1. Progresses in IMaRS Caiyun Zhang Sept. 28, 2006

2. 1. SST validation over Florida Keys 2. Potential application of ocean color remote sensing on deriving salinity in the NE Gulf of Mexico (NEGOM) 3. Analyzing seasonal variability of Yucatan upwelling 4. Analyzing the spatio-temporal variability of SST and Chl in Florida Shelf by EOF method 5. Using monthly SeaWiFS K490 (1997-2005) to delineate the extension of Amazon river; Cutting the monthly Pathfinder SST (1985-2005, 9km and 4km) over equatorial Atlantic ocean 6. Using SeaWiFS nLw555 to study the influence of Yangtz River plume on East China Sea

3. Evolution of a coastal upwelling event during summer 2004 in the southern Taiwan Strait, submitted to Geophysical Research Letter. Surface temperature along the curise transects during July and August, 2004 MODIS SST Vertical distribution of T, S, and Chl along the southern TWS coast on 26-27 July and 1-2 August 29 June 11 July 24 July31 July

4. 1. SST validation over Florida Keys 2. Potential application of ocean color remote sensing on deriving salinity in Northeast Gulf of Mexico 3. Analyzing seasonal variability of Yucatan upwelling by EOF method (Empirical Orthogonal Function) 4. Analyzing the spatio-temporal variability of SST and Chl in Florida Shelf by EOF method 5. Using monthly SeaWiFS K490 (1997-2005) to delineate the extension of Amazon river; Cutting the Pathfinder SST (1985-2005) over equatorial Atlantic ocean 6. Using SeaWiFS nLw555 to study the influence of Yangtz River plume on East China Sea

5. 1. SST Validation over Floriday Keys

6. Try different filtered method, to generate reliable climatology and anomaly imagery Try different filtered method, to generate reliable climatology and anomaly imagery Accuracy of satellite SST? Which sensor performs better? Accuracy of satellite SST? Which sensor performs better? Objective

7. Buoy data Satellite SST data AVHRR SST(1993.8-2005.12), including NOAA11, 12, 14, 15, 16 and 17, deriving from MCSST algorithm MODIS SST (2003.5-2005.12), including Terra and Aqua MODISData

8. Method Method Clim o C were filtered) A weekly climatology filter (data- clim_weekly_mean < - 4 o C were filtered) Climmedian Clim+ temporal (  3 days) median filter (threshold:  2 o C). Clim4 o C) a weekly climatology filter (threshold  4 o C) Clim4mean Clim4 + Clim4 + temporal (  3 days) mean filter (threshold:  2 o C). Clim4median Clim4 + Clim4 + temporal (  3 days) median filter (threshold:  2 o C). stddev-2*stddev<data-clim_weekly_mean<5*stddev Calculating clim_weekly_mean: If the data-clim_weekly_mean <-4 then filtered, runs 3times, get the final climatology weekly mean. How to choose the good satellite SST for comparison:

9. Clim4 rms=1.306 n=9114 stddev=0.964 bias=-0.431 Clim4median rms=1.052 n=8407 stddev=0.740 bias=-0.303 stddev rms=1.280 n=7731 stddev=0.931 bias=-0.406 Clim4mean rms=1.069 n=8379 stddev=0.753 bias=-0.322 (Time difference: ±0.5hour) SST(Buoy) SST (Satellite) Clim rms=1.313 n=9260 stddev=0.968 bias=-0.407 Climmedian rms=1.055 n=8511 stddev=0.742 bias=-0.284 Comparison of buoy vs. satellite SST for different filter method taken buoy LONF1 as example

10. (a) Original (b) Median Filtered (c) Median + Clim4. Filtered An example of the filtering result for cloud-contaminated image. The image was taken from n12 AVHRR sensor on 31 December 2004 around 10:37 GMT. (a). Original image from the Terascan software after initial cloud filtering. (b) The same image after a temporal (  3 days) median filter (threshold:  2oC). (c) The same image after 1) a weekly climatology filter (threshold:  4oC) and 2) the same temporal median filter.

11. The comparison between buoy and satellite SST showed that the overall RMS error varied between 0.86-1.19 for all buoys; the standard deviation ranged between 0.61-0.78. The satellite SST underestimate SST by - 0.58- -0.04, especially at high SST value. (time difference: ±0.5hour; 9 buoys; clim4+median) SST(buoy) Satellite-buoy DRYF1 LONF1

12. MLRF1 station Time = day Satn12n15n16n17MODAMODT RMS0.9981410.8343890.9530190.7622051.05231.07388 STD0.6467590.5668430.6271040.5180610.742120.760404 Mean error-0.089043-0.07811-0.189480.056513-0.474-0.55127 Slope0.8621950.9176550.8562410.8850290.8795080.901851 Intercept3.529892.097513.632443.071242.769952.08494 Min error-3.81-3.71-2.72-2.6-3.89-4.01 Max error5.12.94.12.492.991.3 n_pairs1547808229304250228 Time = night RMS1.061480.9176330.868380.8405981.052310.967402 STD0.6808540.6281660.5824960.5806190.6654530.687512 Mean error-0.4138-0.16118-0.2116-0.20845-0.61812-0.55268 Slope0.8091790.899020.8879110.9149480.9310910.953582 Intercept4.603662.521852.768892.068751.224570.680451 Min error-4.71-4-3.61-4.41-3.21-3.81 Max error3.92.63.192.62.091 n_pairs1604608455530229194 Matrix of sensor performance

13. Summary The clim4median combined method [ is the best one to filter the cloud contaminated pixels The clim4median combined method [a weekly climatology filter (threshold:  4oC)+ temporal (  3 days) median filter (threshold:  2oC)] is the best one to filter the cloud contaminated pixels Overall, the RMS error between buoy and satellite SST over Florida Keys varied between ; the satellite SST underestimate buoy SST, especially at high SST value. Overall, the RMS error between buoy and satellite SST over Florida Keys varied between 0.86-1.19; the satellite SST underestimate buoy SST, especially at high SST value. The NOAA 17 performs better than the other satellites. The NOAA 17 performs better than the other satellites.

14. II. Potential application of ocean color remote sensing on deriving salinity in Northeast Gulf of Mexico (NEGOM)

15. Motivation and objective High Correlation / Linear relationship between CDOM  Salinity base on field measurement Ocean color remote sensing (~1km)  CDOM Is there any possibility to derive the salinity from high resolution ocean color remote sensing? What’s the accuracy? (Hu et al, 2003)

16. Validation of satellite CDOM absorption In situ CDOM absorption (a g 443) In situ CDOM absorption (a g 443) 7 cruises in NEGOM, flow-through 7 cruises in NEGOM, flow-through Summer: NEGOM3, NEGOM6, NEGOM9 Summer: NEGOM3, NEGOM6, NEGOM9 Autumn: NEGOM4, NEGOM7 Autumn: NEGOM4, NEGOM7 Spring: NEGOM5, NEGOM8 Spring: NEGOM5, NEGOM8 Ocean color product: Ocean color product: in situ ag443 SeaWiFS ag443 Satellite: a dg 443_qaa a g 443=a dg 443-a d 443 a d 443 (detritus absorption) is derived from bbp555 by empirical function a dg 443 (CDOM+detritus absorption) SeaDAS offers: -carder (Carder et al, 1999) -gsm01 (Garver and Siegel, 1997; Maritorena et al, 2002) -qaa (Lee et al, 2002)

17. Comparison of in situ ag443 and SeaWiFS derived adg443 summer autumn spring NEGOM3NEGOM6NEGOM9 NEGOM4NEGOM7 NEGOM5NEGOM8 red: ±2h; green: ±12h; blue: ±24h; black: ±48h Validation of satellite CDOM absorption Ship_ag_443 Swf_adg_443_qaa The satellite estimates agree well with the ship data in most cruises.

18. Comparison of in situ ag443(black line) along the ship transect lines and SeaWiFS adg443_qaa(blue points) for NEGOM3, NEGOM4 and NEGOM5 cruises NEGOM4 NEGOM5 NEGOM6 Fall,1998 Spring,1999 Summer,1999 Data index along ship transect lines ag/adg_443(m-1) (Time difference: +-24hour)

19. Time differencenSlopeInterceptRRMS(%)Bias(%)Log_RMSLog_bias NEGOM4 2h911.638-0.0380.90143.54229.4890.1490.098 12h3521.0340.0150.72348.24017.4410.1590.043 24h4421.0650.0080.76245.1958.0700.1600.003 48h7231.0650.0060.78143.87510.4480.1570.014 NEGOM5 2h3050.7450.0270.636105.59725.8430.1830.047 12h26120.6970.0110.78547.9364.7810.144-0.006 24h48870.5580.0160.72846.6446.6000.178-0.006 48h92110.5580.0180.53168.97211.2850.1980.002 NEGOM6 2h1310.790-0.0090.69149.174-36.0260.371-0.265 12h11710.4780.0180.59542.650-23.1280.262-0.161 24h26480.5180.0120.58647.534-25.7790.292-0.186 48h47480.5180.0130.52749.453-25.2920.296-0.187 Statistical result: For NEGOM4 and NEGOM5, the log_rms <0.2, For NEGOM6, the log_rms varied between 0.26-0.37. The slopes are close to 1.0, and the intercept are nearly zero.

20. Relationship between salinity and satellite adg_443

21. Coastal region Relationship between seawifs_adg_443_qaa and salinity in the coastal region (±24h) Rmsstd_errMin_diffMax_diff 0.2280.131-0.6550.628 SeaWiFS_adg443 Salintiy Statistic result for summer season (range of salinity: 34-36) : autumn spring

22. Offshore region Offshore_summer Offshore_spring SlopeInterceptnrRmsStd_errMin_diffMax_diff Spring -62.48136.9574867-0.8280.870.758-4.1387.271 Summer -60.36934.9092552-0.7121.871.049-5.6325.251

23. Comparison of mapping salinity from ship and Seawifs derived for NEGOM5 spring cruise NEGOM 5 (spring) cruise: In situ ag443 SeaWiFS adg443 In situ salinitySatellite derive salinity (Offshore)

24. NEGOM 6 (summer) cruise: Comparison of mapping salinity from ship and Seawifs derive for NEGOM6 summer cruise In situ ag443 SeaWiFS adg443 In situ salinity Satellite derive salinity (offshore) Satellite derive salinity (Coast)

25. Conclusion The accuracy of salinity derived from ocean color remote sensing varied regionally and seasonally. It depend greatly on the accurate estimation of satellite CDOM absorption. The accuracy of salinity derived from ocean color remote sensing varied regionally and seasonally. It depend greatly on the accurate estimation of satellite CDOM absorption.

26. III. Variability of Yucatan upwelling cold water

27. Sea Surface Temperature Space EOF Result Mode2 Demean spatial mean

28. Chl (SVD/Time EOF) 59.55% 15.0% 5.4%

29. Mode 1Mode 2 QuikSCAT wind field

30. Week15 Week18 Week21 Week24 Week27Week30 Week33 Week36 Week39 Week12 Climatology weekly mean SST in Yucatan shelf from March to September Variability of Yucatan upwelling cold water

31. We calculated the areal extent of waters colder than the area-averaged mean SST by 1ºC, as the proxy for the area influenced by upwelling Time series of the areal extent of upwelling cold water in Yucatan shelf The areal extent of upwelling cold water (colder than the area-averaged mean SST by 1 ºC) was maximum ( >20000km 2 ) between weeks 25 to 30 (in July).

32. Movement of thermal centroid with time. The label indicated the number of week Deformation of the upwelling region The deformation and movement process of the cold water area can be characterized by movement of its thermal centroid (xc, yc), which defined as follow (Kuo, et al, 2000) Week 14: early April Week 31: the end of July Week 38: mid September

33. Welcome to visit me at XMU Contact information: Department of oceanography, Xiamen University Xiamen, China, 361005 Email: cyzhang@xmu.edu.cn Tel: 86-592-2188071 (office), 2186871 (lab)

34. Thank you!

35. Offshore region Offshore_summer Offshore_spring SlopeIntercep t nrRmsStd_errMin_diffMax_diff -60.36934.9092552-0.7121.871.049-5.6325.251 Spring SlopeInterceptnrRmsStd_errMin_diffMax_diff -62.48136.9574867-0.8280.870.758-4.1387.271 Summer

36. Week 35 Week 25 Week 20Week 15 Week 30Week 40 Weekly climatology QuikSCAT wind vector from early April to the end of September