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Progresses in IMaRS
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Transcript of Progresses in IMaRS
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: [email protected]: 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