SSS retrieval from space Comparison study using Aquarius and SMOS data

44 th International Liège Colloquium on Ocean Dynamics 7-11 May 2012 SSS retrieval from space Comparison study using Aquarius and SMOS data Physical Oceanography Department Institute of Marine Sciences (ICM) CSIC S. Guimbard, J. Font (co lead investigator), J. Gourrion, J. Ballabrera, A. Turiel, J. Martínez, F. Perez

Outline Introduction SMOS data processing status SSS comparisons between Aquarius and SMOS Conclusion

The Soil Moisture and Ocean Salinity ESA earth mission explorer Since November 2009, a new concept of measurement from space is available. Microwave imaging Radiometer using Aperture synthesis (2D imaging by Fourier synthesis) L-band radiation measured at 1.43 GHz ( protected band for scientific applications) Sun-synchronous (6 am local equator crossing times) Multiple incidence and azimuthal observation angles It has not been design for Sea Surface Salinity only ->Even with a well calibrated instrument, SSS retrieval was expect to be tough. We are at the limit of the instrumental sensitivity

Concept of the measurement Discrete sampling produces spatial periodicity: Aliases Visibility: (u-v) domain Visibility: (u-v) domain T B : (ξ-η) domain Geo-location Alias-Free Field of View T B : (lat-lon) domain Extended Alias-Free Field of view Main issues : Reconstruction, Radio frequency interferences, Sea/land transition

SSS retrieval concept Many models with very good prediction accuracies are needed For example, 0.1 K (~0,003) error on a model component can lead to an error of 0.2 psu

SMOS sea surface salinity status SMOS Level 1 processor v5.04 In operation since November 2011 Principal modifications: Reduction of short and long term drifts Decrease of contaminations due to land and ice Better RFI detection/mitigation Improved sun impact correction Caveats: Fixed spatial bias (2.8K RMS) -> corrected at L2 Software bug: corrupted measurements in polar region (Sea-ice conditions and salinity studies not possible with this version SMOS Level 2 processor v5.50 In operation since December 2011 Principal modifications: Time-varying residual spatial bias correction (Ocean Target Transformation); different ascending/descending Roughness effect correction models tuned to SMOS data Improved flagging /data filtering strategy -> Used to reprocess the full 2010-2011 data set

WOA 2009 SSS SMOS through the years SSS anomalies (WOA 2009) SSS difference 2010 2010 2011-2010 2011 2011 2010: El niño 2011: La niña

Study angle What are the common points between SMOS and Aquarius missions? Produce global salinity maps on a monthly basis with a spatial resolution of ~100 km with an accuracy of 0.2-0.3 psu Both radiometer measuring radiation at 1.4 GHz Physical laws ( Kirchhoff's law of thermal radiation, planck law) 8 months period of SSS products What are the differences? Instrument design and accuracy Spatial and temporal sampling Ascending passes (6am for SMOS vs 6pm for Aquarius) Forward model (or contamination corrections) and auxiliary parameters (SST,wind speed ) used to retrieved salinity We expect differences but let s see what it looks like on a monthly basis.

2011 L3 binned SSS anomaly (Aquarius-WOA09) Sep Oct Nov Dec 2012 Jan Fev Mar Apr L2 v1.3 Aquarius data bin averaged on a regular grid of 1 x1 Minus monthly World Ocean Atlas 2009 OI SSS

2011 L3 binned SSS anomaly (SMOS-WOA09) Sep Oct Nov Dec 2012 Jan Fev Mar Apr L2 SMOS data bin averaged on a regular grid of 1 x1 minus monthly World Ocean Atlas 2009 OI SSS

2011 L3 binned SSS difference (Aquarius-SMOS) Sep Oct Nov Dec 2012 Jan Fev Mar Apr L2 AQUARIUS data bin averaged on a regular grid of 1 x1 minus L2 SMOS data bin averaged on a regular grid of 1 x1

Aquarius vs SMOS? Which one has the ground truth? I compared these Aquarius and SMOS datasets with all in situ data available (ARGO, TAO, RAMA, PIRATA ) -> no clear answer arise, only other questions (surface stratification, spatial representativeness ) The only thing I am sure of, is that we are still missing something in our understanding of this 2 instruments and only time will give us answers

Future improvements Improving land/sea transition impact on L1 signal (Gibbs phenomenon) Better residual bias removal techniques under analysis (understanding the problem at L1; mitigating it as L2 preprocessing) Galactic noise degrades retrieval in function of region and season. New correction model at L2 tested and ready for operational implementation. Sun signal tails on alias-free field-of-view still a problem. Removal techniques under investigation RFI degrades/corrupts salinity retrieval in large areas. Switching-off illegal sources and improving mitigation procedures Still need to improve correction for faraday rotation 14

Conclusions As the SSS sensitivity of the SMOS measurement is very low, space and time averaging are necessary Spatial averaging is more efficient since we are still dealing with unresolved temporal drift. Regional SSS retrieval algorithm will have to be developed in order to achieve mission requirements. A specific SSS retrieval algorithm for both SMOS & Aquarius sea surface brightness temperature with same dielectric constant is needed in order to achieve coherent synergy

L2 SSS statistics Sep Oct Nov ASC DES ASC/ DES

Monthly bin averaged SMOS SSS (1 x1 ) 2010

Monthly bin averaged SMOS SSS (1 x1 ) 2011

Yearly SMOS SSS anomalies 2010 2011 2011-2010 WOA 2009

Global L2 retrieved SSS temporal evolution NO filtering Filtered

Roughness TB modulation

4. Sea Surface Temperature (SST) Anomaly SUMMARY: CRW's SST Anomaly is produced by subtracting the long-term mean SST (for that location in that time of year) from the current value. A positive anomaly means that the current sea surface temperature is warmer than average, and a negative anomaly means it is cooler than average. The spatial resolution is 0.5-degree (50-km), and the data and images are updated twice-weekly. Animations of the most recent SST Anomaly images are also available online. CRW's near-real-time global SST Anomaly product makes it possible to quickly pinpoint regions of elevated SSTs throughout the world oceans. It is especially valuable for the tropical regions where most of the world's coral reef ecosystems thrive. It is also very useful in assessing ENSO (El Niño-Southern Oscillation) development, monitoring hurricane "wake" cooling, observing major shifts in coastal upwellings, etc. A twice-weekly SST anomaly at a 0.5-degree (50-km) grid is calculated by subtracting the daily climatological SST of the last day of the twiceweekly period at that grid from the corresponding twice-weekly SST (described in Sea Surface Temperature Section). The formula for obtaining the anomaly is SST_anomaly = SST - Daily_SST_climatology The color range of temperature anomalies displayed on the SST Anomaly charts is -5.0 to +5.0 C (or Kelvin). Areas with SST anomaly values less than -5.0 C are displayed as -5.0 C, and areas with values greater than +5.0 C are displayed as +5.0 C. Note that these anomalies are somewhat less reliable at high latitudes where more persistent clouds limit the amount of satellite data available for deriving accurate SST analysis fields and climatologies. Data and images of both near-real-time and archived SST anomalies are available from the CRW website, along with the operational 0.5- degree monthly mean SST climatologies. Animations of SST Anomaly images for the past six months are also available. Charts of the retrospective 1984-1998 monthly mean SST anomalies are available online at 36-km resolution.

Sunspot number Moscow Cosmic Rays GOES X-rays GOES Protons GOES magnetometer:hp, He

31 A SMOS snapshot is an irregular 1000 km x 1000 km hexagon image with variable incidence angles, pixel sizes and radiometric sensitivities As the satellite advances a single spot on earth is seen in different positions within the instrument field-of-view Many snapshots are used to retrieve a single SSS value

BEC OS products validation: L3 SMOS OS L3 map 1 o x1 o Optimal Interpolation using WOA2009 as background 15-24 Jan. 2012 Argo SSS interpolated at 7.5m depth SMOS - Argo 1299 points Bias = -0.11 RMS = 0.42

BEC OS products validation: L4 SMOS OS L4 map 1 o x1 o Generated from L3 binned + SST singularity exponents 15-24 Jan. 2012 Argo SSS interpolated at 7.5m depth SMOS - Argo 1202 points Bias = -0.17 RMS = 0.49