1 / 23

Ocean Salinity

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

ifeoma-norman
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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ocean Salinity Commissioningreprocessinganalysis New processorversion: improvements and problemsdetected/solved Present performance Futureevolution: ongoingstudies

  2. 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

  3. 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)

  4. Tb ascending maps

  5. Tb descending maps

  6. Impact on SSS • SSS 3 semi-orbits • Run with patched L1PP and L2OS 3.17 • Specific OTT computed from uncorrected and corrected L1

  7. Uncorrected

  8. Corrected

  9. Uncorrected

  10. Corrected

  11. Conclusion • The correction has removed the first order problem (strongest signal) • Back to the original scene dependant bias issue (A. Camps 2005)?

  12. 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

  13. New semi-empirical roughness model

  14. OTT sensitivity study J. Gourrion, M. Portabella, R. Sabia, S.Guimbard SMOS-BEC, ICM/CSIC

  15. OTT sensitivity • DPGS OTT • Impacton OTT quality of differentfactors: • Number of snapshotsused • Temporal variability and apparentdrift • Latitudinal variability • Alternative OTT estimationstrategy • Methodand preliminaryresults

  16. 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.

  17. 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.

  18. 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. Ocean/ice transition

  19. 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)

  20. 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 BUT • 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.

  21. OTT sensitivity New OTT estimation method: comparison INCONSISTENT ANGULAR DEPENDENCE BETWEEN SMOS DATA AND PRE-LAUNCH FORWARD MODELS

  22. OTT sensitivity New OTT estimation method: stability (1) Selecting different wind speed conditions RMS VALUES CONSISTENT WITH EXPECTED VALUES FROM NUMBER OF SAMPLES – GRANULAR PATTERNS

  23. 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.

More Related