104	86.1 D	83.7
	ALH 82130	MWG	,43	95.2 D	89.9
	EET 83225	MWG	,37	88.3 D	85.5
	LAP 03587	MWG	,10	74.7 WR	74.1
	LAR 04315	MWG	,46	81.9 WR	79.1
	MET 01085	MWG	,23	no olivine	84.9
	Y-791538	NIPR	,126	91.3 D	87.1
	Y-981810	NIPR	,88	78.3	74.4
	Other finds
	Kenna	Saint Louis		78.0 G
	Goalpara	Saint Louis		76.6 D
	Ureilitic trachyandesite (Almahata Sitta fall)
	ALM-A	Münster		no olivine

Table 2 .

 2 Olivine core compositions of the samples analyzed during the course of this study (oxides in 488 wt%, Fe/Mn atomic).

	489
	490
		lab.	n	SiO 2 Cr 2 O 3 FeO	MnO MgO	CaO	total	Fo%	Fe/Mn
	A-881931	NIPR 13 38.07	0.49	19.72 0.40	40.78	0.29	99.75	78.7	48.1
	NWA2236 NIPR 28 42.38	0.38	3.21	0.45	53.91	0.28	100.64	96.8	7.0
	NWA 4471 SCMO 10 38.75	0.49	19.92 0.39	39.92	0.29	99.76	78.1	50.1
	NWA 4509 SCMO 10 39.06	0.75	19.73 0.41	40.49	0.37	100.80	78.5	48.0
	NWA 4511 SCMO 10 38.80	0.54	20.32 0.40	40.03	0.31	100.40	77.8	50.7
	NWA 4512 SCMO 10 39.47	0.71	19.75 0.40	39.95	0.37	100.67	78.3	48.2
	NWA 4513 SCMO 10 40.82	0.59	9.08	0.43	48.52	0.30	99.74	90.5	20.8
	NWA 4516 SCMO 10 39.19	0.67	17.38 0.41	42.28	0.32	100.26	81.3	41.5
	NWA5555 SCMO 10 41.07	0.62	8.89	0.45	49.36	0.31	100.72	90.8	19.5
	NWA 5602 SCMO 10 38.60	0.72	19.32 0.41	40.88	0.35	100.28	79.0	46.3
	NWA5884 SCMO 10 39.32	0.70	19.36 0.40	39.92	0.32	100.06	78.6	47.4
	NWA6056 SCMO 10 40.26	0.75	14.27 0.44	44.73	0.39	100.87	84.8	31.8
	NWA 7349 SCMO 10 38.54	0.39	21.55 0.41	39.29	0.23	100.41	76.5	51.3
	NWA 7630 SCMO 10 38.89	0.74	19.26 0.41	40.78	0.33	100.40	79.1	46.6
	NWA 7686 SCMO 10 40.88	0.64	8.65	0.45	49.16	0.34	100.15	91.0	19.1
	NWA 7880 SCMO 10 38.82	0.74	19.69 0.40	40.58	0.28	100.54	78.6	49.2
	NWA 8049 SCMO 10 39.85	0.71	14.66 0.42	44.11	0.37	100.11	84.3	34.5
	Y-981810	NIPR 18 38.64	0.55	20.05 0.39	40.54	0.35	100.51	78.3	50.2
	491
	492

Table 3 .

 3 Iron isotopic compositions of international standards obtained during the course of this study in Saint Louis and in Plouzané, and compared with literature values. (N# = number of runs of the same solution).

		N#	δ 56 Fe	95% C.I.	δ 57 Fe	95% C.I.
	BIR1
	Plouzané, average (n=6)		0.073	±0.017	0.116	±0.013
	Wang et al. (2012)		0.043	±0.016	0.058	±0.026
	Craddock & Dauphas (2011)		0.053	±0.015	0.087	±0.023
	BHVO2
	Plouzané, average (n=14)		0.117	±0.011	0.181	±0.023
	Wang et al. (2012)		0.102	±0.012	0.159	±0.018
	Craddock & Dauphas (2011)		0.114	±0.011	0.174	±0.016
	Allende USNM3529
	Plouzané #1	5	0.015	±0.015	0.014	±0.069
	Plouzané #2	5	0.021	±0.035	-0.019	±0.039
	Plouzané, average (n=2)		0.018		-0.003
	Craddock & Dauphas (2011)		-0.007	±0.012	0.003	±0.019
	AC-E
	Plouzané #1	5	0.316	±0.025	0.501	±0.065
	Wang et al. (2012)		0.313	±0.018	0.457	±0.028
	Craddock & Dauphas (2011)		0.320	±0.010	0.478	±0.015

Table 4 .

 4 Iron isotopic compositions of unbrecciated and trachyandesitic ureilites. 500

		lab.	N#	δ 56 Fe	95% C.I.	 57 Fe	95% C.I.
	Fall
	Novo Urei #1	St Louis	9	0.024	±0.030	0.027	±0.050
	Novo Urei #2	St Louis	9	0.039	±0.038	0.084	±0.057
	Northwest Africa finds
	El Gouanem	St Louis	9	0.068	±0.028	0.085	±0.052
	NWA 2236	Plouzané	6	0.076	±0.017	0.129	±0.047
	NWA 4471	Plouzané	8	0.086	±0.021	0.133	±0.033
	NWA 4509	St Louis	9	0.024	±0.033	0.060	±0.058
	NWA 4511 #1	Plouzané	8	0.051	±0.032	0.073	±0.052
	NWA 4511 #2	St Louis	8	0.023	±0.032	0.062	±0.059
	NWA 4512	St Louis	8	0.046	±0.032	0.082	±0.059
	NWA 4513	St Louis	9	0.054	±0.038	0.098	±0.057
	NWA 4516	Plouzané	9	0.047	±0.030	0.073	±0.044
	NWA 5555	Plouzané	7	0.053	±0.031	0.081	±0.047
	NWA 5602	Plouzané	10	0.043	±0.029	0.065	±0.049
	NWA 5884	Plouzané	7	0.057	±0.017	0.087	±0.028
	NWA 6056 #1	Plouzané	5	0.040	±0.025	0.027	±0.027
	NWA 6056 #2	Plouzané	7	0.054	±0.021	0.080	±0.035
	NWA 7349	Plouzané	8	0.059	±0.018	0.094	±0.027
	NWA 7630	Plouzané	7	0.081	±0.026	0.124	±0.037
	NWA 7686 #1	Plouzané	5	0.090	±0.017	0.117	±0.066
	NWA 7686 #2	Plouzané	7	0.067	±0.029	0.103	±0.047
	NWA 7880	Plouzané	6	0.040	±0.022	0.063	±0.030
	NWA 8049	Plouzané	7	0.067	±0.031	0.109	±0.055
	Antarctic finds
	A-881931	Plouzané	5	0.109	±0.039	0.209	±0.062
	ALHA 77257	Plouzané	6	0.058	±0.030	0.079	±0.057
	ALH 82130	Plouzané	8	0.044	±0.034	0.053	±0.045
	EET 83225	Plouzané	6	0.035	±0.026	0.038	±0.067
	LAP 03587 #1	Plouzané	7	0.035	±0.030	0.075	±0.016
	LAP 03587 #2	St Louis	9	0.055	±0.033	0.082	±0.058
	LAR 04315	Plouzané	6	0.059	±0.029	0.088	±0.057
	MET 01085	Plouzané	6	0.023	±0.023	0.036	±0.055
	Y-791538	Plouzané	6	0.071	±0.018	0.121	±0.045
	Y-981810	Plouzané	5	0.077	±0.015	0.125	±0.073
	Other finds
	Kenna	St Louis	9	0.009	±0.030	0.030	±0.049
	Goalpara #1	St Louis	9	0.048	±0.030	0.095	±0.049
	Goalpara #2	St Louis	9	0.056	±0.038	0.092	±0.057
	Ureilitic trachyandesite
	ALM-A #1	Plouzané	5	0.095	±0.032	0.155	±0.066
	ALM-A #2	Plouzané	4	0.096	±0.019	0.107	±0.045
	ALM-A #3	Plouzané	8	0.105	±0.023	0.159	±0.038

Acknowledgements: Many samples analyzed during the course of this study were kindly provided by the National Institute of Polar Research and the NASA meteorite working group. US Antarctic meteorite samples are recovered by the Antarctic search for Meteorites (ANSMET) program which has been funded by NSF and NASA, and characterized and curated in the department of Mineral Sciences of the Smithsonian Institution and Astromaterials Curation Office at NASA Johnson Space Center. We thank Bernard Marty for the editorial handling, Hilary Downes, Franck Poitrasson, and an anonymous reviewer for their fast and constructive comments, Albert Jambon, Mathieu Roskosz and Richard Greenwood for discussions, and Pascale Barrat for her help. We gratefully acknowledge the Programme National de Planétologie (CNRS-INSU) for financial support.