dataesr/scientific-paragraphs-categorization huggingface dataset:
This article details the bias correction scheme and the error estimation scheme used in the data assimilation system for those data. This is a necessary step to have a positive impact of SSS data assimilation. The structure of this article is as follows: after a description of the OSE where the operational system, the bias correction, the SSS observation error and the presentation of the experimental design are described in section 2, the effect of the SMOS SSS data assimilation is presented in section 3, while discussions and conclusions are provided in section 4.
OSE approach The OSE are conducted with the global ¼° ocean analysis and forecasting system running in real time at Mercator Ocean. Detailed descriptions of the system can be found in  (Lellouche et al. , 2013; Lellouche et al. , 2018). After a brief description of the system configuration, we will describe the data assimilation components that were specifically developed or adapted for the SSS data assimilation in details.
This correction has been inferred from the comparison between the Remote Sensing Systems (RSS) Passive Microwave Water Cycle (PMWC) product  (Hilburn, 2009)  and the IFS ECMWF precipitation  (Lellouche et al. , 2013). A monthly river runoff climatology is built with data on coastal runoff from 100 major rivers from the  Dai et al. (2009). This database uses new data, mostly from recent years, streamflow simulated by the Community Land Model version 3  (Verstentein et al. , 2004)  to fill the gaps, in all land areas except Antarctica and Greenland. At high latitudes the effect of iceberg melting is also parameterized. The lack of interannual variability of the largest rivers is known to lead to large errors in the surface ocean salinity in the analysis and forecast. There is no SSS relaxation term to any climatology as it is the case in operational conditions.
Regular observation data All ocean observations assimilated in the real time forecasting system are assimilated in the same way in the OSEs presented here. Along track SLA observations distributed by CMEMS (http://marine. copernicus. eu/) referenced to an unbiased Mean Dynamic Topography (MDT) based on the CNES/CLS 2013 MDT are used. Gridded satellite SST OSTIA Level 4 (L4: SST analysis using optimal interpolation (OI) on a global 0. 054 degree grid) are assimilated each week in addition to SST measurements from the in-situ database delivered by the CORIOLIS centre (http://www. coriolis. eu. org/). Assimilation of in situ temperature and salinity profiles from this database are mostly from Argo floats, XBT, CTDs, moorings, gliders and sea mammals. The assimilation of those routine observations in the OSEs provides a realistic context for the global ocean observing system so that the experiments address the complementarity of the different data sets with satellite SSS.
SSS from space In this study, we assimilate a SMOS Level 3 (L3: provided on a grid, but with no in-filling) SSS product at 0. 25° resolution. L3 products are qualified (quality controlled) and processed at the Data Production Center (CPDC) of the Centre Aval de Traitement des Données SMOS (CATDS CEC-LOCEAN)  (Boutin et al. , 2017). Compared to Level 2 products (L2: SSS values at the native swath resolution), they benefit from additional corrections. These are 18-day products sampled at 25km resolution provided every 4 days (the precise description of the time filtering is in the documentation at http://www. catds. fr/Products/Available-products-from-CEC-OS/L3-Debiased-Locean-v2). We checked that this temporal resolution fits well the model resolution and the weekly analysis window used in the assimilation scheme, see next section.
Data Assimilation Scheme The assimilation scheme implemented in the real time Mercator Ocean systems is based on a reduced order Kalman Filter called SAM2 (Système d'Assimilation Mercator V2) and is described in  Lellouche et al. , (2013)  and  Lellouche et al. , (2018). As in the operational ocean forecasting system, we use a weekly assimilation cycle with an analysis date on the fourth day of the week.
OSE design Two parallel simulations were produced, the REF experiment and the SMOS experiment (hereafter SMOSexp) see Table  2. The only difference is the assimilation of the SSS SMOS observations. Both experiments begin in January 2014 from the same initial conditions coming from a previous reanalysis using only the bias correction of T/S without any data assimilation. The period covers the onset and development of the El-Niño 2015 event. The length of the OSE should at least cover one year, more if possible, as it takes 3 months for the system to be in equilibrium with the new data assimilated. This "adjustment" period is longer for observations deeper in the ocean (below the thermocline). Here, up to 2-year simulations are analyzed  [January 2014 -March 2016]. The comparison between the two simulations highlights the impact of the SSS data assimilation on the ocean circulation and the comparison to the other observations (independent or not) will allow us to verify the coherency between the different observation networks and the way they are assimilated.
Evaluation of the analysis toward independent observations We now compare the analysed fields to independent observations, i. e. withheld from all assimilation experiments. This will allow verifying that the changes in the physical fields induced by the SMOS data assimilation are in agreement with external sources of information. For this purpose, the TAO mooring (salinity) observations and the reprocessed TSG data from the French SS Observation Service were withheld from all experiments. This is therefore a fully independent validation.
Comparisons to TAO mooring TAO moorings deliver high frequency measurements at fixed locations. Such platforms allow us to look at high frequency variability that is not captured by drifting platforms. The hourly analysed salinity is collocated at the TAO mooring positions for the REF and SMOSexp simulations. Figure  15  shows the time evolution of TAO salinity observations (valid at 1 m depth) at three mooring locations in the equatorial Pacific (warm pool, cold tongue and salt front) compared to the model (analysis) for the REF and SMOSexp OSE experiments at the first level (~0. 5 m depth). Assimilated SMOS data have also been added. In this example, the salinity evolution of the REF experiment (in green) appears less correlated with the TAO salinity mooring observations (black dots). The SMOSexp simulation shows a better agreement, except for some strongly variable events.
The differences between the SMOSexp simulation and TAO non-assimilated observations are most of the time less than 0. 1 pss. The high frequency variability seen in the observations is also reproduced in the assimilative simulations, with a better agreement when SMOS data are assimilated, except during some specific periods. Tang et al. , (2017) also found some disagreement among the TAO, SMAP/SMOS, and Argo analysis during short periods. There is an improvement in the cold tongue during the end of summer, in fall 2015 and during the last 2 months of the SMOS simulation (Figure  15a ). The data assimilation of SMOS reduces the freshening in this region. Globally, an improvement occurs also in the warm pool (15b) over the entire period. One interesting feature is that when TAO mooring data are missing during a long period near the salt front, the SSS from the SMOSexp experiment is different but closer to TAO mooring when measurements come back (Figure  15c ).
Obviously, the time-series of the assimilated SMOS data is smoother but are able to capture the large scales variability. This also shows the level of accuracy we need to capture higher variability. The precipitation rate superimposed on the SSS proves that it is not the only process that plays a role in the salinity variability. Indeed, a high precipitation rate does not induce necessarily a strong freshening at the sea surface where advection, vertical mixing and SSS SMOS data assimilation can counteract its effect. This also shows that the observation error is not necessarily increased locally depending on the precipitation. These three examples show a positive impact but it is also interesting to have a global view of all TAO moorings over the 2015/2016 El-Niño event. As in  (Martin et al.
, 2018) , Figure  16  shows the differences in RMSD from hourly TAO mooring salinity values at 1 m depth calculated over the period 1 st Jan 2014 to 16 th March 2016. The impact of the SMOS assimilation is contrasted by showing negative (positive) values which indicates that it reduces (increases) the RMSD. The impact is positive and more significant in the western Tropical Pacific near the dateline and in the western Pacific up to 5°N. The impact is quite neutral and even negative in the eastern tropical Pacific (140°W-110°W) between 2°S and 2°N where generally (i) the SMOS bias is larger (Figure  3b ), (ii) there are few in-situ SSS data (Figure  2 ) and (iii) where the observation error is larger (Figure  5 ). Actually, the impact of SMOS SSS assimilation is larger in the ITCZ and SPCZ regions as shown also in the Figure  9.
This reflects the tendency that the SMOS data assimilation reduces the low salinity biases by mitigating the overestimation of E-P in the regions of large precipitation. Finally, during the El-Niño 2015/2016 event, there is a small positive impact overall from the SMOS assimilation with a reduction in RMSD from 0. 326 to 0. 316 pss (about 3%).
Discussion and conclusions The L3 SMOS CATDS data used in this study is considered as an "unbiased" product. Yet, they still contain some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenge of this study was to estimate the residual SSS bias and a suitable observation error for the data assimilation system. It was made possible by using a 3D-Var bias correction scheme and an analysis of the residuals and errors with a statistical technique  (Desroziers et al. , 2005). The "debiased" data could then be assimilated by the SAM2 assimilation scheme which relies on the unbiased hypothesis. The bias estimated by the ocean forecasting system can also be used to correct the L3 SMOS CATDS data for other purposes. The system was carefully tuned and tested to efficiently assimilate the new SSS observations before running the longer simulations that are analyzed here.
The proper specification of the observation operator and error covariance matrix were also based on discussions with the data provider. This study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance. Nevertheless, there is still room for improvement. For instance, we used a zonal error as input to the error estimation with the Dezroziers technique. It could be beneficial to take into account the smaller scales linked to a shallow stratification that arises with strong precipitations and/or river runoff. The SMOS data need accurate in-situ data (not only at the surface) to correct their own biases and estimate a suitable error (including data/system representativity). When enough accurate SMOS data are available, they really act as a gap filler.
There is a clear impact on the scales about 1°-2°. This can be seen on the Figure  12  (Hovmöller), and additional spectral analyses (not shown) confirm this finding. So, it is important for future satellite SSS to provide a good accuracy at those scales. It also shows that background error correlation length-scales used in the bias correction scheme could be optimized with an improvement of the in-situ network and the SSS SMOS accuracy. Globally, the SSS data assimilation slightly improves the simulation compared to a simulation assimilating only observations of in situ, SST and SLA data. It highlights that no incoherent information was brought by the SSS data compared to the other assimilated observations. When looking at the impact of the SMOS SSS assimilation, we found a positive impact in salinity with respect to in-situ data over the top 30 meters.
The root mean square error (RMSE) of in-situ surface salinity is reduced in all regions of the Tropical Pacific and is very often close to 0. 15 pss. The improvement varies depending on the region and can reach 10% in the North Tropical Pacific where the SSS anomaly is the strongest. Comparisons to independent TAO/Triton data corroborate the fact that the impact of SMOS SSS assimilation is larger in the ITCZ and SPCZ regions. This also reflects that the overestimation of E-P is mitigated by the data assimilation through salting in regions of large precipitations. There is little impact on the SST. For instance, the area of the SST warmer than 28. 5°C (warm pool region) was little affected. It means that the local impact on the air-sea coupling is negligible. But, an impact on TIW have been seen through SSH fields.
, 2014). A longer ocean reanalysis with continuously improved SSS SMOS (available for over 9 years) and SMAP (available since 2015) data could bring new information on the water cycle. The focus of this study was on the tropical Pacific. But the system is global, and, in spite of RFI pollution near some coasts, we found clear improvements near the Amazon and the Rio Del Plata river plumes. So, the benefit from assimilating SMOS SSS is not restricted to the equatorial band. Its positive impact near the mid-latitudes major rivers is a chance to better monitor the strengthening of the water cycle  (Durack, 2015). In-situ at sea surface XBT, moorings, Argo floats, sea mammals 0. 03°C and 0. 0075 pss.
Acknowledgements We gratefully acknowledge funding from  ESA  as part of the  SMOS-Niño15  project, coordinated by  C. Donlon. We also thank the providers of the datasets used here. J. Boutin  ( LOCEAN/CATDS ) provided the SMOS data and provided useful inputs to understand the nature of the SMOS bias estimates. Sea surface salinity data derived from voluntary observing ships were collected, validated, archived, and made freely available by the  French Sea Surface Salinity Observation Service  (http://www. legos. obs-mip. fr/observations/sss/). Thanks to the  GTMBA  Project Office of  NOAA/PMEL  to provide TAO/TRITON mooring data. We would also like to acknowledge  Matthew Martin  ( MetOffice ) for his careful reading of the manuscript and his comments which were very helpful. We would also like to acknowledge the contribution of one anonymous reviewer whose suggestions improved this paper significantly.
We refer to Land Data Assimilation Systems (LDASs) as the framework where LSMs are driven by and/or ingest such observations generating enhanced estimates of the land surface variables (LSVs)  [10]. Several LDASs now exist from point to regional scale, amongst them are the Global Land Data Assimilation System (GLDAS,  [19] ), the Carbon Cycle Data Assimilation System (CCDAS,  [20] ), the Coupled Land Vegetation LDAS (CLVLDAS,  [21, 22] ) and more recently the U. S. National Climate Assessment LDAS (NCA-LDAS,  [10] ) as well as LDAS-Monde  [7, 18]  to name a few. These LDASs either optimize process parameters (e. g. , CCDAS), state variables (e. g. , GLDAS, NCA-LDAS, LDAS-Monde) or both (e. g. , CLVLDAS). Assimilated Earth Observations (EOs) generally include satellite retrieval of surface soil moisture  [5, 8, [23] [24] [25] , snow depth  [26] [27] [28] [29]  and snow cover  [9, 27, 30, 31] , vegetation  [7, 18, [32] [33] [34] [35] , as well as terrestrial water storage  [36] [37] [38].
Few studies have included multiple remote sensing measurements. For instance, Kumar et al. [10]  assimilates various remote sensing measurements of the terrestrial water cycle within the NCA-LDAS over the USA while LDAS-Monde  [7, 18]  considers the joint assimilation of vegetation (Leaf Area Index, LAI) and surface soil moisture (SSM) measurements. LDAS-Monde is a sequential land data assimilation system with global capacity. It has been evaluated over various domains at various spatial resolutions including France at 8 km scale  [33, 39]  forced by the SAFRAN reanalysis of Météo-France (Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige  [40, 41] , Europe at 0. 5 • × 0. 5 •  [18, 35]  forced by ERA-Interim atmospheric reanalysis from the European Centre For Medium Range Weather Forecast (ECMWF)  [42] ; North America  [7]  and Burkina-Faso in western Africa at 0.
25 • × 0. 25 • [43] forced by ERA5 atmospheric reanalysis  [44]. In those studies, analysis impact was successfully evaluated using several datasets such as (i) in situ measurements of soil moisture (ii) agricultural statistics, (iii) river discharge, (iv) independent flux estimates related to vegetation dynamics (evapotranspiration, Sun-Induced Fluorescence (SIF) and Gross Primary Productivity (GPP)). Albergel et al. [7]  highlighted LDAS-Monde capacity to better characterize agricultural droughts (spatial area and intensity) than an open-loop counterpart (i. e. model without any assimilation of satellite derived measurements) over the continental United States of America. They found that LDAS-Monde can provide improved initial conditions to initialize forecast and that its impacts persist through time.
In the above mentioned study, LDAS-Monde satellite-derived surface soil moisture dataset (ESA CCI SSM,  [45] [46] [47] [48] ) along with satellite derived LAI (GEOV1, http://land. copernicus. eu/global/ last access on June 2018), were jointly assimilated leading to a quarter degree spatial resolution reanalysis of the LSVs over 2010-2016. Stemming from previous work  [7] , the present study investigates the capability of LDAS-Monde to represent the impact of the summer 2018 heatwave in Europe on vegetation. Spring and summer 2018 in Europe were marked by unusually hot weather that had led to record-breaking temperatures in many countries across Northern and Central Europe. According to ECMWF, near-surface air temperature anomaly in Europe in the period of April to August, calculated with respect to the 1981-2010 average for those months, was much larger in 2018 than in any previous year since 1979  [49].
Several LAI collections/versions are available from the CGLS project from 1999. They are retrieved from the SPOT-VGT (from 1999 to 2014) and then from PROBA-V (from 2014 to present) satellite data according to the methodology proposed by  [72]. This study makes use of the GEOV2, 1km spatial resolution and 10-day steps in near real time product. Its development has followed several steps including (1) applications of a neural network for providing instantaneous estimates from SPOT-VGT reflectances, (2) a multi-step filtering approach to eliminate contaminated data (e. g. , affected by atmospheric effects and snow cover), and (3) temporal techniques for ensuring consistency and continuity as well as short term projection of the product dynamics  [73]  (LAI Product User Manual, https://land. copernicus.
ECMWF Atmospheric Forcing LDAS-Monde is driven by near-surface meteorological fields from both ECMWFs' reanalysis, ERA5, released in 2018, as well as its high resolution operational high resolution weather analysis and forecasts (HRES). ERA5 underlying model and data assimilation system are very similar to that of the operational weather forecast. ERA5 production cycle (IFS Cycle 41r2) is still close to that of the HRES (IFS Cycle 41r2 to 43r3 from 2016 and 45r1 from June 2018, more information at https://www. ecmwf. int/en/forecasts/documentation-and-support/changes-ecmwf-model, last access January 2019). The main difference between the two is the horizontal resolution with 31 km in ERA5 and 9 km in HRES. Another difference is the data assimilation time window which is from 21:00 UTC to 09:00 UTC in ERA5 and from 21:00 UTC to 03:00 UTC in HRES, as it allows more observations to be assimilated in ERA5.
The shorter time window in HRES is due to ECMWF operational constraints to deliver timely forecasts. The ERA5 forcing data includes the lowest model level (about 10-meters above ground level) air temperature, wind speed, specific humidity and pressure and the downwelling fluxes of shortwave and longwave radiation as well as precipitation partitioned in solid and liquid phases. ERA5 forcing data is extracted from the forecasts initialized daily at 00:00 UTC and 12:00 UTC using the hourly forecasts from +1 to +12 h. HRES forcing data is extracted from the forecasts initialized at 00:00 UTC and 12:00 UTC also using the forecasts from +1 h to 12 h. The same downwelling fluxes as in ERA5 are used but for HRES 2-meter temperature and dewpoint temperature, 10-meters wind-speed are extracted. Specific humidity is then calculated from 2-meter temperature and dewpoint temperature.
1 • × 0. 1 • (HRES). Despite the spatial resolution, ERA5 being a recently released dataset, its production cycle (IFS Cycle 41r2) is still close to that of the HRES (IFS Cycle 41r2 to 43r3 from 2016 and 45r1 from June 2018). At the ERA5 spatial resolution, large scale, long time experiments are computationally affordable, and HRES can be used to focus on specific domains or events. Vegetation outputs from this set of 4 experiments (assimilation of SSM and LAI as well as their model counterpart, i. e. open-loops without assimilation) are then evaluated. Vegetation from another experiment (model only, without assimilation) is evaluated: ISBA forced by ERA5 down-scaled to HRES spatial resolution (from 0. 25 • × 0. 25 • to 0. 10 • × 0. 10 • ) for 1 year (2017). Additionally to the LDAS-HRES analysis experiment, daily forecast experiments with 8-day lead time (from LDAS-HRES analyzed initial conditions) were also performed over 2018.
Monitoring the Heatwave Impact on Vegetation Using LDAS-Monde LDAS-Monde, being an offline reanalysis of the land surface variables, is forced by atmospheric datasets: ERA5 and HRES in this study. Using both datasets to force LDAS-Monde produces a long reanalysis of the LSVs (from the use of ERA5) with real-time and even forecast capacity (from the use of HRES). As ERA5 is available over a large temporal extent (from 2000 at the time of study) it can be useful to study recent climate changes. Anomaly time-series of air temperature and precipitation from ERA5 are presented in Figure  3. While it is not our intention to repeat the study from  [49]  on predicting the summer 2018 heatwave, Figure  3  highlights that the April to August period in 2018 exhibits rather large positive anomaly values of air temperature (Figure  3a ) with July 2018 being the highest value observed between January 2001 and October 2018.
While microwave remote sensing provides a way to quantitatively describe the water content of a shallow near-surface soil layer,  [76] , the variable of interest for applications in short-and mediumrange meteorological modeling and hydrological studies over vegetated areas is the root-zone soil moisture content which controls e. g. , plant transpiration  [68]. Similarly, estimates of above-ground biomass might be more useful than LAI for applications linked to agriculture. Integration of these satellite derived datasets into LSMs through data assimilation is, therefore of paramount importance to improve monitoring accuracy of extreme events impacts on LSVs. Not only the representation of LAI and SSM in such system would be improved but other model variables will benefit from the assimilation through biophysical processes and feedback in the model too  [7, 10, 18, 77].
Similarly, estimates of above-ground biomass might be more useful than LAI for applications linked to agriculture. Integration of these satellite derived datasets into LSMs through data assimilation is, therefore of paramount importance to improve monitoring accuracy of extreme events impacts on LSVs. Not only the representation of LAI and SSM in such system would be improved but other model variables will benefit from the assimilation through biophysical processes and feedback in the model too  [7, 10, 18, 77].