1 / 36

Progresses in IMaRS

Progresses in IMaRS. Caiyun Zhang Sept. 28, 2006. SST validation over Florida Keys Potential application of ocean color remote sensing on deriving salinity in the NE Gulf of Mexico (NEGOM) Analyzing seasonal variability of Yucatan upwelling

osma
Download Presentation

Progresses in IMaRS

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. Progresses in IMaRS Caiyun Zhang Sept. 28, 2006

  2. SST validation over Florida Keys • Potential application of ocean color remote sensing on deriving salinity in the NE Gulf of Mexico (NEGOM) • Analyzing seasonal variability of Yucatan upwelling • Analyzing the spatio-temporal variability of SST and Chl in Florida Shelf by EOF method • 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 • Using SeaWiFS nLw555 to study the influence of Yangtz River plume on East China Sea

  3. 29 June 11 July 24 July 31 July MODIS SST Surface temperature along the curise transects during July and August, 2004 Vertical distribution of T, S, and Chl along the southern TWS coast on 26-27 July and 1-2 August Evolution of a coastal upwelling event during summer 2004 in the southern Taiwan Strait, submitted to Geophysical Research Letter.

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

  5. 1. SST Validation over Floriday Keys

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

  7. Data • 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 MODIS

  8. Method How to choose the good satellite SST for comparison: Calculating clim_weekly_mean: If the data-clim_weekly_mean <-4 then filtered, runs 3times, get the final climatology weekly mean.

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

  10. (a) Original (b) Median Filtered (c) Median + Clim4. Filtered (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. 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.

  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) Satellite-buoy Satellite-buoy LONF1 DRYF1 SST(buoy) SST(buoy)

  12. Matrix of sensor performance MLRF1 station

  13. Summary • 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 0.86-1.19; the satellite SST underestimate buoy SST, especially at high SST value. • 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. High Correlation / Linear relationship between CDOMSalinity base on field measurement Motivation and objective (Hu et al, 2003) 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?

  16. SeaWiFS ag443 in situ ag443 Validation of satellite CDOM absorption • In situ CDOM absorption (ag443) • 7 cruises in NEGOM, flow-through • Summer: NEGOM3, NEGOM6, NEGOM9 • Autumn: NEGOM4, NEGOM7 • Spring: NEGOM5, NEGOM8 • Ocean color product: ag443=adg443-ad443 • adg443 (CDOM+detritus absorption) • SeaDAS offers: • -carder (Carder et al, 1999) • -gsm01 (Garver and Siegel, 1997; Maritorena et al, 2002) • -qaa (Lee et al, 2002) • ad443 (detritus absorption) is derived from bbp555 by empirical function Satellite: adg443_qaa

  17. Validation of satellite CDOM absorption NEGOM3 NEGOM6 NEGOM9 summer The satellite estimates agree well with the ship data in most cruises. NEGOM4 NEGOM7 autumn NEGOM5 NEGOM8 Comparison of in situ ag443 and SeaWiFS derived adg443 spring Swf_adg_443_qaa red: ±2h; green: ±12h; blue: ±24h; black: ±48h Ship_ag_443

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

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

  22. Offshore region Offshore_summer Offshore_spring

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

  26. III. Variability of Yucatan upwelling cold water

  27. Sea Surface TemperatureSpace EOF Result Mode2 Demean spatial mean

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

  29. Mode 1 Mode 2 QuikSCAT wind field

  30. Variability of Yucatan upwelling cold water Week18 Week12 Week15 Week21 Week27 Week30 Week24 Week33 Climatology weekly mean SST in Yucatan shelf from March to September Week36 Week39

  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 1ºC) was maximum (>20000km2)between weeks 25 to 30 (in July).

  32. 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 Movement of thermal centroid with time. The label indicated the number of week

  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 Spring Summer

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

More Related