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ZONAL SLOPE OF THE THERMOCLINE IN THE EQUATORIAL INDIAN OCEAN FROM ALTIMETER AND ARGO OBSERVATIONS

ZONAL SLOPE OF THE THERMOCLINE IN THE EQUATORIAL INDIAN OCEAN FROM ALTIMETER AND ARGO OBSERVATIONS. MM Ali 1 , Udaya Bhaskar 2 and M Ravichandran 2 1 National Remote Sensing Agency 2 International Centre for Ocean Information Services Hyderabad 500037 India Email: mmali73@yahoo.com.

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ZONAL SLOPE OF THE THERMOCLINE IN THE EQUATORIAL INDIAN OCEAN FROM ALTIMETER AND ARGO OBSERVATIONS

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  1. ZONAL SLOPE OF THE THERMOCLINE IN THE EQUATORIAL INDIAN OCEAN FROM ALTIMETER AND ARGO OBSERVATIONS MM Ali1, Udaya Bhaskar2 and M Ravichandran2 1National Remote Sensing Agency 2 International Centre for Ocean Information Services Hyderabad 500037 India Email: mmali73@yahoo.com

  2. Equatorial Indian Ocean (EIO: 5 S to 5 N & 50 to 110 E) behaves uniquely compared to other oceanic regions due to reversal of monsoon winds. • Non-monsoon months: Equator – 8oN westward flowing north equatorial current Equator – 8oS  eastward flowing equatorial counter current • Monsoon months: 8oS – 8oN  A jet of surface waters moves from west to east

  3. Water piles up at the eastern end of the basin creating a west to east up-slope • This results in a west-east down-slope in the thermocline • This phenomena occurs every year with different year to year variations in intensities

  4. Sea Surface Height Variations Along the EIO 1987 1988

  5. Years Monthly SSH Slope Anomalies (deg.) Along the EIO (2S-2N; 40-100 E) During 1994-2005 From Topex/Poseidon & Jason The negative (positive) slope during July-September corresponds to the warm (cold) phase

  6. Surface slopes can be obtained from altimeter observations • In situ measurements are required to obtain thermocline slopes (Argo is the best platform) Best Approach: To model the dynamic system from first principles using equations of motion In absence of such a perfect physical model: Use statistical approaches governing physical processes Assumption: Estimation of D20 can be modelled from chaotic nature of a non linear and deterministic dynamics In this study Artificial Neural Network (ANN) approach is used to estimate D20 from surface parameters.

  7. Data: • SSHA from Jason, monthly SSHA (MSSHA) from Jason w.r.t. 1993-2002 Topex mean • D20 from Argo floats • Climatological D20 from WOA01 Period:2003-2005 Approach: • D20 from Argo floats (i) Predictors: longitudes, month, climatological D20, 1993-2002 monthly average SSHA from Topex, SSHA from Jason, monthly SSH anomalies from Jason wrt 1993-2002 monthly means (ii) Predictand: D20 • Radial Basis Function with one hidden layer and 265 neurons • Analysed separately for 2002-2005 and 2005 • One single ANN model used for the entire area

  8. ANN model needs three sets of data: 2003-05 2005 % Training: 1816 736 50% Testing: 908 367 25% Predicting: 908 367 25%

  9. Statistical Analysis for D20 during (2003-05)(2005) Predictions

  10. Scatter of in situ and Predicted D20 (m) Predi2005ction 2003-2005

  11. Histograms of Difference Between In Situ and Predicted D20 (m) 2003-2005 2005 64% (86%) of the estimations lye within ±10 m for 2003-2005 (2005)

  12. Why 2005 result is better? Thermosteric effects (though small) may be different Thermocline could be due to factors other than SSH To improve the accuracy: Inclusion of windstress (vector) Analysis for different sectors Studying the effect of thermosteric anomalies

  13. Thank You

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