An Investigation of the Influences of SWOT Sampling and Errors on Ocean Eddy Observation
"> Figure 1
<p>Illustration of five types of coincidence status of eddies (CSE). Black lines represent the boundaries of eddies in <math display="inline"><semantics> <mrow> <mi>E</mi> <msub> <mi>D</mi> <mi>t</mi> </msub> </mrow> </semantics></math>. Red and blue lines represent the boundaries of eddies in <math display="inline"><semantics> <mrow> <mi>E</mi> <msub> <mi>D</mi> <mi>s</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>E</mi> <msub> <mi>D</mi> <mi>o</mi> </msub> </mrow> </semantics></math>. (<b>a</b>–<b>e</b>) present examples of “matched”, “split”, “merged”, “missed”, and “artifact”, respectively. The points denote surface centers of eddies.</p> "> Figure 2
<p>Snapshots of eddies and corresponding SLA data on 12 January 2015. (<b>a</b>,<b>c</b>,<b>e</b>) show the eddy identification results in the KE-EDt, KE-EDs, and KE-EDo dataset, respectively. The blue (red) lines represent the effective perimeter of cyclonic (anticyclonic) eddies, and the blue (red) points represent their surface centers. (<b>b</b>,<b>d</b>,<b>f</b>) show SLA data corresponding to (<b>a</b>,<b>c</b>,<b>e</b>), respectively.</p> "> Figure 3
<p>Geographical distribution of the averaged radius and amplitude on a 1° × 1° grid for (<b>a</b>,<b>b</b>) KE-EDt, (<b>c</b>,<b>d</b>) KE-EDs, and (<b>e</b>,<b>f</b>) KE-EDo, respectively.</p> "> Figure 4
<p>Effective perimeters and centers of eddies on 12 January 2015. Black, red, and blue lines represent the effective perimeters of eddies in KE-EDt, KE-EDs, and KE-EDo, respectively. (<b>a</b>) plots the eddies in these three datasets together. (<b>b</b>–<b>f</b>) show the effective perimeters of eddies labelled with “matched”, “missed”, “artifact”, “split”, and “merged”, respectively. Besides, those points represent their surface centers.</p> "> Figure 5
<p>Histograms of the eddy radius (left) and amplitude (right) for all datasets (the black, red and blue lines stand for KE-EDt, KE-EDs, and KE-EDo, respectively) in the Kuroshio Extension (KE) region. (<b>a</b>,<b>b</b>) show the histograms of all eddies in three datasets. (<b>c</b>,<b>d</b>), (<b>e</b>,<b>f</b>), (<b>g</b>,<b>h</b>), (<b>i</b>,<b>j</b>), and (<b>k</b>,<b>l</b>) correspond to the eddies of “matched”, “split”, “merged”, “missed”, and “artifact”, respectively. The values marked with dashed lines will be discussed in detail.</p> "> Figure 6
<p>The ratio of the number of “matched” eddies (the black line) and “missed” eddies (the red line) to the total number of eddies in KE-EDt with the radius (<b>a</b>) and amplitude (<b>b</b>). The red dashed lines indicate the ratio of “missed” eddies due to the sampling and mapping process. The values marked with black dashed lines will be discussed in detail.</p> "> Figure 7
<p>The scatter diagram of “matched” eddies: (<b>a</b>)/(<b>c</b>) are the radius/amplitude of “matched” eddies in KE-EDs versus corresponding eddies in KE-EDt, and (<b>b</b>)/(<b>d</b>) show the radius/amplitude of “matched” eddies in KE-EDo versus corresponding eddies in KE-EDs. The color shows the data density. The black lines are the diagonal. The red lines show the average radius (amplitude). The green lines show the average ratio (<math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> <mi>i</mi> <mi>u</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math>). The “CORR” represents the correlation coefficient. The values marked with dotted lines will be discussed in detail.</p> "> Figure 8
<p>The mean structures of eddies. (<b>a</b>)/(<b>b</b>) is the mean structure of all “matched”/“missed” eddies. (<b>c</b>) is the mean structure of cyclonic eddies for all eddies in the KE-EDo (black line), KE-EDs (red line) and KE-EDo (blue line). (<b>d</b>) shows the mean structure of the anticyclonic eddies corresponding to (<b>c</b>).</p> "> Figure 9
<p>The scatter diagram of eddy properties in KE-EDt (left) and SCS-EDt (right). (<b>a</b>,<b>b</b>) are the radii and amplitudes of all eddies, with the color representing the data density. The “missed” eddies are shown in (<b>c</b>,<b>d</b>). (<b>e</b>,<b>f</b>) are the ratio of the “missed” eddies to all eddies. The black line (KE-EDt) and the red line (SCS-EDt) in (<b>g</b>)/(<b>i</b>) represent the average amplitude/radius of all eddies. (<b>h</b>) and (<b>j</b>) represents the average ratio of “missed” eddies.</p> "> Figure 10
<p>Sampling density of Surface Water and Ocean Topography (SWOT) within a period (21 days). (<b>a</b>) Kuroshio Extension (KE) region. (<b>b</b>) South China Sea (SCS) region.</p> "> Figure 11
<p>Comparison of the loss rate of eddies between the two sub-datasets with similar characteristics selected from KE-EDt and SCS-EDt. (<b>a</b>) The black line (KE) and the red line (SCS) are the average amplitudes; the red shading shows the selected range of the radius and amplitude. (<b>b</b>) shows the eddy loss rate of the two sub-datasets. The black line stands for KE and red line stands for SCS.</p> "> Figure 12
<p>(<b>a</b>,<b>b</b>) show the along-track geostrophic velocity fields and SSH in the Oregon area. (<b>c</b>) shows the geostrophic velocity fields of the two eddies without errors. Two examples with observation errors are shown in (<b>d</b>,<b>e</b>). The contours of SSH are shown in the left, the geostrophic velocity fields are shown in the middle, and the right sub images present observation errors. Anticyclone (cyclone) eddies are located in the positions of orange (blue) circles.</p> "> Figure 13
<p>Power spectrum of the true SSH (red), the observation SSH (black), and the filtered observation SSH (blue).</p> ">
Abstract
:1. Introduction
2. Data and Methods
2.1. SSH Model Data
2.2. Mapping Procedure Based on Optimal Interpolation
2.3. Eddy Identification
2.4. CSE Determination
Algorithm 1: CSE determination for every eddy in three datasets |
Input: , ,; |
Output: the CSE label for every eddy in three datasets; |
begin: |
for i from 1 to m { |
find space-time matching eddies for in : return a sub dataset with eddies; |
if ( > 1) { |
check space-time matching eddies for in : return ; |
if ( != NULL) { |
set the CSE labels of eddies in ,, as “merged”;}}} |
for i from 1 to l { |
if ( without a CSE label in ){ |
find space-time matching eddies for in : return a sub dataset |
with eddies; |
find space-time matching eddies for in : return a sub dataset |
with eddies; |
if ( = 1 && = 1) { |
set the CSE labels of eddies in , , as “matched”} |
if (( > 1 || > 1) && ( & != 0)) { |
set the CSE labels of eddies in , , as “split”} |
if ( || = 0) { |
set the CSE labels of eddies in , , as “missed”}}} |
for i from 1 to m { |
if ( without a CSE label in ) { |
set the CSE labels of eddies as “artifact”}} |
for i from 1 to n { |
if ( without a CSE label in ) { |
set the CSE labels of eddies as “artifact”}} |
end |
3. Results of OSSEs in the Kuroshio Extension (KE) Region
4. Discussion
4.1. Comparison with OSSEs in the South China Sea (SCS)
4.2. The Influence of SWOT Errors on Small Scale Eddies (with Radius Scales of ~10 km)
- The eddy center is visible in the geostrophic velocity field;
- The rotating structure is visible in the geostrophic velocity field;
- The SSH contour is closed in the SSH field.
4.3. Underlying Assumptions and Biases
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Coverage | Model/Type | Spatial Resolution Lat × Lon (°) | Temporal Resolution (Day) |
---|---|---|---|
KE (24–44°N and 140–180°E) | HYCOM/ Gridded | 1/12.5 1/12.5 | 1 day |
SCS (5–25°N and 105–125°E) | |||
Oregon Coast (42–48°N and 124–130°W) | ROMS/ Gridded | 1/170 1/130 (~0.6 km ) | 1 day |
CSE | |||
---|---|---|---|
1 | 1 | 1 | matched |
1 | >1 | >1 | split |
1 | >1 | 1 | |
1 | 1 | >1 | |
>1 | 1 | 1 | merged |
1 | 0 | 0 | missed |
1 | ≥1 | 0 | |
1 | 0 | ≥1 | |
0 | 1 | 1 | artifact |
0 | 1 | 0 | |
0 | 0 | 1 |
Proportion of Detected Eddies In KE-Edt (%) | Proportion of Missed Eddies In KE-Edt (%) | ||||
---|---|---|---|---|---|
Matched | Split | Merged | Missed | ||
(Due to Sampling and Mapping) | (Due to SWOT Observation Errors) | (“Unstably Missed”) | |||
61.0 | 2.0 | 3.0 | 23.5 | 6.5 | 4.0 |
66.0 | 34.0 |
Proportion of Detected Eddies In SCS-Edt (%) | Proportion of Missed Eddies In SCS-Edt (%) | ||||
---|---|---|---|---|---|
Matched | Split | Merged | Missed | ||
(Due to Sampling and Mapping) | (Due to SWOT Observation Errors) | (“Unstably Missed”) | |||
58.5 | 0.5 | 1.0 | 29.0 | 6.0 | 5.0 |
60.0 | 40.0 |
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Ma, C.; Guo, X.; Zhang, H.; Di, J.; Chen, G. An Investigation of the Influences of SWOT Sampling and Errors on Ocean Eddy Observation. Remote Sens. 2020, 12, 2682. https://doi.org/10.3390/rs12172682
Ma C, Guo X, Zhang H, Di J, Chen G. An Investigation of the Influences of SWOT Sampling and Errors on Ocean Eddy Observation. Remote Sensing. 2020; 12(17):2682. https://doi.org/10.3390/rs12172682
Chicago/Turabian StyleMa, Chunyong, Xiaoxiao Guo, Haoxin Zhang, Jiankai Di, and Ge Chen. 2020. "An Investigation of the Influences of SWOT Sampling and Errors on Ocean Eddy Observation" Remote Sensing 12, no. 17: 2682. https://doi.org/10.3390/rs12172682