data flow started in January 2010 First 6 months to be used with care

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SMOS Mission Launched in 2009 data flow started in January 2010 First 6 months to be used with care Tests dual pol full pol Out gassing and calibration issues Maximum RFI environment Several

1 SMOS Mission Launched in 2009 data flow started in January 2010 First 6 months to be used with care Tests dual pol full pol Out gassing and calibration issues Maximum RFI environment Several re-processings New measurements & new instrument -> wax and strings, trial and error approach to overcome the unexpected! Many improvements from V3 to V6, V7 underway! Availability of L2 and L3 -L4, new products in the making

5 hours after sensing Rodriguez-Fernandez et al.

2 Near Real Time SM Training on SMOS Level 2 v620 SM Similar performances (slightly better indeed) Much faster! Less than 3.5 hours after sensing Rodriguez-Fernandez et al. (2017, HESS) Implemented by : With support by : Delivered to : Disseminated by:

3 V650 is ready! All archive (2010 july 2017) reprocessed Soon to start in the operational processor Satellite Averaged Soil Moisture SM values Validation over and Application Jan/Apr/Jul/Oct Workshop Wien from September Jul to Apr-2017 YHKerr (28 months)

First step: Calibration Comparison at TB level are conducted over various surfaces of known temperatures Galactic background Ice sheet (Antarctica) Ocean bodies Or other similar sensors

4 First step: Calibration Comparison at TB level are conducted over various surfaces of known temperatures Galactic background Ice sheet (Antarctica) Ocean bodies Or other similar sensors Based on near simultaneous, iso geometry Land Ocean Ice core around Dome Concordia SMOS view of the Galaxy Compared TBs are ToA, without reflexion foreign source corrections (gal, sun, moon)

62 Aquarius v4 45 185.03 213.40 1.01-0.98 SMAP R12170 40 187.67 212.46-0.88 0.41 SMAP R13080 40 186.17 210.08-2.38-1.

5 SMOS and DomeX Long term stability Overall good agreement Incidence induced bias between SMAP and Aquarius F. Cabot Sensor Version Inc TBH TBV DTBH DTBV Aquarius v Aquarius v Aquarius v SMAP R SMAP R SMOS v SMOS v SMOS v SMOS v DOMEX DOMEX

6 SMOS -SMAP, Successful Retrievals Monthly Animation: SMAP SMOS Mahmoodi A. SMOS-SMAP

7 L3 SM, SMOS Successful Rets & SMAP Recommended Rets BIAS RMSE Corr unb RMSE Mahmoodi A.

8 Step 2 Validation Done over different targets Sparse networks (Scan, SNOTel, ) Dense networks USDA Watersheds, OziNet, Hobe, Specific, complicated targets and core sites Rugged terrain (Alps) Boreal areas (Sodankyla) Tropical forest (Chaco) Satellite data different sensors (SMAP, Aquarius, AMSR, ) Different retrieval approaches (RT model, Neural networks, simplified approaches,.) Model outputs Note that the retrieval approaches are all global not point to point Note that when using a large number of sites the total distribution of quality metrics should be studied instead of just quality metrics averages

9 Step 2 Validation Issues to be tackled Surface area of ground measurements (representativeness) HOBE like set ups Cosmos probes Spatio-temporal dimensions Beatriz Molero Rodenas study Organic soils Specific calibrations Quality of measurements Ground measurements have own uncertainties and are never truth Models might be wrong over specific ecosystems Algorithms point to point or regional algorithms

10 Models and proxy sensors give erroneous estimates A. Mialon Very important region: Hotspot (land feedback to atmosphere, Koster et al., Seneviratne et al.) Very little in situ data to constrain weather models -> Remote sensing

11 SMOS SM used as reference for other instruments AMSR-E retrievals using a neural network trained on SMOS L3 SM ( > ) Long time series Rodriguez-Fernandez et al. (2016. Remote Sensing)

SMOS support to the CCI ESA SMOS/AMSR-E fusion project Neural networks are a promising approach SMOS can be used as reference for re-scaling other instruments SMOS should be inserted into

12 SMOS support to the CCI ESA SMOS/AMSR-E fusion project Neural networks are a promising approach SMOS can be used as reference for re-scaling other instruments SMOS should be inserted into the CCI framework using LPRM CESBIO : extraction and pre-processing of SMOS and ECMWF auxiliary data for their use by the CCI team SMOS now taken into account in CCIv3 (Dorigo et al. 2017)

13 J. Malbeteau Soil Moisture 1 km Morocco DisPATCh-SM actual Soil Moisture SMOS Land Surface Temperature Optic/Thermal Soil Moisture SMOS 40 km / 3 days Land Surface Temperature MODIS (Aqua/Terra) 1 km / 1 day A. Mahmoodi

14 Example of SMOS High Resolution data for irrigation monitoring Molero et al

USING SMOS DATA IN NWP Assimilating SMOS data moderately improves the soil moisture analysis: On average, for more than 400 in situ sites, the performances of the analysed soil moisture fields are

forecasts: SMOS soil moisture (NRT, NN based product) improves the forecast in the Northern Hemisphere RMSE of 36h FC 850 hpa temperature forecasts SLV+SMOS DA (sigmao*3) minus OL SLV+SMOS DA

15 USING SMOS DATA IN NWP Assimilating SMOS data moderately improves the soil moisture analysis: On average, for more than 400 in situ sites, the performances of the analysed soil moisture fields are close (within 2-3 %) to those of the open loop experiment In situ Open Loop SMOS NN SM σ x1 + T2m + RH2m SMOS NN SM σ x3 + T2m + RH2m Analysed surface fields are used to compute atmospheric forecasts: SMOS soil moisture (NRT, NN based product) improves the forecast in the Northern Hemisphere RMSE of 36h FC 850 hpa temperature forecasts SLV+SMOS DA (sigmao*3) minus OL SLV+SMOS DA (sigmao*9) minus OL Red: negative impact Further work assimilating L-Band into NWP, e.g. J. Kolassa: Merging active and passive microwave observations in soil moisture data assimilation, RSE,2017 G. De Lannoy: Assimilation of SMOS brightness temperatures or From: soil moisture retrievals into a land Blue: surface positive model, impact Hydrology and Rodriguez-Fernandez, de Rosnay, Albergel, et al. 2017, Earth ECMWF System ESA Sciences, report 2016 Rodriguez-Fernandez et al. (in prep.)

16 Summary L Band radiometry (SMOS- SMAP and Aquarius) has proven its ability to deliver reliable accurate and absolute (non model scaled) soil moisture fields over the globe even in areas of dense vegetation thanks to its long wavelength Independent retrieval approaches (RT, NN, SCA, SMOS-IC, DCA,..) or completely different instruments provide very similar if not totally similar results (without fiddling our massaging such as trend correction, anomaly etc..) showing the robustness of the measurements High temporal revisit (<14 h on average over the globe) using both SMOS and SMAP Why passive L band? Because of its characteristics and inherent qualities The most appropriate tool as shown by all the products stemming from it Temporal stability and robustness L band radiometry --> proof of concept demonstrated Uniqueness of the measurements hence Many science outstanding results A very large number of operational or pre operational demonstration products BUT No follow on mission currently --> Data gap Need to act now

L-Band radiometry missions Kerr and Al Bitar 2008 2010 2012 2014 2016 2018 2020 2022

(NASA) (100km / 8days / L-Band/ global) Color codes Altimeters L-Band Passive Optical

17 L-Band radiometry missions Kerr and Al Bitar SMOS (ESA CNES) (40 km / 3days / L-band / global ) Aquarius (NASA) (100km / 8days / L-Band/ global) Color codes Altimeters L-Band Passive Optical Thermal Radar Precipitatio n Gravity SMAP (NASA) (10-60 km / 3days / L-band / global) TODAY!

SMOS DATA PRODUCTS Over land Operational/NRT products / Latency < 3 hours Data product Resolution/format Latency Available from DATA ACCESS ESA: http://smos-diss.eo.esa.int/ CATDS www.catds.

NRT/ 3 hours from sensing ESA EUMETCAST WMO GTS Level 2 soil moisture in NRT (based on Neural Network) 15 km (ISEA 4H9 grid), swath based; NETCDF.

Brightness Temperature and Soil Moisture Level 4 fine-scale soil moisture 15 km (ISEA), swath EEF /NetCDF. 25 km, global, EASE- NetCDF 15 km (ISEA), swath EEF /NetCDF.

18 SMOS DATA PRODUCTS Over land Operational/NRT products / Latency < 3 hours Data product Resolution/format Latency Available from DATA ACCESS ESA: CATDS BEC: NRT light: Level 1 brightness temperature 30-50km (N256 Gaussian grid), swath based; BUFR. NRT/ 3 hours from sensing ESA EUMETCAST WMO GTS Level 2 soil moisture in NRT (based on Neural Network) 15 km (ISEA 4H9 grid), swath based; NETCDF. NRT/~4 hours from sensing Science and composite products/ Latency > 3 hours Data product Resolution/format Latency Available from Level 1 brightness temperature Level 2 Soil moisture Level 3 Brightness Temperature and Soil Moisture Level 4 fine-scale soil moisture 15 km (ISEA), swath EEF /NetCDF. 25 km, global, EASE- NetCDF 15 km (ISEA), swath EEF /NetCDF. 25 km, global, EASE- NetCDF 15 km (ISEA 4H9) grid/ 25 km (EASE) grid depending on product. NETCDF 1 km, for Iberian Peninsula; NETCDF 1 km for MODIS Tiles 6-8 hours after sensing 1 d after sensing 8-12 hours 1 d Daily, 3, 9 days, weekly, monthly 2 daily maps (one asc/ one desc) in NRT ESA CATDS(+ stereopolar) ESA CATDS CATDS BEC BEC CATDS (2017) SMOS swath-based L2 soil moisture product. Credits ESA 10-day global composite of L3 soil moisture. Credits CATDS/CESBIO Level 4 CATDS Root Zone Soil Moisture ~25 km (EASE grid version 2); NetCDF Daily, 10 days, monthly CATDS Level 4 Drought Index 25 km EASE 2 grid netcdf Daily, 10 day, Monthly CATDS Freeze and thaw ~25 km (EASE grid version 2); NetCDF, Northern Hemisphere Surface roughness 25 km NETCDF, global Yearly CATDS Daily Demo data set available from FMI Root zone soil moisture in m 3 /m 3. Credits CATDS/CESBIO

19 Acknowledgments This work is funded by the 2016 NASA ROSES call for the Science Utilization of the Soil Moisture Active Passive Mission (SUSMAP) sponsored by the NASA Terrestrial Hydrology Program 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

20 Chan et al (2017) Flow Diagram SMOS SMAP SMOS L1 TB Part 1 L1 gain/offset adjustment SMOS/SMAP L1 TB SMAP L1 TB fine grid resampling on 9 km EASE Grid 2.0 SMAP Ancillary Data SMAP Algorithm Part 2 SMOS/SMAP soil moisture at 9 km (2009 present) 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

Chan et al (2017) Part 2: Soil Moisture Retrieval Good agreement between SMOS and SMAP Slight code differences from current SMAP operational setup (further work needed) Jun 2017 9 km soil moisture

21 Chan et al (2017) Part 2: Soil Moisture Retrieval Good agreement between SMOS and SMAP Slight code differences from current SMAP operational setup (further work needed) Jun km soil moisture using 6 am SMOS TBs with SMAP algorithm and ancillary data Consistent TBs, algorithm, and ancillary data lead to consistent SMOS/SMAP soil Jun 2017 moisture 9 km soil moisture using 6 am SMAP TBs with SMAP algorithm and ancillary data 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

Chan et al (2017) Part 2: Soil Moisture Retrieval 9 km soil moisture retrieval using 6 am SMOS TBs with SMAP algorithm and ancillary data Jun 2017 9 km soil moisture

22 Chan et al (2017) Part 2: Soil Moisture Retrieval 9 km soil moisture retrieval using 6 am SMOS TBs with SMAP algorithm and ancillary data Jun km soil moisture retrieval using 6 am SMAP TBs with SMAP algorithm and ancillary data Jun nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

23 The SMOS soil moisture validation and intercomparison approach and results Yann Kerr, Jean Pierre Wigneron, Beatriz Molero-Rodenas, Rajat Bindlish, Steven Chan, Roberto Fernandez- Moran, Arnaud Mialon, Amen Al-Yaari, Ali Mahmoodi, Simone Bircher, Nemesio Rodriguez-Fernandez, Philippe Richaume, Ahmad Al Bitar and François Cabot CESBIO, INRA ISPA, JPL, GSFC

Before SMOS TB H (K) SMOS TB H (K) Overall SMOS and SMAP in good agreement; SMAP > SMOS (ocean) but SMAP < SMOS

24 Chan et al (2017) Part 1: SMAP TB vs. SMOS TB 6:00 am + 6:00 pm May 2015 Jun 2017 SMAP TB H (K) Before SMAP TB H (K) SMAP TB H (K) SMOS TB H (K) Before Before SMOS TB H (K) SMOS TB H (K) Overall SMOS and SMAP in good agreement; SMAP > SMOS (ocean) but SMAP < SMOS (land) Match SMOS to SMAP to use SMAP s inversion setup (algorithm and ancillary data) 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

25 Chan et al (2017) Part 1: SMAP TB vs. SMOS TB 6:00 am + 6:00 pm May 2015 Jun 2017 SMAP TB H (K) After SMAP TB H (K) SMAP TB H (K) SMOS TB H (K) After After SMOS TB H (K) SMOS TB H (K) After adjustments, SMOS and SMAP exhibit minimal bias over the entire TB range Separate adjustments needed for 6:00 am TB H, 6:00 am TB V, 6:00 pm TB H, and 6:00 pm TB V 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

26 Chan et al (2017) Part 1: SMAP TB vs. SMOS TB 6:00 am SMOS TB H overlaid with 6:00 am SMAP TB H (Jun 14 16, 2017) moderate swath discontinuity before adjustments minimal swath discontinuity after adjustments SMOS 6 am SMAP 6 am SMOS 6 am SMAP 6 am After adjustments, swath discontinuity between SMOS and SMAP is reduced, though not completely eliminated due to: Different observation times away from SMOS/SMAP swath intersection region Different azimuth angles (SMOS: fore only; SMAP: fore and aft) 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

27 Chan et al (2017) Part 1: SMAP TB vs. SMOS TB Bias (SMAP minus SMOS) (K) RMSE (K) Before Adjustments After Adjustments Before Adjustments After Adjustments 6:00 am ocean TB H :00 am land TB H :00 am TB H :00 pm ocean TB H :00 pm land TB H :00 pm TB H :00 am ocean TB V :00 am land TB V :00 am TB V :00 pm ocean TB V :00 pm land TB V :00 pm TB V After adjustments, SMOS and SMAP exhibit minimal bias over the entire TB range Customized adjustments necessary for 6:00 am TB H, 6:00 am TB V, 6:00 pm TB H, and 6:00 pm TB V 32 nd URSI General Assembly and Scientific Symposium Montreal, Canada Aug 19-26, 2017 Steven Chan et al.

Assimilation of land surface satellite data for Numerical Weather Prediction at ECMWF

4 th workshop on Remote Sensing and Modelling of Surface properties Saint Martin d Hères, 14-16 March 2016 Assimilation of land surface satellite data for Numerical Weather Prediction at ECMWF P. de Rosnay,