Evaluating the impact of ocean gravity wave variability on Aquarius satellite measurements D. Vandemark, H. Feng Univ. of New Hampshire/EOS N. Reul, F.

Slides:



Advertisements
Similar presentations
UPDATE ON BIAS TRENDS, DIRECT SUN CORRECTION, AND ROUGHNESS CORRECTION Joe Tenerelli May 10, 2011.
Advertisements

AN INITIAL LOOK AT THE IMPACT OF THE NEW ANTENNA LOSS MODEL Joe Tenerelli SMOS QUALITY WORKING GROUP #4 7-9 March 2011.
Draft Recommendations subtitle here. Recommendation 1 The study groups from this workshop continue to collaborate with the goal of reporting progress.
F. Wentz, T. Meissner, J. Scott and K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November ,
OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements.
26/11/2012 – Observatoire de Paris Analysis of wind speed evolution over ocean derived from altimeter missions and models M. Ablain (CLS)
SMOS L2 Ocean Salinity Level 2 Ocean Salinity Using TEC estimated from Stokes 3 24 October 2012 ACRI-st, LOCEAN & ARGANS SMOS+polarimetry.
Calibration and Validation Studies for Aquarius Salinity Retrieval Shannon Brown and Sidharth Misra Jet Propulsion Laboratory, California Institute of.
The Aquarius Salinity Retrieval Algorithm Frank J. Wentz and Thomas Meissner, Remote Sensing Systems Gary S. Lagerloef, Earth and Space Research David.
Linking satellite SSS and SST to water mass formation Marlene Klockmann 1, Roberto Sabia 2, Diego Fernández-Prieto 3, Craig Donlon 4 1 Max Planck Institute.
IFREMER EMPIRICAL ROUGHNESS MODEL Joe Tenerelli, CLS, Brest, France, November 4, 2010.
Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity E. P. Dinnat 1,2, D. M. Le Vine 1, J. Boutin 3, X. Yin 3, 1 Cryospheric.
Aquarius/SAC-D Mission Validation Working Group Summary Gary Lagerloef 6 th Science Meeting; Seattle, WA, USA July 2010.
Aquarius/SAC-D Mission Mission Simulators - Gary Lagerloef 6 th Science Meeting; Seattle, WA, USA July 2010.
1 Satellite & In Situ Salinity (SISS) Working Group: Current Status and Future Plans Yi Chao, Co-Chair (Jacqueline Boutin, Co-Chair) Aquarius Science Meeting.
Observations of Ocean response to Hurricane Igor: A Salty Tropical Cyclone Wake observed from Space N.Reul 1, Y, Quilfen 1, B. Chapron 1, E. Vincent 2,
Ifremer Planning of Cal/Val Activities during In orbit commisioning Phase N. Reul, J. Tenerelli, S. Brachet, F. Paul & F. Gaillard, ESL & GLOSCAL teams.
ATMS 373C.C. Hennon, UNC Asheville Observing the Tropics.
MWR Roughness Correction Algorithm for the Aquarius SSS Retrieval W. Linwood Jones, Yazan Hejazin, Salem Al-Nimri Central Florida Remote Sensing Lab University.
SMOS STORM KO meeting 30/01/2012 ESRIN Ocean Surface Remote Sensing at High Winds with SMOS.
SMOS Science Workshop, Arles, th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC ERROR DIAGNOSTIC J. Gourrion, R. Sabia, M. Portabella,
SMOS QWG-5, 30 May- 1 June 2011, ESRIN Ocean Salinity 1 1.Commissioning reprocessing analysis 2.New processor version: improvements and problems detected/solved.
SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.
Problems and Future Directions in Remote Sensing of the Ocean and Troposphere Dahai Jeong AMP.
Evaluation of Microwave Scatterometers and Radiometers as Satellite Anemometers Frank J. Wentz, Thomas Meissner, and Deborah Smith Presented at: NOAA/NASA.
Aquarius Ocean Salinity Open Discussion Yi Chao and Peter Hacker  Surface Stratification Advanced Argo floats Flag in situ data for expected mixed layer.
Calibration and Validation Studies for Aquarius Salinity Retrieval PI: Shannon Brown Co-Is: Shailen Desai and Anthony Scodary Jet Propulsion Laboratory,
Séverine Fournier, Nicolas Reul, Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER Joe Salisbury, Doug Vandemark University of New Hampshire,
Satellite Sea-surface Salinity: Data and Product Biases and Differences Eric Bayler and Li Ren NOAA/NESDIS Center for Satellite Applications and Research.
Ocean Salinity validation of mission requirements review / improvements: Points of Reflexion ESL teams Mission Requirements: The so-called GODAE requirements:
A. Montuori 1, M. Portabella 2, S. Guimbard 2, C. Gabarrò 2, M. Migliaccio 1 1 Dipartimento per le Tecnologie (DiT), University of Naples Parthenope, Italy.
Sea surface salinity from space: new tools for the ocean color community Joe Salisbury, Doug Vandemark, Chris Hunt, Janet Campbell, Dominic Wisser, Tim.
T. Meissner, F. Wentz, J. Scott, K. Hilburn Remote Sensing Systems ESA Ocean Salinity Science and Salinity Remote Sensing Workshop November.
SPCM-9, Esac, May 3 rd, 2012 MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES J. Gourrion, S. Guimbard, R. Sabia,
Modeling the upper ocean response to Hurricane Igor Zhimin Ma 1, Guoqi Han 2, Brad deYoung 1 1 Memorial University 2 Fisheries and Oceans Canada.
OS-ESL meeting, Barcelona, February nd, 2011 OTT sensitivity study and Sun correction impact J. Gourrion and the SMOS-BEC team SMOS-BEC, ICM/CSIC.
EXTENDING THE LAND SEA CONTAMINATION CHARACTERIZATION TO THE EXTENDED ALIAS- FREE FIELD OF VIEW Joe Tenerelli (CLS) and Nicolas Reul (IFREMER) SMOS Quality.
Optimization of L-band sea surface emissivity models deduced from SMOS data X. Yin (1), J. Boutin (1), N. Martin (1), P. Spurgeon (2) (1) LOCEAN, Paris,
2nd GODAE Observing System Evaluation Workshop - June Ocean state estimates from the observations Contributions and complementarities of Argo,
SMOS QWG-6, ESRIN October 2011 OTT generation strategy and associated issues 1 The SMOS L2 OS Team.
Space Reflecto, November 4 th -5 th 2013, Plouzané Characterization of scattered celestial signals in SMOS observations over the Ocean J. Gourrion 1, J.
Sea Surface Salinity from Space Simulation of Aquarius brightness temperature, Tb, at spacecraft during one orbit Top Panel –Ground track (red) –Outer.
Sea Surface Salinity as Measured by SMOS and by Surface Autonomous Drifters: Impact of Rain J. Boutin, N. Martin, X. Yin, G. Reverdin, S. Morrisset LOCEAN,
USING SMOS POLARIMETRIC BRIGHTNESS TEMPERATURES TO CORRECT FOR ROUGH SURFACE EMISSION BEFORE SALINITY INVERSION.
Cal/Val Discussion. Summary No large errors in rain, freshening observed by Aquarius can be significant and real (up to about 3 hours on average after.
Simulator Wish-List Gary Lagerloef Aquarius Principal Investigator Cal/Val/Algorithm Workshop March GSFC.
Ocean Surface Topography Science Team, Reston, VA, USA, October, 2015 References Feng et al., Splin based nonparametric estimation of the altimeter.
T. Meissner and F. Wentz Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014 Seattle. Washington,
Observations of Ocean response to Hurricane Igor: A Salty Tropical Cyclone Wake observed from Space Nicolas Reul 1, Joseph Tenerelli 2 1 IFREMER, Laboratoire.
Level 2 Scatterometer Processing Alex Fore Julian Chaubell Adam Freedman Simon Yueh.
Assimilating Satellite Sea-Surface Salinity in NOAA Eric Bayler, NESDIS/STAR Dave Behringer, NWS/NCEP/EMC Avichal Mehra, NWS/NCEP/EMC Sudhir Nadiga, IMSG.
UPDATE ON GALACTIC NOISE CORRECTION Joe Tenerelli SMOS Quality Working Group #9 ESA ESRIN 24 October 2012.
Dependence of SMOS/MIRAS brightness temperatures on wind speed: sea surface effect and latitudinal biases Xiaobin Yin, Jacqueline Boutin LOCEAN.
Ocean Sciences The oceans cover 3/4 of the Earth’s surface. They provide the thermal memory for the global climate system, and are a major reservoir of.
T. Meissner, F. Wentz, J. Scott, K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014.
EXTREME WINDS AND PRECIPITATION FROM SPACE
(2) Norut, Tromsø, Norway Improved measurement of sea surface velocity from synthetic aperture radar Morten Wergeland Hansen.
Enhancement of Wind Stress and Hurricane Waves Simulation
Blending multiple-source wind data including SMOS, SMAP and AMSR-2 new products Joe TENERELLI, Fabrice COLLARD OceanDataLab, Brest, France.
Spatial Modes of Salinity and Temperature Comparison with PDO index
Retrieving Extreme wind speeds using C-band instruments
Institute of Low Temperature Science, Hokkaido University
Roughness Correction for Aquarius (AQ) Sea Surface Salinity (SSS) Algorithm using MicroWave Radiometer (MWR) W. Linwood Jones, Yazan Hejazin Central FL.
Extreme Wind Speed Measurements from NASA’s SMAP L-Band Radiometer
Aquarius SSS space/time biases with respect to Argo data
Haline hurricane wake in the Amazon/Orinoco plume: AQUARIUS/SACD and SMOS observations S. A. Grodsky1 N. Reul2, G. Lagerloef3, G. Reverdin4, J. A. Carton1,
Evaluation of ASCAT Winds by Assessing their Self-consistency
Nicolas Reul1, Joseph Tenerelli2
Values of density st (curved lines) and the loci of maximum density and freezing point (at atmospheric pressure) for seawater as functions of temperature.
CMOD Observation Operator
Presentation transcript:

Evaluating the impact of ocean gravity wave variability on Aquarius satellite measurements D. Vandemark, H. Feng Univ. of New Hampshire/EOS N. Reul, F. Ardhuin, B. Chapron IFREMER/Centre de Brest within Aq. Cal/Val team efforts D. Vandemark, H. Feng Univ. of New Hampshire/EOS N. Reul, F. Ardhuin, B. Chapron IFREMER/Centre de Brest within Aq. Cal/Val team efforts OSST Meeting OSST 2012

Overview Goal Develop and refine an empirical satellite salinity correction for long wave impacts that augments the 1 st order roughness corrections made using NCEP winds, Aquarius scatterometer, or other ocean roughness information Approach Geolocate ancillary ocean wave data with each satellite data point Detect, characterize, and quantify long-wave impacts seen in the scatterometer, radiometer, and ultimately salinity Help implement a point-by-point correction using operational wave model data 3OSST 2012

4

5 The general geophysical problem To obtain accurate salinity we need to accurately remove the signal due to rough surface emission Specifically for Aquarius 1) A three beam radiometer at L band - Tb_ocean (2 polarizations, 3 incidence angles, look angle) = F ( variable sea surface waves, S(k) ) ~= rms slopes = 2) A three beam radar scatterometer at L-band (Bragg waves ~ 18 cm) NRCS_ocean (2 polarizations, 3 incidence angles, look angle) = F ( variable sea surface waves ) ~= S(k Bragg ) + tilting effects 3) The portion of the wave spectrum and EM interaction differs for the radar and radiometer and for each beam’s incidence angle – how well correlated are 1) and 2)? 4) Usual surrogate for sea surface wave information is wind – unlikely to provide sufficient precision to correct for the true ocean wave field.

OSST Global Wave model fields, Aquarius L2_wwav files, Day Aquarius *L2_wwav files are assembled daily at UNH for the Aq. cal/val team available at PO.DAAC

Aquarius Level 2 wave model collocation products: Processing status (March 2012) Near Real Time processing ( Latency: 1 day after Aq-L2 V1.1 data become available at GSFC) Aq-L2 V1.1 SCIdata (daily wget) L2V1.1_wwav* Ifremer/Previmer WW3 model product ( Partial dataset: 5 NRT variables, limited QC) Science level processing ( Latency: days after Aq-L2 data become available) L2V1.2_wwav (2011)* L2V1.2.3_wwav (2012)* Ifremer/Previmer WW3 model product (Full wave model dataset: 14 variables with QC) Aq-L2 V1.2 EVSCI data (2011) Aq-L2 V1.2.3 EVSCI data (2011, 2012) * ftp access via PODAAC for Aq. Cal/Val team members

OSST Diagnosis using Significant Wave Height (SWH) Evaluate Aquarius L2 scatterometer and SSS data Expectations: 1)For fixed wind speed we’ll see long wave impacts in the scatterometer roughness and derived wind speeds 1)For fixed wind speed we’ll see long wave impacts in the radiometer-derived salinity

Aquarius Scatterometer sigma0 vs. significant wave height (17 weeks; Day ) ; X-axis=NCEP wind OSST 2012 VV POL INNER BEAM θ = 28 deg. HH POL INNER BEAM θ = 28 deg. VV POL OUTER BEAM θ = 46 deg. HH POL OUTER BEAM θ = 46 deg. LARGEST IMPACT LEAST IMPACT 9

Aquarius Scatterometer Wind vs. significant wave height (17 weeks; Day ) ; X-axis=NCEP wind OSST 2012 INNER BEAM θ = 28 deg. OUTER BEAM θ = 46 deg. LARGEST IMPACTWEAKER IMPACT Long-waves lead to wind speed error when seas are exceedingly high This is a good thing – indicates that the scatterometer is sensitive to longer waves that likely impact the radiometer 10

Getting sidetracked OSST All wind products show significant systematic differences – likely associated with ocean currents. wind waves, and atmospheric stability (SST) impacts. NCEP, ECMWF, SSM/I, Aquarius Scatterometer

Wind products and spatial differences Day OSST

Wind products and spatial differences Day OSST

Onto radiometer derived SSS OSST Some substantial improvement already L2 V1.2 -> 1.3

Aquarius Radiometer Salinity error vs. SWH (17 weeks; Day ) ; X-axis=NCEP wind, V`1.2; DESC OSST 2012 Residual Salinity with respect to HYCOM Long-waves lead to salinity anomaly – low for low seas, high for high seas Residual Salinity taken with respect to HYCOM model SSS 15

Aquarius Radiometer Salinity error vs. SWH (17 weeks; Day ) ; X-axis=NCEP wind; Ver 1.2.3; DESC; OUTER BEAM Aq Cal/Val, March 2012 Residual Salinity with respect to HYCOM Long-waves lead to salinity anomaly – low for low seas, high for high seas Residual Salinity taken with respect to HYCOM model SSS

Aquarius Scatterometer Wind vs. significant wave height (17 weeks; Day ) ; X-axis=NCEP wind; V1.2 OSST 2012 OUTER BEAM θ = 46 deg. Desc Pass data, Galactic refl < 1 K Long-waves lead to wind speed error of order when seas are exceedingly high OUTER BEAM θ = 46 deg psu/ 1m sea state change 0.21 psu/ 1m sea state change 17 DESCENDING V1.2 Long-waves can lead to wind speed error of order when seas are exceedingly high

Aquarius Scatterometer Wind vs. significant wave height (17 weeks; Jan-Feb2012) ; X-axis=NCEP wind; Ver. 1.3 Aq Cal/Val, March 2012 OUTER BEAM θ = 46 deg. Desc Pass data, Galactic refl < 1 K OUTER BEAM θ = 46 deg. 0.1 psu/ 1m sea state change <0.1 psu/ 1m sea state change DESCENDING V1.3 Substantial improvements

Aquarius Scatterometer Wind vs. significant wave height (17 weeks; Jan-Feb2012) ; X-axis=NCEP wind; Ver. 1.3 Aq Cal/Val, March 2012 OUTER BEAM θ = 46 deg. ASC Pass data, Galactic refl < 1 K OUTER BEAM θ = 46 deg. >0.1 psu/ 1m sea state change ~0.1 psu/ 1m sea state change ASCENDING V1.3 But not done yet

Some caveats and future work Final Aquarius surface Tb_V and Tb_H are still being developed with issues such as reflected Galaxy and Faraday rotation correcting impacting these 2nd order ocean wave impact results (V1.4?) ascending vs. descending results differ time dependence highest wind/wave environment are in coldest waters SST-wave covariance Fully exploit and document the certain benefit of using the scatterometer for SSS inversion Further formal evaluation of the radiometer and radar data within a scattering/emission model + global wave field data What wind model to use as a reference? SSM/I != NCEP != ECMWF 20OSST 2012

Analysis of Satellite SSS signatures over Hurricane IGOR Sep 2010 using SMOS Cat 4-5

Freshwater Plume=> warmer SST & shallow stable surface layer (barrier layer) 65% of TC crossing the Amazon Plume evolve into cat 5 Hurricanes Ffield (J. Clim 2007) Amazon & Orinoco plumes =>Strong positive SST anomalies (~1°C) Impact of plume SST on storm intensity addressed using WRF simulation Vizy and Cook, (JGR 2010) Can we use SMOS retrieved SSS to study ocean-atmosphere interactions in TCs ? Nico Reul with Chapron, Tenerelli, Vandemark, Vialard,...

Analysis of SMOS data signature over Hurricane IGOR Sep 2010 Cat 4-5

Location of the Plume (SSS<35.5) the last 10 days before Igor passage (5->13 Sep) 09/13/ /15/ /17/2010

are cross-track locations from the eye at t o Strong resalinisation of the SSS after Igor passage, over the path of the storm Right-hand quadrant: ΔSSS~> pss Cool Wake induced by Igor: SST= GHRSST OSTIA (MetOffice) Salty Wake induced by Igor: SSS=SSS SMOS (CATDS/ifremer)

SSS averaged 5-11/09 (3-5 days before Igor ) SSS averaged 19-24/09 (3-5 days after Igor ) Apparent Erosion of the freshwater surface layer on the right-hand side quadrants of the storm ARGO profiler # /09 ARGO profiler # /09

ΔSSS smos =+1.4 ΔSSS argo =+1.3 Enhancement of Sea Surface Salinity as seen by ARGO after IGOR passage: ΔSSS argo ~+1.3 Excellent consistency with SMOS observation trend: ΔSSS smos ~+1.4. SMOS SSS is however systematically ~0.5 pss fresher than ARGO observations at 5 m depth Maximum surface Wind encountered at the argo float location is ~33 m/s (GFDL model) Maximum significant wave height up to 11 m (Wave Watch III, NAH)

Original Plume surface freshwater layer ~20 m thick After the passage of Igor: =>Drop of sea surface temperature Δ T~2.8°C =>Enhancement of Sea Surface Salinity by ΔS=1.4 Excellent consistence with SMOS obs~1.5 Increase in sea surface density ~2 kg/m 3 Mixing of the Warm freshwater surface layer by Hurricane Igor (ARGO# ) 6 days Before Igor 1 day after Igor 9 days After Igor Deepening of the Mixed surface layer From 20 m down to ~90 m depth Salinity Argo # Temperature Argo # Density Argo #

ARGO profiler # /09 SSS averaged 5-11/09 (3-5 days before Igor ) SSS averaged 19-24/09 (3-5 days after Igor ) ARGO profiler # /09

ΔSSS argo =+0.3 ΔSSS smos =+0.4 Surface wind speed encountered >15 m/s up to 48 m/s from 09/15->09/17 Significant wave height >3 m up to 9 m from 09/15->09/17 SMOS SSS in general saltier than Argo SSS by ~ pss However very consistent SSS temporal trend from before to after Igor between ARGO float & SMOS surface data: =>both are showing a pss increase following the surface mixing induced by IGOR on its left hand side quadrant.

9 days Before Igor 1 day before Igor 6 days After Igor Cooling ~1°C More intense wave mixing On the RHS ? Mixing Damped By the thicker Plume Barrier layer On the LHS ?

EARLY CONCLUSIONS SMOS SSS data able to produce before and after snapshots of plume location associated with TC passage Satellite SSS data yielding accurate SSS perturbation due to TC as compared to two ARGO floats – 0.5 to 1.5 SSS INCREASE New look at plume-TC interaction with SSS + SST perhaps allowing enhanced diagnosis

Figure 4: Surface wakes of Hurricane Igor. Post minus Pre-hurricane (a) Sea Surface Temperature (ΔSST ) (b) Sea surface Salinity (ΔSSS), (c) Sea Surface Density (Δσ o ) and (d) Sea Surface CDOM absorption coefficient.The thick and thin curves are showing the hurricane eye track and the locii of maximum winds, respectively. The dotted lines is showing the pre-hurricane plume extent. ΔSST, ΔSSS, Δσ o wakes were only evaluated at spatial locations around the eye track for which the wind exceeded 34 knots during the passing of the hurricane. Surface wakes of Igor Six days of data centered on t o –(+) 4 days have been averaged to construct the pre (post)- cyclonic quantities. Here a cdom = a d + ag ag: CDOM (dissolved matter) ad: non living particulate organic material, bacteria, inorganic material and bubbles

Thanks! 34OSST 2012 This work is supported by NASA’s Ocean Surface Salinity Science Team – Grant NNX09AU69G