ocean salinity n.
Skip this Video
Loading SlideShow in 5 Seconds..
Ocean Salinity PowerPoint Presentation
Download Presentation
Ocean Salinity

Loading in 2 Seconds...

play fullscreen
1 / 23

Ocean Salinity - PowerPoint PPT Presentation

  • Uploaded on

Ocean Salinity. Commissioning reprocessing analysis New processor version : improvements and problems detected / solved Present performance Future evolution : ongoing studies. Land sea contamination correction. J. Martínez, V. González, C. Gabarró, J. Gourrion and BEC–TEAM

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

Ocean Salinity

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
ocean salinity

Ocean Salinity


New processorversion: improvements and problemsdetected/solved

Present performance

Futureevolution: ongoingstudies

land sea contamination correction

Land sea contamination correction

J. Martínez, V. González, C. Gabarró, J. Gourrion and BEC–TEAM

SMOS Barcelona Expert Centre

Pg. Marítim de la Barceloneta 37-49, Barcelona SPAIN

E-mail: jfont@icm.csic.es

URL: www.smos-bec.icm.csic.es

land contamination
Land contamination
  • Impact of correction implemented by Deimos on the strong halo around continental surfaces
    • to avoid multiplying the first Fourier parameter by the element of area (sqrt(3) * Distance_ratio * Distance_ratio/2)
  • L1PP run at BEC without and with correction
  • 71 ascending orbits, 71 descending from 17-21 August 2010
  • Tb at 42.5º; filtering 40 < Tb < 200
  • Tb maps: average per ISEA GP and then average for 1º*r*cos(lat).
  • SSS semi-orbits (problem in running several orbits at a time)
impact on sss
Impact on SSS
  • SSS 3 semi-orbits
  • Run with patched L1PP and L2OS 3.17
  • Specific OTT computed from uncorrected and corrected L1
  • The correction has removed the first order problem (strongest signal)
  • Back to the original scene dependant bias issue (A. Camps 2005)?

Pre-launch semi-empirical roughness model (SSS3) was derived from data obtained during the WISE experiments (2000-2001) on an oil platform in the NW Mediterranean

  • New fitting using actual SMOS data (residual after removing the rest of modelled emission components)
  • Guimbard et al., “SMOS semi-empirical ocean forward model adjustment” submitted to TGRS SMOS special issue

New semi-empirical roughness model


OTT sensitivity study

J. Gourrion, M. Portabella, R. Sabia, S.Guimbard


ott sensitivity
OTT sensitivity
  • Impacton OTT quality of differentfactors:
    • Number of snapshotsused
    • Temporal variability and apparentdrift
    • Latitudinal variability
  • Alternative OTT estimationstrategy
    • Methodand preliminaryresults
ott sensitivity1
OTT sensitivity

Impact of number of snapshots

  • For a 16-days period dataset (Aug. 3rd – Aug 18th), about 12000 snapshots are available after comprehensive filtering (land, outliers, descending overpasses)
  • N OTTs are computed by randomly selecting n snapshots among all available. (N-1) rms difference of the N OTTs are then computed.
  • N decreases with increasing n, leading to N=2 when n=6000, i.e., about half of the total amount in the 16-days period.
  • For consistency, the same experiment is repeated for two additional 16-days periods (Aug. 19th – Sep 3rd, Sep. 4th – Sep 19th). The overall rms values are obtained by averaging the 3 16-day period scores.
  • As expected, OTT robustness depends on number of snapshots used. Current operational OTT has a 0.25K error only due to sampling.
ott sensitivity2
OTT sensitivity

Temporal variability

  • A 48-days period dataset (August-Sept 2010) is used and split into 8-days subsets. Same filtering than previous experiment.
  • The reference situation is given by the first 8-days subset.
  • For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n = 6250.
  • The OTT rms increase (relative to reference) indicates an increasing data inconsistency with time, i.e., apparent drift.
ott sensitivity3
OTT sensitivity

Latitudinal variability

Salinity ?

Rain ?

Roughness residuals ?

New model 3

SSA/SPM model

  • A 16-day period dataset (Aug. 3rd – Aug 18th) is used and split into 6° latitudinal band subsets.
  • The reference situation is given by the [36° S, 30° S] latitudinal band subset.
  • For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n = 610.
  • The OTT rms differences (relative to reference) mainly indicate potential forward model and auxiliary data errors.



ott sensitivity4
OTT sensitivity

OTT as mean departure from full forward model: summary

  • OTT robustnesssignificantlydependsonsampling. Current OTT computationshould use a largernumber of snapshots.
  • Temporal inconsistenciesdueto non-modelled instrumental/reconstructioninstability and imperfectForeignSourcesmodelling
  • Latitudinal inconsistenciesduetoimperfectmodellingorauxiliaryparameters
  • OTTsestimatedfromdifferentdatasetswillvarydependingonthedistribution of sampledgeophysicalconditions
  • Withcurrent OTT methodology, the data are adjustedto reproduce the mean forward modelbehaviour (e.g., angular dependency): updated forward models are NOT independentfrom pre-launchversions (usedto compute theOTT)
ott sensitivity5
OTT sensitivity

New OTT estimation method: basics (1)

  • Objective:

Estimate systematic errors in the antenna frame

while avoiding use of forward models as much as possible

  • Main differences with current OTT:
    • do not use forward models
    • do not assume that geophysical variability is negligible


    • select specific environmental conditions (U,SST,SSS,low galactic,…)
  • MEAN angular dependency is fitted with a simple polynomial function and removed from the mean scene to obtain the systematic error pattern
  • Work in progress: only five days of data processed in this study.
ott sensitivity6
OTT sensitivity

New OTT estimation method: comparison




ott sensitivity7
OTT sensitivity

New OTT estimation method: stability (1)

Selecting different wind speed conditions


ott sensitivity8
OTT sensitivity

New OTT estimation method: summary

  • Adequate data selection techniques + mean angular dependence removal allows to obtain ROBUST OTT estimates WITHOUT introducing systematic errors from imperfect forward model and auxiliary information
  • Temporal drift effects still need to be accounted for.
  • Angular dependence of the corrected images is consistent with the original SMOS data
  • Work in progress:
    • Use more data
    • Further analyze latitudinal and temporal variations
    • New GMF fit using new OTT
  • Near-future work will compare the goodness of either additive or multiplicative formulations.