Progresses in Progresses in IMaRSIMaRSCaiyun ZhangCaiyun Zhang

Sept. 28, 2006Sept. 28, 2006

1.1. SST validation over Florida KeysSST validation over Florida Keys

2.2. Potential application of ocean color remote Potential application of ocean color remote sensing on deriving salinity in the NE Gulf of sensing on deriving salinity in the NE Gulf of Mexico (NEGOM)Mexico (NEGOM)

3.3. Analyzing seasonal variability of Yucatan Analyzing seasonal variability of Yucatan upwelling upwelling

4.4. Analyzing the spatio-temporal variability of SST Analyzing the spatio-temporal variability of SST and Chl in Florida Shelf by EOF method and Chl in Florida Shelf by EOF method

5.5. Using monthly SeaWiFS K490 (1997-2005) to Using monthly SeaWiFS K490 (1997-2005) to delineate the extension of Amazon river; Cutting delineate the extension of Amazon river; Cutting the monthly Pathfinder SST (1985-2005, 9km the monthly Pathfinder SST (1985-2005, 9km and 4km) over equatorial Atlantic oceanand 4km) over equatorial Atlantic ocean

6.6. Using SeaWiFS nLw555 to study the influence of Using SeaWiFS nLw555 to study the influence of Yangtz River plume on East China SeaYangtz River plume on East China Sea

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 July 31 July

1.1. SST validation over Florida KeysSST validation over Florida Keys

2.2. Potential application of ocean color remote Potential application of ocean color remote sensing on deriving salinity in Northeast Gulf of sensing on deriving salinity in Northeast Gulf of MexicoMexico

3.3. Analyzing seasonal variability of Yucatan Analyzing seasonal variability of Yucatan upwelling by EOF method (Empirical Orthogonal upwelling by EOF method (Empirical Orthogonal Function)Function)

4.4. Analyzing the spatio-temporal variability of SST Analyzing the spatio-temporal variability of SST and Chl in Florida Shelf by EOF method and Chl in Florida Shelf by EOF method

5.5. Using monthly SeaWiFS K490 (1997-2005) to Using monthly SeaWiFS K490 (1997-2005) to delineate the extension of Amazon river; Cutting delineate the extension of Amazon river; Cutting the Pathfinder SST (1985-2005) over equatorial the Pathfinder SST (1985-2005) over equatorial Atlantic oceanAtlantic ocean

6.6. Using SeaWiFS nLw555 to study the influence of Using SeaWiFS nLw555 to study the influence of Yangtz River plume on East China SeaYangtz River plume on East China Sea

1. SST Validation 1. SST Validation over Floriday Keysover Floriday Keys

Try different filtered method, to Try different filtered method, to generate reliable climatology and generate reliable climatology and anomaly imageryanomaly imagery

Accuracy of satellite SST? Which Accuracy of satellite SST? Which sensor performs better?sensor performs better?

Objective

• 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

DataData

MethodMethod

MethodMethod

ClimClim A weekly climatology filter (data-clim_weekly_mean < - 4ooC were C were filtered)filtered)

ClimmedianClimmedian Clim+ temporal (3 days) median filter (threshold: 2oC).

Clim4Clim4 a weekly climatology filter (threshold 4ooC)C)

Clim4meanClim4mean Clim4 + Clim4 + temporal (3 days) mean filter (threshold: 2oC).

Clim4medianClim4median Clim4 + Clim4 + temporal (3 days) median filter (threshold: 2oC).

stddevstddev -2*stddev<data--2*stddev<data-clim_weekly_mean<5*stddevclim_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:

Clim4rms=1.306n=9114stddev=0.964bias=-0.431

Clim4medianrms=1.052n=8407stddev=0.740bias=-0.303

stddevrms=1.280n=7731stddev=0.931bias=-0.406

Clim4meanrms=1.069n=8379stddev=0.753bias=-0.322

(Time difference: ±0.5hour)

SST(Buoy)S

ST

(S

ate

llit

e)

Climrms=1.313n=9260stddev=0.968bias=-0.407

Climmedianrms=1.055n=8511stddev=0.742bias=-0.284

Comparison of buoy vs. satellite SST for different filter method taken buoy LONF1 as example

(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.

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) SST(buoy)

Sate

llit

e-

bu

oy

Sate

llit

e-

bu

oy

DRYF1 LONF1

MLRF1 stationTime = day

Sat n12 n15 n16 n17 MODA MODT

RMS 0.998141 0.834389 0.953019 0.762205 1.0523 1.07388

STD 0.646759 0.566843 0.627104 0.518061 0.74212 0.760404

Mean error -0.089043 -0.07811 -0.18948 0.056513 -0.474 -0.55127

Slope 0.862195 0.917655 0.856241 0.885029 0.879508 0.901851

Intercept 3.52989 2.09751 3.63244 3.07124 2.76995 2.08494

Min error -3.81 -3.71 -2.72 -2.6 -3.89 -4.01

Max error 5.1 2.9 4.1 2.49 2.99 1.3

n_pairs 1547 808 229 304 250 228

Time = night

RMS 1.06148 0.917633 0.86838 0.840598 1.05231 0.967402

STD 0.680854 0.628166 0.582496 0.580619 0.665453 0.687512

Mean error -0.4138 -0.16118 -0.2116 -0.20845 -0.61812 -0.55268

Slope 0.809179 0.89902 0.887911 0.914948 0.931091 0.953582

Intercept 4.60366 2.52185 2.76889 2.06875 1.22457 0.680451

Min error -4.71 -4 -3.61 -4.41 -3.21 -3.81

Max error 3.9 2.6 3.19 2.6 2.09 1

n_pairs 1604 608 455 530 229 194

Matrix of sensor performanceMatrix of sensor performance

SummarySummary

The clim4median combined method [The clim4median combined method [a weekly climatology filter (threshold: 4oC)+ temporal (3 days) median filter (threshold: 2oC)] is is the best one to filter the cloud contaminated the best one to filter the cloud contaminated pixelspixels

Overall, the RMS error between buoy and Overall, the RMS error between buoy and satellite SST over Florida Keys varied between satellite SST over Florida Keys varied between 0.86-1.19; the satellite SST underestimate ; the satellite SST underestimate buoy SST, especially at high SST value.buoy SST, especially at high SST value.

The NOAA 17 performs better than the other The NOAA 17 performs better than the other satellites.satellites.

II. Potential application of II. Potential application of ocean color remote sensing ocean color remote sensing

on deriving salinity in on deriving salinity in Northeast Gulf of Mexico Northeast Gulf of Mexico

(NEGOM)(NEGOM)

Motivation and objectiveMotivation and objective

High Correlation / Linear relationship between High Correlation / Linear relationship between CDOMCDOMSalinity base on field measurement Salinity base on field measurement

Ocean color remote sensing (~1km)

CDOMIs there any possibility to derive the salinity from high resolution ocean color remote sensing? What’s the accuracy?

(Hu et al, 2003)

Validation of satellite Validation of satellite CDOM absorptionCDOM absorption

In situ CDOM absorption (aIn situ CDOM absorption (agg443)443) 7 cruises in NEGOM, flow-7 cruises in NEGOM, flow-

throughthrough Summer: NEGOM3, NEGOM6, Summer: NEGOM3, NEGOM6,

NEGOM9NEGOM9 Autumn: NEGOM4, NEGOM7Autumn: NEGOM4, NEGOM7 Spring: NEGOM5, NEGOM8Spring: NEGOM5, NEGOM8

Ocean color product:Ocean color product:in situ ag443

SeaW

iFS

ag

443Satellite: adg443_qaa

ag443=adg443-ad443 • ad443 (detritus

absorption) is derived from bbp555 by empirical function

• 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)

Comparison of in situ ag443 and SeaWiFS derived adg443

summer

autumn

spring

NEGOM3 NEGOM6 NEGOM9

NEGOM4 NEGOM7

NEGOM5 NEGOM8

red: ±2h; green: ±12h; blue: ±24h; black: ±48h

Validation of satellite CDOM Validation of satellite CDOM absorptionabsorption

Ship_ag_443

Sw

f_ad

g_4

43_

qaa

The satellite estimates agree well with the ship data in most cruises.

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

/ad

g_4

43(m

-1)

(Time difference: +-24hour)

Time difference nn SlopeSlope

IntercepInterceptt RR RMS(%)RMS(%) Bias(%)Bias(%) Log_RMSLog_RMS Log_biasLog_bias

NEGOM4

2h 91 1.638 -0.038 0.901 43.542 29.489 0.149 0.098

12h 352 1.034 0.015 0.723 48.240 17.441 0.159 0.043

24h 442 1.065 0.008 0.762 45.195 8.070 0.160 0.003

48h 723 1.065 0.006 0.781 43.875 10.448 0.157 0.014

NEGOM5

2h 305 0.745 0.027 0.636 105.597 25.843 0.183 0.047

12h 2612 0.697 0.011 0.785 47.936 4.781 0.144 -0.006

24h 4887 0.558 0.016 0.728 46.644 6.600 0.178 -0.006

48h 9211 0.558 0.018 0.531 68.972 11.285 0.198 0.002

NEGOM6

2h 131 0.790 -0.009 0.691 49.174 -36.026 0.371 -0.265

12h 1171 0.478 0.018 0.595 42.650 -23.128 0.262 -0.161

24h 2648 0.518 0.012 0.586 47.534 -25.779 0.292 -0.186

48h 4748 0.518 0.013 0.527 49.453 -25.292 0.296 -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.

Relationship between Relationship between salinity and satellite salinity and satellite

adg_443adg_443

-90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

O ffshore

Coast

Coastal Coastal region region

0 0.04 0.08 0.12 0.16 0.2sw f_ a d g

34

34.4

34.8

35.2

35.6

36sa

linity

Sum m er

Equation: Y = -6.88591114 * X + 35.65574571N um ber of data po in ts used = 306R -squared = 0.524281

0 0.04 0.08 0.12 0.16sw f_ a d g

33

34

35

36

salin

ity

sp ring

0 0.04 0.08 0.12 0.16sw f_ a d g

33

34

35

36

salin

ity

au tum n

Relationship between seawifs_adg_443_qaa and salinity in the coastal region (±24h)

Rms std_err Min_diff Max_diff

0.228 0.131 -0.655 0.628

SeaWiFS_adg443

Sali

nti

y

Statistic result for summer season (range of salinity: 34-36) :

autumn

spring

Offshore region Offshore region

Offshore_summer Offshore_spring

Slope Intercept n r Rms Std_err Min_diff Max_diff

Spring -62.481 36.957 4867 -0.828 0.87 0.758 -4.138 7.271

Summer

-60.369 34.909 2552 -0.712 1.87 1.049 -5.632 5.251

Comparison of mapping salinity from ship and Comparison of mapping salinity from ship and Seawifs derived for NEGOM5 spring cruiseSeawifs derived for NEGOM5 spring cruise

NEGOM 5 (spring) cruise:

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

31

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

31

N 5_ship_ag_443

N 5_ship_sa lin ity

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4sw f_adg_443

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

20

22

24

26

28

30

32

34

36sw f_sa lin ity

In situ ag443 SeaWiFS adg443

In situ salinity Satellite derive salinity(Offshore)

NEGOM 6 (summer) cruise:

Comparison of mapping salinity from ship and Comparison of mapping salinity from ship and Seawifs derive for NEGOM6 summer cruiseSeawifs derive for NEGOM6 summer cruise

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

31

N6_ship_ag_443

-90 -88 -86 -84 -8226

27

28

29

30N6_ship_salin ity

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

0

0 .0 2

0 .0 4

0 .0 6

0 .0 8

0 .1

0 .1 2

0 .1 4

0 .1 6

0 .1 8

0 .2

0 .2 2

0 .2 4sw f_adg_443_qaa

-91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

26

27

28

29

30

31

32

33

34

35

36sw f_offshore_salin ity

-90 -89 -88 -87 -86 -85 -84 -83 -82 -8126

27

28

29

30

26

27

28

29

30

31

32

33

34

35

36

sw f_coast_salin ity

In situ ag443 SeaWiFS adg443

In situ salinitySatellite derive salinity

(offshore)

Satellite derive salinity(Coast)

ConclusionConclusion

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

III. Variability of III. Variability of Yucatan upwelling Yucatan upwelling

cold watercold water

Sea Surface TemperatureSea Surface TemperatureSpace EOF ResultSpace EOF Result

1 0 2 0 3 0 4 0 5 0W e e k

- 0 . 1

0

0 . 1

0 . 2

0 . 3

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

M ode1

-92 -91 -90 -89 -88 -87 -86

19

20

21

22

23

24

25

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

M ode173.69%

-92 -91 -90 -89 -88 -87 -86

19

20

21

22

23

24

25

-11

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

M ode213.42%

-92 -91 -90 -89 -88 -87 -86

19

20

21

22

23

24

25

- 7

- 6

- 5

- 4

- 3

- 2

- 1

0

1

2

3

4

M ode38.22%

1 0 2 0 3 0 4 0 5 0W e e k

- 0 . 4

- 0 . 2

0

0 . 2

0 . 4

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

M ode3

1 0 2 0 3 0 4 0 5 0W e e k

- 0 . 1

0

0 . 1

0 . 2

0 . 3

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

Mode2

Demean spatial mean

1 0 2 0 3 0 4 0 5 0W e e k

- 2

- 1

0

1

2A

mp

litu

de

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

10 20 30 40 50W e e k

-3

-2

-1

0

1

2

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

10 20 30 40 50W e e k

-3

-2

-1

0

1

2

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

M ode1

M ode2

M ode3

Chl (SVD/Time EOF)

59.55%

-92 -91 -90 -89 -88 -87 -86

19

20

21

22

23

24

-0 .28

-0.24

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

M ode1

-92 -91 -90 -89 -88 -87 -86

19

20

21

22

23

24

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

M ode2

-92 -91 -90 -89 -88 -87 -86

19

20

21

22

23

24

-0.18

-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

M ode3

15.0%

5.4%

Mode 1 Mode 2

1 0 2 0 3 0 4 0 5 0W e e k

- 2

- 1

0

1

2

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

M ode1(61.34% )

1 0 2 0 3 0 4 0 5 0W e e k

- 3

- 2

- 1

0

1

2

Am

plit

ud

e

Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec

M ode2(15.92% )

QuikSCAT wind field

Week15 Week18 Week21

Week24 Week27 Week30 Week33

Week36 Week39

Week12

Climatology weekly mean SST in Yucatan shelf from March to September

Variability of Yucatan upwelling cold waterVariability of Yucatan upwelling cold water

1 0 1 5 2 0 2 5 3 0 3 5 4 0W e e k s

0

5 0 0 0

1 0 0 0 0

1 5 0 0 0

2 0 0 0 0

2 5 0 0 0

Are

a

space anom aly C

space anom aly C

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).

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

ii

iii

c T

Txx

ii

iii

c T

Tyy

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)

-90 -89.6 -89.2 -88.8 -88.4 -88

L o n titu de

21.2

21.4

21.6

21.8

22

22.2

La

titu

de

1 41 5

1 61 71 8

1 92 0

2 12 22 32 4

2 5

2 62 7

2 82 93 0

3 13 23 3

3 4

3 53 6

3 7

3 8

Week 14: early AprilWeek 31: the end of JulyWeek 38: mid September

Welcome to visit me at Welcome to visit me at XMUXMU

Contact information:Department of oceanography, Xiamen

UniversityXiamen, China, 361005

Email: cyzhang@xmu.edu.cnTel: 86-592-2188071 (office),

2186871 (lab)

Thank you!Thank you!

Offshore region Offshore region

Offshore_summer Offshore_spring

Slope Intercept

n r Rms Std_err Min_diff Max_diff

-60.369 34.909 2552 -0.712 1.87 1.049 -5.632 5.251

SpringSlope Intercept n r Rms Std_err Min_diff Max_diff

-62.481 36.957 4867 -0.828 0.87 0.758 -4.138 7.271

Summer

Week 35

Week 25Week 20Week 15

Week 30 Week 40

Weekly climatology QuikSCAT wind vector from early April to the end of September