Determination of Accurate Dynamic Topography for the Baltic Sea Using Satellite Altimetry and a Marine Geoid Model
<p>Inter-relations between the participating datasets (hydrodynamic model, tide gauges, and satellite altimetry data), geoid model, and different reference ellipsoids. Virtual Station (VS) is used to correct the HDM near the TG. The top inset illustrates the VS selection principles along the SA tracks.</p> "> Figure 2
<p>Determination of the HDM bias and selection principles of virtual stations (VSs). (<b>a</b>) The horizontal profile of a single SA pass and selection of VSs along the SA track near TG locations. (<b>b</b>) Determination of the HDM bias at the location of VS (<math display="inline"><semantics> <mrow> <mi>B</mi> <mi>i</mi> <mi>a</mi> <msubsup> <mi>s</mi> <mrow> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> <mrow> <mi>V</mi> <mi>S</mi> </mrow> </msubsup> </mrow> </semantics></math>) using the <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>T</mi> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math> by adding the relative DT.</p> "> Figure 3
<p>Workflow of different stages of developed methodology and analysis for validation and assessment process (the corresponding section, table, or figure of each stage is denoted by blue font). The used data products (in brackets) refer to the case study.</p> "> Figure 4
<p>Characteristics of the study area: (<b>a</b>) Location of the Baltic Sea (the background represents the NKG2015 geoid model) together with the location of the tide gauges (triangle symbols). The names of sub-basins and main islands are denoted in white and red, respectively. (<b>b</b>) TG data availability between December 2016 to April 2019 of each TG ID (<b>b</b>). The TG numbering is clockwise, starting from the eastmost Estonian TG station and finishing with the Russian Kronstadt TG station (No. 74), which is located at the eastmost end of the Gulf of Finland.</p> "> Figure 5
<p>Steps for TG observation data reconstructions and corrections.</p> "> Figure 6
<p>Statistics of HDM discrepancies with respect to participating TG stations. (<b>a</b>) Means (characterized by the circle size) and standard deviations (characterized by the colors) of discrepancies between HDM and TGs (Equations (16) and (17)) during 2017–2019 over the Baltic Sea. (<b>b</b>) Monthly average of HDM discrepancies over three years (Equation (16)). (<b>c</b>) Monthly average of HDM discrepancies at six selected TG locations (one station in each country with the largest STD or mean). TG IDs are explained in <a href="#remotesensing-15-02189-f004" class="html-fig">Figure 4</a>a as well as in <a href="#remotesensing-15-02189-t0A1" class="html-table">Table A1</a> (<a href="#app1-remotesensing-15-02189" class="html-app">Appendix A</a>).</p> "> Figure 7
<p>Coverage of SA passes within the study area. (<b>a</b>) Sentinel 3A (note that S3B has almost the same track patterns) and (<b>b</b>) Jason-3 missions. For each mission ascending pass numbers are shown. The locations of VS and TG stations are denoted by red circles and black triangles, respectively.</p> "> Figure 8
<p>SA data preprocessing steps diagram including data filtering, harmonization, and corrections.</p> "> Figure 9
<p>(<b>a</b>) Comparison of S3A pass #272 <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math> (blue dots) with the TG-corrected HDM (<math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>H</mi> <mi>D</mi> <mi>M</mi> <mo>−</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math>) (green line) for 3 cycles (representing different seasons) in 2017. The <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math> moving average is denoted by the blue solid line, the grey zones showing masked near coast and land areas, and the triangles denote locations of virtual stations; (<b>b</b>) the location of the S3A pass #272 in the Baltic Sea.</p> "> Figure 10
<p>Along-track <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> <mo> </mo> <mfenced> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>,</mo> <msub> <mi>λ</mi> <mi>s</mi> </msub> </mrow> </mfenced> </mrow> </semantics></math> for 4 SA passes ((<b>a</b>): S3A#158, (<b>b</b>): S3A#169, (<b>c</b>): JA3#111, and (<b>d</b>): JA3#16) considering all available cycles during 2017–2019. The blue line represents the moving median of <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> <mo> </mo> <mfenced> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>,</mo> <msub> <mi>λ</mi> <mi>s</mi> </msub> </mrow> </mfenced> </mrow> </semantics></math> and the dashed red lines represent the moving standard deviation “envelope”. The average of <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> <mo> </mo> <mfenced> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>,</mo> <msub> <mi>λ</mi> <mi>s</mi> </msub> </mrow> </mfenced> </mrow> </semantics></math> is also denoted. The bottom row (<b>e</b>–<b>h</b>) represents each pass location in the Baltic Sea. The grey zone denotes masked land areas. The potential problematic areas are classified into three types. Yellow shaded regions: the suspected geoid model problems; green shade: TG records problem (or the HDM problem); purple shade: SA problem, possibly due to sea ice presence or land contamination.</p> "> Figure 11
<p>The averaged <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> <mo> </mo> </mrow> </semantics></math>during 2017–2019 over the Gulf of Bothnia of S3A for two descending (<b>a</b>) and three ascending passes (<b>b</b>), denoted in blue lines. The geoid undulations along the passes are denoted in magenta-color lines (the different line styles represent each pass). The yellow masked area is the location of steep geoid slopes, which may cause deteriorations in <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> <mo>.</mo> </mrow> </semantics></math> The locations of yellow masked areas in the left-hand-side profiles are denoted by red rectangles in the right-hand-side maps (<b>c</b>,<b>d</b>), whereas the NKG2015 geoid model is in the background. Note that for pass#272 the drastic drop at 63° is most likely due to land contamination.</p> "> Figure 12
<p>Statistics of the along-track SA examination. (top row) Mean <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math>, discrepancies between SA along-track DT data (<math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>) and <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>H</mi> <mi>D</mi> <mi>M</mi> <mo>−</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math>) during 2017–2019 for (<b>a</b>) S3B, (<b>b</b>) S3A and (<b>c</b>) JA3 missions (Equation (10)). The <math display="inline"><semantics> <mrow> <mfenced close="|" open="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </mfenced> <mo>></mo> <mn>20</mn> <mo> </mo> <mi>c</mi> <mi>m</mi> </mrow> </semantics></math> are represented as black dots in the <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> plots and this is excluded from the calculation of the RMSE value of the whole basin per each mission (Equations (12) and (14)). In addition, (bottom row; (<b>d</b>–<b>f</b>)) associated STDs of <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> (Equation (11)) larger than 20 cm are denoted in black dots.</p> "> Figure 12 Cont.
<p>Statistics of the along-track SA examination. (top row) Mean <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math>, discrepancies between SA along-track DT data (<math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math>) and <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>H</mi> <mi>D</mi> <mi>M</mi> <mo>−</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math>) during 2017–2019 for (<b>a</b>) S3B, (<b>b</b>) S3A and (<b>c</b>) JA3 missions (Equation (10)). The <math display="inline"><semantics> <mrow> <mfenced close="|" open="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </mfenced> <mo>></mo> <mn>20</mn> <mo> </mo> <mi>c</mi> <mi>m</mi> </mrow> </semantics></math> are represented as black dots in the <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> plots and this is excluded from the calculation of the RMSE value of the whole basin per each mission (Equations (12) and (14)). In addition, (bottom row; (<b>d</b>–<b>f</b>)) associated STDs of <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> (Equation (11)) larger than 20 cm are denoted in black dots.</p> "> Figure 13
<p>Sea ice concentration over Baltic Sea for March (<b>a</b>) and June (<b>b</b>) 2017 (source: gridded ice chart model available from Copernicus Marine Service Information) and the comparison between <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>H</mi> <mi>D</mi> <mi>M</mi> <mo>−</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> (green) and <math display="inline"><semantics> <mrow> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math> (blue) over pass#158 in two cycles, cycle 15, March (<b>c</b>) and cycle 19, June (<b>d</b>) 2017.</p> "> Figure 14
<p>Problematic DT determination areas over the Baltic Sea. (<b>a</b>) Normalized mean of <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>D</mi> <msub> <mi>T</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math>) of S3A data (<b>a</b>) and JA3 data (<b>b</b>) over the Baltic Sea in 2017. The problematic areas are enclosed by colored rectangles (and numbers) to classify the possible reasons. Geoid: yellow, SA: purple, and HDM or TG: green. Four selected passes (two passes for S3A and two passes for JA3) are specified to illustrate the possible reasons (cf. <a href="#remotesensing-15-02189-f012" class="html-fig">Figure 12</a>).</p> "> Figure 15
<p>Along-track DT and <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>e</mi> <mi>a</mi> <msub> <mi>n</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> (Equation (10)) of four selected passes (cf., <a href="#remotesensing-15-02189-f014" class="html-fig">Figure 14</a>). The along-track mean DT of SA is denoted in blue dots (whereas the blue solid line is the moving median of 0.5° latitude window) and HDM in green line (left axis). The <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>E</mi> <mi>A</mi> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>A</mi> <mo>−</mo> <mi>H</mi> <mi>D</mi> <mi>M</mi> </mrow> </msub> </mrow> </semantics></math> is represented on the right axis by magenta color (<b>a</b>,<b>c</b>) for two S3A passes at almost the same locations as two JA3 passes (<b>b</b>,<b>d</b>). The problematic areas of possible geoid modeling are highlighted in yellow; the purple areas are due to poor quality of SA data near the land (vertical dashed lines represent the locations of islands). The grey zones mask land areas.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. TG/HDM Based DT Estimates
2.1.1. General Overview of the Method
2.1.2. Detailed Method
2.2. Estimation of DT from Satellite Altimetry and Statistical Examinations
- values larger than a specific predefined threshold (|| > threshold) are considered as gross errors and removed from the data points. This threshold value (here selected as 1.5 m) corresponds to the study area characteristics that depend on historical extrema of the DT occurring in the study area (which is ~1.3 m in the Baltic Sea);
- The erratic are identified as those three times larger than the standard deviation (STD) of the mean value of the whole track in a cycle (the longer tracks are divided into sub-tracks to obtain more homogeneous selections);
- The outliers are detected as elements more than three local scaled moving medians (MADs) from the median over the 0.5° latitude (~55 km) window length along the track to have a smooth low-pass behavior.
3. Study Area and Datasets
3.1. Baltic Sea
3.2. Datasets
3.2.1. Tide Gauge Stations
3.2.2. Hydrodynamic Model
3.2.3. Geoid Model
3.2.4. Satellite Altimetry
4. Results
4.1. SA Along-Track Performance
4.2. Evaluation of DT Accuracy and Identification of Problematic Areas
4.2.1. Along-Track over Baltic Sea
4.2.2. DT Examination over the Entire Baltic Sea
4.3. Spatial Pattern of Discrepancies
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
ID | TG Station (Country) | Latitude (°N) | Longitude (°E) | |
---|---|---|---|---|
1 | Narva-jõesuu | EE | 59.46905 | 28.04211 |
2 | Kunda | EE | 59.52100 | 26.54173 |
3 | Loksa | EE | 59.58447 | 25.70721 |
4 | Pirita | EE | 59.46887 | 24.82081 |
5 | Paldiski | EE | 59.35076 | 24.04932 |
6 | Dirhami | EE | 59.20843 | 23.49693 |
7 | Haapsalu | EE | 58.95801 | 23.52743 |
8 | Heltermaa | EE | 58.86555 | 23.04714 |
9 | Ristna | EE | 58.92121 | 22.05518 |
10 | Roomassaare | EE | 58.21725 | 22.50377 |
11 | Virtsu | EE | 58.57225 | 23.51126 |
12 | Pärnu | EE | 58.38747 | 24.48196 |
13 | Häädemeeste | EE | 58.03745 | 24.46360 |
14 | Ruhnu | EE | 57.78354 | 23.26350 |
15 | Salacgrīva | LV | 57.75528 | 24.35361 |
16 | Skulte | LV | 57.31583 | 24.40944 |
17 | Daugavgrīva | LV | 57.05944 | 24.02333 |
18 | Mērsrags | LV | 57.33472 | 23.13278 |
19 | Kolka | LV | 57.73722 | 22.59278 |
20 | Ventspils | LV | 57.39556 | 21.53444 |
21 | Liepāja | LV | 56.51556 | 20.99944 |
22 | Klaipeda | LT | 55.73024 | 21.08112 |
23 | Gdynia | PL | 54.51770 | 18.55520 |
24 | Leba | PL | 54.76340 | 17.55050 |
25 | Ustka | PL | 54.58800 | 16.85380 |
26 | Kolobrzeg | PL | 54.18660 | 15.55340 |
27 | Swinoujscie | PL | 53.90840 | 14.25430 |
28 | Greifswald | DE | 54.09280 | 13.44610 |
29 | Sassnitz | DE | 54.51080 | 13.64310 |
30 | Warnemünde | DE | 54.16972 | 12.10333 |
31 | Travemünde | DE | 53.95810 | 10.87220 |
32 | Rodby | DK | 54.65000 | 11.35000 |
33 | Tejn | DK | 55.25000 | 14.83330 |
34 | Rodvig | DK | 55.25420 | 12.37280 |
35 | Dragor | DK | 55.60000 | 12.68330 |
36 | Helsingborg sjöv | SE | 56.04460 | 12.68700 |
37 | Barsebäck | SE | 55.75640 | 12.90330 |
38 | Skanör | SE | 55.41670 | 12.82940 |
39 | Ystad sjöv | SE | 55.42270 | 13.82570 |
40 | Simrishamn | SE | 55.55750 | 14.35780 |
41 | Karlshamn sjöv | SE | 56.15420 | 14.82130 |
42 | Kalmar sjöv | SE | 56.67130 | 16.38880 |
43 | Oskarshamn | SE | 57.27500 | 16.47810 |
44 | Ölands norra udde | SE | 57.36610 | 17.09720 |
45 | Visby | SE | 57.63920 | 18.28440 |
46 | Västervik sjöv | SE | 57.74820 | 16.67470 |
47 | Arkö | SE | 58.48430 | 16.96070 |
48 | Landsort norra | SE | 58.76890 | 17.85890 |
49 | Loudden sjöv | SE | 59.34130 | 18.13730 |
50 | Forsmark | SE | 60.40860 | 18.21080 |
51 | Bönan sjöv | SE | 60.73840 | 17.31860 |
52 | Ljusne sjöv | SE | 61.20670 | 17.14520 |
53 | Spikarna | SE | 62.36330 | 17.53110 |
54 | Lunde sjöv | SE | 62.88650 | 17.87640 |
55 | Skagsudde sjöv | SE | 63.19060 | 19.01190 |
56 | Holmsund sjöv | SE | 63.68030 | 20.33310 |
57 | Furuögrund | SE | 64.91580 | 21.23060 |
58 | Strömören sjöv | SE | 65.54970 | 22.23830 |
59 | Kalix-storön | SE | 65.69690 | 23.09610 |
60 | Kemi | FI | 65.67337 | 24.51526 |
61 | Oulu | FI | 65.04030 | 25.41820 |
62 | Raahe | FI | 64.66630 | 24.40708 |
63 | Pietarsaari | FI | 63.70857 | 22.68958 |
64 | Vaasa | FI | 63.08150 | 21.57118 |
65 | Kaskinen | FI | 62.34395 | 21.21483 |
66 | Mäntyluoto | FI | 61.59438 | 21.46343 |
67 | Rauma | FI | 61.13353 | 21.42582 |
68 | Föglö | FI | 60.03188 | 20.38482 |
69 | Turku | FI | 60.42828 | 22.10053 |
70 | Hanko | FI | 59.82287 | 22.97658 |
71 | Helsinki | FI | 60.15363 | 24.95622 |
72 | Porvoo | FI | 60.20579 | 25.62509 |
73 | Hamina | FI | 60.56277 | 27.17920 |
74 | Kronstadt | RU | 59.96670 | 29.75000 |
TG ID | S3A | JA3 | S3B 1 |
---|---|---|---|
1 | 72,197,311,425,528,642 | 92,168,187 | 83,197,311,425,528,642,756 |
2 | 83,197,411,528 | 72,187 | 83,197,311,414,528 |
3 | 83,300,414,739 | - | 83,300,414,739 |
4 | 186,311,625 | 16 | 186,300,625,739 |
5 | 72,186,511,625 | 16,111 | 186,511,625 |
6 | 72,511 | 16,111,194 | 72,397,511 |
7 | 72,511 | 194 | 72,397 |
8 | 397,72 | 111,194 | 72,283,397 |
9 | 283,683,728 | 111,194 | 728,283 |
10 | 72,169,283,728 | 118 | 72,169,186,283,397 |
11 | 72,186,397 | 187,194 | 72,186,397 |
12 | 186,300,511 | 187,194 | 186,300,511 |
13 | 300,511 | 187,194 | 300,511 |
14 | 186,283 | 187,194 | 72,169,186,283 |
15 | 300 | 118,187 | 397 |
16 | 300 | 118,194 | 397 |
17 | 397,283,300 | 118 | 283,300,397 |
18 | 300,186,283 | 118 | 283,300,397,186 |
19 | 72,169,186,283 | 118,187 | 283,72,169,186 |
20 | 55,169,728 | 187,118 | 55,72,169 |
21 | 55,597,711,728 | 187,42 | 55,72,597,711,728 |
22 | 369,483,597,711,728 | 187,220,9,144,187,220 | 72,369,483,597,711,728 |
23 | 369,500,597,614,728 | 144,187,68 | 141,255,369,483,500,597,614,728 |
24 | 255,369,386,500,614,27 | 187,68 | 141,255,369,386,500,614 |
25 | 27,141,255,272,386,500,683 | 68,187,246 | 141,255,272,386,500,683 |
26 | 27,158,272,386,569,683 | 246,68,11 | 27,158,272,386,569,683 |
27 | 44,158,272,455,569,683 | 246,111 | 44,158,199,272,455,569,683 |
28 | 44,455,700 | 246,111 | 44,158,199,272,455,569,683 |
29 | 44,227,341,455,700 | 35,111,170 | 44,158,199,341,455,569,700 |
30 | 113,227,244,341,472,586,655,700 | 35,170 | 85,113,216,227,341,541,558,586,672,700 |
31 | 113,227,244,358,472,586,655,769 | 35,170,213 | 85,113,216,227,358,472,541,558,586,655,672,769 |
32 | 113,227,244,341,358,472,586,655,769 | 35,170,213 | 85,102,113,216,227,244,341,358,472,541,558,586,655,672,769 |
33 | 158,569,683,27,44, | 68,111 | 569,683 |
34 | 341,586 | 246,35 | 85,199,216,341,455,586,672,700,769 |
35 | 455,586 | 246,35 | 199,216,455,586,672 |
36 | 16,130,244,341,358,455,472,586 | 137,213,246 | 16,102,130,199,216,227,244,302,341,358,444,455,472,541,558,586,672,758 |
37 | 455,586 | 35,246 | 16,102,130,199,216,302,455,586,672,758 |
38 | 455,586,700 | 246,35 | 199,341,455,569,586,700 |
39 | 44,569,700 | - | 199,700 |
40 | 27,44,569,683,700 | 68 | 27,44,569,683 |
41 | 227,44,141,158,683 | 68,144 | 27,44,141,158,683 |
42 | 27,141,158,255 | 144 | 27,141,158,255 |
43 | 158,255,369,386,483,500 | 35,144,220 | 158,255 |
44 | 158,255,369,386,483,500 | 35,144,220 | 158,255,369,386,483 |
45 | 386,483,597 | 35,220 | 158,255,369,386,483 |
46 | 158,255,369,483,500 | 35,220 | 158,255,369,483 |
47 | 158,369,483,500 | 42,220 | 158,369,483,597 |
48 | 158,272,369,483,597 | 42,118,213 | 158,272,369,386,483,597 |
49 | 272,4,597,711 | 118,213 | 272,711 |
50 | 44,158,272,711 | 61,118,137,194,213 | 44,158,272,711 |
51 | 44,700,711 | 61,137,194 | 44,700 |
52 | 44,700,711 | 61,194,239,16 | 700,711 |
53 | 55,700,711 | 61,92,239,16 | 55,169,711 |
54 | 55,169,283,700 | 92,163,168,239,244 | 55,169,283,700 |
55 | 44,169,283,397,511,700 | 163,168,244 | 44,169,397,511,700 |
56 | 44,283,397,511,625,739 | 11,66,87,142,163,218,244 | 44,283,397,511,625,700,739 |
57 | 44,83,625,739 | 11,40,66,87,116,142,163,189,218 | 44,83,158,197,311 |
58 | 59,158,197,272,428 | 14,37,113,116,189,192 | 83,158,197,272,311,425 |
59 | 272,311,386,425,539 | 14,87,113,116,189,192 | 272,311,386,425,539 |
60 | 272,311,386,425,539 | 14,87,113,116,189,192 | 272,311,386,425,539 |
61 | 386,425,500,539 | 11,40,87,116,189,192 | 311,386,425,539 |
62 | 197,311,386,425,500 | 11,40,87,116,142,163,189,192,244 | 83,197,272,386,625,739 |
63 | 197,272,311,386,511,625,739 | 66,142,163,218,239,244 | 83,197,272,386,625,739 |
64 | 272,511,625,739 | 61,66,142,168,239,244 | 272,511,625,739 |
65 | 272,386,511 | 61,92,137,168,244 | 272,386,511 |
66 | 386,397 | 61,92,137,168 | 272,386,397 |
67 | 272,397,500 | 92,137,213 | 283,386,397,500 |
68 | 158,169,272,283,386,500,614 | 16,35,118,194,213 | 169,272,283,386,397,500,597,614 |
69 | 283,397,500,614,728 | 16,35,213 | 283,386,397,500,614,728 |
70 | 72,397,511,614,625,728 | 16,35 | 72,283,397,511,614,625,728 |
71 | 83,186,300,625,739 | 92,111 | 72,83,186,300,625,739 |
72 | 83,186,197,300,414 | 92,111,186 | 83,186,197,300,414,628,739 |
73 | 83,197,300,311,414,425,528,642,756 | 111,168,187 | 83,197,300,311,414,425,528,642 |
74 | 311,425,528,539,642,756 | 168,187 | 100,311,425,539,642,756 |
JA Pass# | TG ID | S3 Pass# | TG ID Crossed Over |
---|---|---|---|
9 | 21,22,23 | 16 | 36 |
11 | 56,57,58,60,61,62 | 27 | 24,41,25,26,40,42 |
14 | 58,59,60 | 44 | 27,28,40,41,51,56,57,58,55,50,52,29,39 |
16 | 4,5,6,70,69,68,52,53 | 55 | 20,53,21,54 |
35 | 29,30,31,32,34,35,38,37,43,44,45,46,70,69,68 | 72 | 6,8,10,19,7,11,5,70,1,22 |
37 | 58,59 | 83 | 2,72,63,58,3,71,73,57 |
40 | 57,58,61,62 | 113 | 30,31,32 |
42 | 20,21,48,47 | 130 | 36 |
61 | 50,51,52,53,64,65,66 | 141 | 24,42,25,41 |
66 | 63,64,56,57 | 158 | 59,50,47,46,43,26,27,41,42,44,48,68,58 |
68 | 23,24,25,40,41,26 | 169 | 20,68,54,19,10,55 |
87 | 56,57,60,61,62 | 186 | 18,14,5,71,4,72,11,12,19 |
92 | 1,2,71,72,65,66,67,53,54 | 197 | 58,62,73,1,2,72,63,59 |
111 | 26,27,28,9,8,6,5,72,73,71,29 | 227 | 29,31,30,32 |
113 | 58,59,60 | 244 | 30,32,36,31 |
116 | 57,58,59,60,61,62 | 255 | 23,43,44,24,25,42,46 |
118 | 50,49,48,68,10,14,15,16,17,18,19 | 272 | 25,48,65,64,60,67,26,27,49,50,68,63,59 |
137 | 36,50,51,65,66,67 | 283 | 55,9,10,19,54,56,68,69,14,18,17 |
142 | 56,57,62,63,64 | 300 | 72,3,12,13,15,17,71,73,4,16,18 |
144 | 21,22,23,41,42,43,44 | 311 | 1,73,62,59,74,58,60,61 |
163 | 62,63,54,55,56,57 | 341 | 29,32,34,30,36 |
168 | 1,72,73,74,66,65,64,55,54 | 358 | 31,32,36 |
170 | 29,30,31,32 | 369 | 47,44,22,23,24,46,48, |
187 | 73,74,1,11,12,13,14,18,2,19,20,21,22,23,24,25 | 386 | 24,45,68,63,66,60,61,62,65,25,26,44 |
189 | 57,58,59,60,61,62 | 397 | 55,67,69,8,11,17,66,56,70 |
192 | 58,59,60,61,62 | 414 | 2,73,3,72 |
194 | 50,51,52,68,15,13,12,11,9,8,7,6 | 425 | 60,62,74,1,73,61,59 |
213 | 31,36,48,49,50,67,68,69,32 | 455 | 27,28,38,37,36,29,35 |
218 | 62,63,57,56 | 472 | 30,31,36,32 |
220 | 21,22,45,44,43,46,47 | 483 | 48,45,22,49,47,44,46,23 |
239 | 52,53,54,63,64 | 500 | 23,67,68,61,62,69,24,25,47,44,46 |
244 | 54,55,56,63,64,65 | 511 | 6,12,70,56,5,13,7,64,65,63,55 |
246 | 25,26,27,28,34,35,36,37,38 | 528 | 2,73,1,74 |
539 | 74,61,60 | ||
569 | 39,26,27,40 | ||
586 | 32,34,35,37,30,31,36,38 | ||
597 | 21,49,22,23,45,48 | ||
614 | 23,69,24,70,68 | ||
625 | 4,71,64,56,5,70,63,57 | ||
642 | 1,73,74 | ||
655 | 30,31,32 | ||
683 | 25,41,26,27,40,39 | ||
700 | 28,39,52,55,51,53,54,40,38,29,30 | ||
711 | 21,50,52,22,49,51,53 | ||
728 | 9,20,21,22,70,23,69,10 | ||
739 | 3,71,63,57,64,56,58 | ||
756 | 74,73 | ||
769 | 31,32 |
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ID | Country 1 | Vertical Datum 2 | No. TGs | No. of Data Gaps 3 [h] | Data Provider |
---|---|---|---|---|---|
1–14 | ▲Estonia | EH2000 | 14 | 1128 | www.ilmateenistus.ee, accessed on 18 February 2020 |
15–21 | ▲Latvia | LAS2000,5 | 7 | 56 | www.meteo.lv, accessed on 25 February 2020 |
22 | ▲Lithuania | LAS07 | 1 | 2163 | www.aaa.am.lt, accessed on 2 February 2020 |
23–27 | ▲Poland | PL-EVRF2007-NH | 5 | 100 | www.imgw.pl, accessed on 11 April 2020 |
28–31 | ▲Germany | DHHN92 | 4 | 4419 | www.bsh.de, accessed on 23 October 2020 |
32–35 | ▲Denmark | DVR90 | 4 | 1861 | www.emodnet-physics.eu, accessed on 12 October 2020 |
36–59 | ▲Sweden | RH2000 | 24 | 37,567 | www.smhi.se, accessed on 31 March 2020 |
60–73 | ▲Finland | N2000 | 14 | 0 | www.ilmatieteenlaitos.fi, accessed on 28 March 2020 |
74 | ▲Russia | BHS77 (+15 cm) | 1 | 9125 | www.emodnet-physics.eu, accessed on 15 February 2020 |
Mission | Altimeter | Mode | Altitude [km] | Inclination [°] | Cycle [Days] | Used Retracker | Launch Date |
---|---|---|---|---|---|---|---|
Sentinel-3B | SRAL | SAR | 814.5 | 98.65 | 27 | ALES+SAR | Apr’18 |
Sentinel-3A | SRAL | SAR | 814.5 | 98.65 | 27 | ALES+SAR | Feb’16 |
Jason-3 | Poseidon-3B | LRM | 1336 | 66.04 | 9.91 | ALES+ | Feb’16 |
Mission | Passes | Observations | Outliers | Cycle No. | Data Period | ||
---|---|---|---|---|---|---|---|
S3A | 42 | 42,536 | 6595 | 1,459,334 | 101,422 | 13–45 | January 2017–May 2019 |
S3B | 41 | 41,277 | 1396 | 267,084 | 19,767 | 19–25 | November 2018–May 2019 |
JA3 | 33 | 20,483 | 9493 | 2,473,488 | 212,386 | 30–121 | December 2016–May 2019 |
Total | 116 | 104,296 | 17,484 | 4,199,906 | 333,575 | 131 cycles | - |
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Mostafavi, M.; Delpeche-Ellmann, N.; Ellmann, A.; Jahanmard, V. Determination of Accurate Dynamic Topography for the Baltic Sea Using Satellite Altimetry and a Marine Geoid Model. Remote Sens. 2023, 15, 2189. https://doi.org/10.3390/rs15082189
Mostafavi M, Delpeche-Ellmann N, Ellmann A, Jahanmard V. Determination of Accurate Dynamic Topography for the Baltic Sea Using Satellite Altimetry and a Marine Geoid Model. Remote Sensing. 2023; 15(8):2189. https://doi.org/10.3390/rs15082189
Chicago/Turabian StyleMostafavi, Majid, Nicole Delpeche-Ellmann, Artu Ellmann, and Vahidreza Jahanmard. 2023. "Determination of Accurate Dynamic Topography for the Baltic Sea Using Satellite Altimetry and a Marine Geoid Model" Remote Sensing 15, no. 8: 2189. https://doi.org/10.3390/rs15082189
APA StyleMostafavi, M., Delpeche-Ellmann, N., Ellmann, A., & Jahanmard, V. (2023). Determination of Accurate Dynamic Topography for the Baltic Sea Using Satellite Altimetry and a Marine Geoid Model. Remote Sensing, 15(8), 2189. https://doi.org/10.3390/rs15082189