# Impact of Altimeter Data Processing on Sea Level Studies

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study area and data used

## 3. Data Processing

#### 3.1. Computation of along-track sea level anomalies

- DataSet1 - all corrections according to Table 1.
- DataSet3 - all corrections according to Table 1, except for the wet tropospheric correction where a slightly modified version of the Ruf model [35] for modelling the TMR drift has been used instead. The modification consisted in the extension of a constant drift rate until cycle 241, followed by a constant drift correction after cycle 241, according to [28]. The parameter ΔL defined in [35] has been replaced by$$\{\begin{array}{ll}\mathrm{\Delta}\text{L}=0.0186722*\text{cycle}\_\text{number dB}/\text{cycle}\hfill & \text{if cycle}\_\text{number}<241\hfill \\ \mathrm{\Delta}\text{L}=4.5\phantom{\rule{0.2em}{0ex}}\text{dB}/\text{cycle}\hfill & \text{if cycle}\_\text{number}\ge 241\hfill \end{array}$$
- DataSet4 - all corrections according to Table 1, except for the inverse barometer which has been applied by adopting a model which makes use of a constant reference atmospheric pressure (CRP) of 1013.3 mbar, [24], according to (3).$$IB=-9.948*\mathit{(}{P}_{\mathit{\text{atm}}}-1013.3\mathit{)}$$
_{atm}is the surface atmospheric pressure in mbar, computed from the dry tropospheric correction (dry_corr) field provided in the GDR-Ms, using Equation (4), where φ is the geodetic latitude.$${P}_{\mathit{\text{atm}}}=\frac{\mathit{\text{dry}}\_\mathit{\text{corr}}}{-2.277\phantom{\rule{0.2em}{0ex}}(1+0.0026cos\left(2\mathrm{\phi}\right))}$$ - DataSet5 - all corrections according to Table 1, except for the inverse barometer which has been applied by adopting a model which makes use of a variable reference surface atmospheric pressure (VRP) interpolated from global average values obtained from the Centre National d'Études Spatiales (CNES/CLS), [36], according to equation (5).$$IB=-9.948*({\text{P}}_{\mathit{\text{atm}}}-\overline{P})$$
_{atm}is the surface atmospheric pressure in mbar, computed according to (4) and P̅ is the global average pressure interpolated to the time of the measurement from values determined at 6 hour intervals from CNES. According to [36], the values of P̅ have been smoothed using a (2-day)^{-1}cut-off frequency. - DataSet6 - all corrections according to Table 1, except for the inverse barometer which has been applied by adopting a combined model where the low frequencies are taken from the inverse barometer correction according to (5) and the high frequency effects are given by the MOG2D barotropic model [37]. This combined correction has been interpolated from global grids (0.25° × 0.25°) at 6 hour intervals provided by AVISO and it will be referred as “combined-MOG2D IB model”.

#### 3.2. Determination of interannual variation and sea level trend

## 4. Results and Discussion

#### 4.1. Sea state bias

#### 4.2. Radiometer wet tropospheric correction

#### 4.3 Inverse barometer correction

^{-1}cut-off frequency. However it was found that the smoothing does not affect the mean sea level determination since the maximum difference in the values of P̅ is 0.7 mbar, which causes a maximum SLA change of 7 mm.

#### 4.4 Satellite orbit field

^{2}relative to the NASA orbit [25], with a degradation of the CNES orbit particularly high from cycle 100 to cycle 246.

## 5. Conclusions

## Acknowledgments

## References

- Chelton, D. B.; Ries, J. C.; Haines, B. J.; Fu, L. L.; Cazenave, A. Satellite altimetry. In Satellite Altimetry and Earth Sciences; Academic Press; Fu, L., Cazenave, A., Eds.; 2001; pp. 1–131. [Google Scholar]
- Leuliette, E. W.; Nerem, R.S.; Mitchum, G.T. Calibration of TOPEX/Poseidon and Jason altimeter data to construct a continuous cecord of mean sea level change. Marine Geodesy
**2004**, 27, 79–94. [Google Scholar] - Born, G. H.; Tapley, B. D.; Ries, J. C.; Stewart, R. H. Accurate measurements of mean sea level changes by altimetric satellites. Journal of Geophysical Research
**1986**, 91(C10), 11775–11782. [Google Scholar] - Wagner, C. A.; Cheney, R. E. Global sea level change from satellite altimetry. Journal of Geophysical Research
**1992**, 97(C10), 15607–15615. [Google Scholar] - Anzenhofer, M.; Gruber, T. Fully reprocessed ERS-1 altimeter data from 1992 to 1995: Feasibility of the detection of long-term sea level change. Journal of Geophysical Research
**1998**, 103(C4), 8089–8112. [Google Scholar] - Cazenave, A.; Dominh, K.; Gennero, M.; Ferret, B. Global mean sea level changes observed by T/P and ERS-1. Physics and Chemistry of the Earth
**1998**, 23, 1069–1075. [Google Scholar] - Andersen, O.; Knudsen, P.; Beckley, B. Monitoring sea level and sea surface temperature trends from ERS satellites. Physics and Chemistry of the Earth
**2002**, 27, 1413–1417. [Google Scholar] - Nerem, R. S. Measuring global mean sea level variations using TOPEX/Poseidon altimeter data. Journal of Geophysical Research
**1995**, 100, 25135–25152. [Google Scholar] - Nerem, R. S. Measuring very low frequency sea level variations using satellite altimeter data. Global and Planetary Change
**1999**, 20, 157–171. [Google Scholar] - Nerem, R. S.; Haines, B. J.; Hendricks, J.; Minster, J. F.; Mitchum, G. T.; White, W. B. Improved determination of global sea level variations using TOPEX/Poseidon altimeter data. Geophysical Research Letters
**1999**, 24, 1331–1334. [Google Scholar] - Cabanes, C.; Cazenave, A.; Le Provost, C. Sea level rise during past 40 years determined from satellite and in situ observations. Science
**2001**, 294(5543), 840–842. [Google Scholar] - Cazenave, A.; Nerem, R. S. Present-day sea level change: observations and causes. Reviews of Geophysics
**2004**, 42, RG3001. [Google Scholar] - Fenoglio-Marc, L. Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges. Physics and Chemistry of the Earth
**2002**, 27, 1419–1431. [Google Scholar] - Cazenave, A.; Bonnefond, P.; Mercier, F.; Dominh, K.; Toumazou, V. Sea level variations in the Mediterranean Sea and Black Sea, from satellite altimetry and tide gauges. Global and Planetary Change
**2002**, 34, 59–86. [Google Scholar] - Larnicol, G.; Ayoub, N.; Le Traon, P.Y. Major changes in Mediterranean sea level variability from 7 years, of TOPEX/Poseidon and ERS-1/2 data. Journal of Marine System
**2002**, 33–34, 63–89. [Google Scholar] - Choi, B.-J; Haidvoge, D. B.; Cho, Y.-K. Nonseasonal sea level variations in the Japan/East Sea from satellite altimeter data. Journal of Geophysical Research
**2004**, 109, C12028. [Google Scholar] [CrossRef] - Fu, L.-L. The interannual variability of the North Atlantic Ocean revealed by combined data from TOPEX//Poseidon and Jason altimetric measurements. Geophysical Research Letters
**2004**, 31(23), L23303. [Google Scholar] [CrossRef] - Zanifé, O. Z.; Vincent, P.; Amarouche, L.; Dumont, J. P.; Thibault, P.; Labroue, S. Comparison of Ku-band range noise level and the relative sea-state bias of the Jaosn-1 TOPEX and Poseidon-1 Radar altimeters. Marine Geodesy
**2003**, 26, 201–238. [Google Scholar] - Robinson, I. S. Measuring the Oceans from Space, the principles and methodology of satellite oceanography; Springer Verlag, 2004. [Google Scholar]
- Ponte, R. M.; Gaspar, P. Regional analysis of the inverted barometer effect over the global ocean using TOPEX/Poseidon data and model results. Journal of Geophysical Research
**1999**, 104(C7), 15587–15602. [Google Scholar] - Peneva, E. L.; Stanev, E. V.; Stanychni, S. V.; Salokhiddinov, A.; Stulina, G. The recent evolution of Aral sea level and water properties: analysis of satellite, gauge and hydrometereological data. Journal of Marine Systems
**2004**, 47, 11–24. [Google Scholar] - Tziavos, I. N.; Vergos, G. S.; Kotzev, V.; Pashova, L. Mean sea level and sea level variation studies in the Black Sea and the Aegean. In International Association of Geodesy Symposia; Jekeli, et al., Eds.; Springer Verlag, 2005; Volume 129, pp. 254–259. [Google Scholar]
- Barbosa, S. Sea level change in the North Atlantic from tide gauges and satellite altimetry. PhD Thesis, submitted. University of Porto, 2006. [Google Scholar]
- AVISO. AVISO User handbook for merged TOPEX/Poseidon products. AVI-NT-02-101-CN
**1996**. [Google Scholar] - Ablain, M.; Mertz, M.; Dorandeau, J.; Destouesse, M.; Vincent, P.; Picot, N. TOPEX/Poseidon validation activities 12 years of T/P data (GDR-Ms); Contract No 03/CNES/1340/00-DSO310-lot2.C, CNES/CLS; 2005. [Google Scholar]
- Scharroo, R.; Lillibridge, J. L.; Smith, W. H. F.; Schrama, E. J. O. Cross-calibration and long-term monitoring of the microwave radiometers of ERS, TOPEX, GFO, Jason and Envisat. Marine Geodesy
**2004**, 27, 279–297. [Google Scholar] - Chambers, D. P.; Hayes, S. A.; Ries, J. C.; Urban, T. J. New TOPEX sea state bias models and their effect on global mean sea level. Journal of Geophysical Research
**2003**, 108(C10), 3305–3311. [Google Scholar] - Berwin, R. W. TOPEX/Poseidon sea surface height anomaly product; User's Reference Manual; NASA JPL Physical Oceanography DAAC: Pasadena, CA, 2003. [Google Scholar]
- Kulhânek, O. Introduction to Digital Filtering in Geophysics.; Amsterdam; Elsevier, 1976. [Google Scholar]
- Imel, D. A. Evaluation of the TOPEX/Poseidon dual-frequency ionospheric correction. Journal of Geophysical Research
**1994**, 99(C12), 24895–24906. [Google Scholar] - Matsumoto, K.; Takanezawa, T.; Ooe, M. Ocean tide models developed by assimilating TOPEX/Poseidon altimeter data into hydrodynamical model: a global model and a regional model around Japan. Journal of Oceanography
**2000**, 56, 567–581. [Google Scholar] - Fernandes, M. J.; Antunes, M. Eight years of satellite radar altimetry in the Northeast Atlantic. Proceedings of the 3ª Assembleia Luso-Espanhola de Geodesia e Geofísica; Garcia, F., Valero, J., Eds.; Editorial UPV, 2003; Vol. I, pp. 226–230. [Google Scholar]
- Wang, Y. M. GSFC00 Mean sea surface, gravity anomaly, and vertical gravity gradient from satellite altimeter data. Journal of Geophysical Research
**2001**, 106(C12), 31167–31174. [Google Scholar] - Labroue, S.; Gaspar, P.; Dorandeu, J.; Ogor, F.; Zanife, O. Z. Overview of the improvements made on the empirical determination of the sea state bias correction. 15 Years of Progress in Radar Altimetry Symposium, Venice, 13–18 March, 2006.
- Ruf, C. S. Characterization and correction of a drift in calibration of the TOPEX Microwave Radiometer. IEEE Transactions on Geoscience and Remote Sensing
**2002**, 40(2), 509–511. [Google Scholar] - Dorandeu, J.; Le Traon, P. Y. Ocean effects of global mean atmospheric pressure variations on mean sea level changes from TOPEX/Poseidon. Journal of Atmospheric and Oceanic Technology
**1999**, 16, 1279–1283. [Google Scholar] - Carrère, L.; Lyard, F. Modelling the barotropic response of the global ocean to atmospheric wind and pressure forcing - comparisons with observations. Geophysical Research Letters
**2003**, 30(6), 1275–10.1029/2002GL016473. [Google Scholar] - Cleveland, R. B.; Cleveland, W. S.; McRae, J. E.; Terpenning, I. STL: A Seasonal-trend decomposition procedure based on loess. Journal of Official Statistics
**1990**, 6, 3–73. [Google Scholar] - Barbosa, S. M.; Fernandes, M. J.; Silva, M. E. Nonlinear sea level trends from European tide gauge records. Annales Geophysicae
**2004**, 22, 1465–1472. [Google Scholar] - R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria, 2005. ISBN 3-900051-07-0. http://www.r-project.org.
- Barbosa, S. M.; Fernandes, M. J.; Silva, M. E. Interannual sea level change in the North Atlantic from TOPEX/Poseidon satellite altimetry. Physics and Chemistry of the Earth
**2005**. submitted. [Google Scholar] - Gaspar, P.; Ogor, F.; Le Traon, P. Y.; Zanife, O. Z. Estimating the sea state bias of TOPEX and POSEIDON altimeters from crossover differences. Journal of Geophysical Research
**1994**, 99(C12), 24981–24994. [Google Scholar] - Gaspar, P.; Ogor, F.; Escoubes, C. Nouvelles calibration et analyse du bias d'état de mer des altimètres TOPEX et Poseidon. Technical note 96/018 of CNES Contract 95/1523
**1996**. [Google Scholar] - Gaspar, P.; Labroue, S.; Ogor, F.; Lafitte, G.; Marchal, L.; Rafanel, F. improving nonparametric estimates of the sea state bias in radar altimeter mesaurements of sea level. Journal of Atmospheric and Oceanic Technology
**2002**, 19, 1690–1706. [Google Scholar] - Labroue, S.; Gaspar, P.; Dorandeau, J.; Ablain, M.; Vincent, P. Comparison of non parametric estimates of the TOPEX A, TOPEX B and JASON-1 sea state bias. Poster presented at the Jason-1 and TOPEX/Poseidon SWT meeting, New Orleans, 21–23 October 2002.
- Labroue, S.; Gaspar, P.; Dorandeau, J.; Zanifé, O.; Mertz, F.; Vincent, P.; Choquet, D. Nonparametric estimates of the sea state bias for the Jason-1 radar altimeter. Marine Geodesy
**2004**, 27, 453–481. [Google Scholar] - Scharroo, R.; Lillibridge, J. L.; Smith, W. H. F.; Vandermark, D.; Beckley, B. A hybrid method of direct estimation sea state bias models. poster presented at the Jason-1 Science Working Team meeting, New Orleans; 2002. [Google Scholar]
- Scharroo, R.; Lillibridge, J. L. Hybrid sea state models and their impact on sea level change studies. poster presented at the Ocean Surface Topography Science Team meeting; 2004. [Google Scholar]
- Vandemark, D.; Tran, N.; Beckley, B.; Chapron, B.; Gaspar, P. Direct estimation of sea state impacts on radar altimeter sea level measurements. Geophysical Research Letters
**2002**, 29(24), 2148. [Google Scholar] [CrossRef] - Dong, X.; Woodworth, P.; Moore, P.; Bingley, R. Absolute calibration of the TOPEX/Poseidon altimeters using UK tide gauges, GPS, and precise, local geoid-differences. Marine Geodesy
**2002**, 25, 189–204. [Google Scholar] - Keihm, S. J.; Zlotniki, V.; Ruf, C. S. TOPEX Microwave Radiometer performance evaluation, 1992-1998. IEEE Transactions on Geoscience and Remote Sensing
**2000**, 38(3), 1379–1386. [Google Scholar] - Callahan, P. S. User notes for revised GDR Correction Products (GCPB), Tech. Note to the TOPEX/Poseidon/Jason Science Working Team.; Pasadena, California; Jet Propulsion Laboratory, California Institute of Technology, 2003. [Google Scholar]
- Ruf, C.; Keihm, S.; Subramanyam, B.; Janssen, M. TOPEX/Poseidon microwave radiometer performance in-flight calibration. Journal of Geophysical Research
**1994**, 99, 24915–24926. [Google Scholar] - Fu, L.-L.; Pihos, G. Determining the response of sea level to atmospheric pressure forcing using TOPEX/POSEIDON data. Journal of Geophysical Research
**1994**, 99(C12), 24633–24642. [Google Scholar] - Esselborn, S.; Eden, C. Sea surface height changes in the North Atlantic Ocean related to the North Atlantic Oscillation. Geophysical Research Letters
**2001**, 28(18), 3473–3476. [Google Scholar] - Woolf, D. K.; Shaw, A. G. P.; Tsimplis, M. N. The influence of North Atlantic Oscillation on sea level variability in the North Atlantic region. Global Atmosphere & Ocean System
**2003**, 9(4), 145–167. [Google Scholar] - Wakelin, S. L.; Wodworth, P. L.; Flather, R. A.; Williams, J. A. Sea level dependence on the NAO over the NW European continental shelf. Geophysical Research Letters
**2003**, 30(7), 1403. [Google Scholar] [CrossRef] - Yan, Z.; Tsimplis, M. N.; Woolf, D. Analysis of the relationship between the North Atlantic Oscillation and sea level changes in NorthWest Europe. International Journal of Climatology
**2004**, 24, 743–758. [Google Scholar] - Ogi, M.; Yamazaki, K.; Tachibana, Y. The summer northern annular mode and abnormal summer weather in 2003. Geophysical Research Letters
**2005**, 32, L04706, 10.1029/2004GL021528. [Google Scholar] - Morel, L.; Willis, P. Systematic effects of terrestrial reference frames on mean sea level determinations; Jason Science Working Team: Saint-Raphael, 25-27 October 1999. http://www.jason.oceanobs.com/documents/swt/posters1999/morel.pdf.
- Morel, L.; Willis, P. Terrestrial reference frame effects on global sea level rise determination from TOPEX/Poseidon altimetric data. Advances in Space Research
**2005**, 3, 358–368. [Google Scholar] - Nerem, R. S.; Mitchum, G. T. Sea level change. In Satellite Altimetry and Earth Sciences; Academic Press; Fu, L., Cazenave, A., Eds.; 2001; pp. 329–347. [Google Scholar]

**Figure 3.**

**a**Various curves for DataSet1 in the Tropical region. : SLA; : interannual variations; : linear trend.

**b**Various curves for DataSet1 in the Sub-Tropical region. : SLA; : interannual variations; : linear trend.

**c**Various curves for DataSet1 in the Sub-Arctic region. : SLA; : interannual variations; : linear trend.

**Figure 4.**SLA differences for the Tropical region. - DataSet1- DataSet2; - DataSet1- DataSet3; - DataSet1- DataSet5; - DataSet1- DataSet7; –DataSet5- DataSet4.

**Figure 5.**SLA differences DataSet1-DataSet2 (different SSB models: Chambers – Labroue). - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 6.**SLA differences DataSet1-DataSet3 (different wet_cor models: Scharroo - Ruf). - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 7.**

**a**SLA differences DataSet1-DataSet4 (non-IB-corrected versus IB Constant Reference Pressure) - Tropical; : Sub-Tropical; Sub-Arctic.

**b**SLA differences DataSet1-DataSet5 (non-IB-corrected versus IB Variable Reference Pressure). - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 8.**

**a**SLA differences DataSet5-DataSet4 (different IB models: Variable Reference Pressure - Constant Reference Pressure) - Tropical; : Sub-Tropical; Sub-Arctic.

**b**SLA differences DataSet5-DataSet6 (different IB models: Variable Reference Pressure - combined_MOG2D) - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 9.**SLA differences DataSet1-DataSet7 (different orbits) - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 10.**

**a**Interannual variation of sea level for the tropical region. – DataSet1; – DataSet2 (Labroue SSB); – DataSet3 (Ruf wet_cor); –DataSet4 (IB-CRP); – DataSet5 (IB-VRP);

**dashed black**– DataSet6 (IB-MOG2D); – DataSet7 (CNES orbit).

**b**Interannual variation of sea level for the Sub-Tropical region. – DataSet1; – DataSet2 (Labroue SSB); – DataSet3 (Ruf wet_cor); –DataSet4 (IB-CRP); – DataSet5 (IB-VRP);

**dashed black**– DataSet6 (IB-MOG2D); – DataSet7 (CNES orbit).

**c**Interannual variation of sea level for the Sub-Arctic region. – DataSet1; – DataSet2 (Labroue SSB); – DataSet3 (Ruf wet_cor); –DataSet4 (IB-CRP); – DataSet5 (IB-VRP);

**dashed black**– DataSet6 (IB-MOG2D); – DataSet7 (CNES orbit).

**Figure 11.**

**a –**Chambers SSB model for TOPEX A (metres).

**b –**Labroue SSB model for TOPEX A (metres).

**c –**Difference between Labroue and Chambers SSB models for TOPEX A (metres).

**Figure 12.**

**a –**Chambers SSB model for TOPEX B (metres).

**a –**Labroue SSB model for TOPEX B (metres).

**c –**Difference between Labroue and Chambers SSB models for TOPEX B (metres).

**Figure 13.**

**a –**Average SWH. - Tropical; : Sub-Tropical; Sub-Arctic.

**b –**SWH standard deviation (σ). - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 14.**

**a –**Average WS. - Tropical; : Sub-Tropical; Sub-Arctic.

**b –**WS standard deviation (σ). - Tropical; : Sub-Tropical; Sub-Arctic.

**Figure 15.**

**a**Significant wave height for cycle 273 (February 2000).

**b**Wind speed for cycle 273 (February 2000).

**Figure 16.**

**a**Significant wave height for cycle 364 (August 2002).

**b**Wind speed for cycle 364 (August 2002).

**Table 1.**Geophysical corrections and models used in the processing of TOPEX altimeter data (DataSet1).

Corrections/models | T/P |
---|---|

Mean Sea Surface | GSFC00.1, [33] |

Orbit | NASA JGM3, [24] |

Dry troposphere | ECMWF, [24] |

Wet troposphere | Scharroo TMR model (yaw state and drift effect on TMR TBs applied, [26]) |

Ionosphere | TOPEX: dual-frequency (filtered, [32]) |

Sea state bias | Chambers model, [27]; additional +2 mm for TOPEX B |

Earth tide | Applied, [24] |

Ocean tide | NAO99, [31] |

Pole tide | Applied, [24] |

Inverse barometer | Not Applied |

**Table 2.**Geophysical corrections and models used in the processing of the various TOPEX altimeter data sets. The box corresponding to the correction that has been changed with respect to DataSet1 is shaded.

Data Sets/Corrections | Radiometer Wet troposphere (wet_cor) | Sea state bias (SSB) | Inverse barometer (IB) | Orbit |
---|---|---|---|---|

DataSet1 | Scharroo TMR model, [26] | Chambers model, [27] | Not Applied | NASA |

DataSet2 | Scharroo TMR model, [26] | Labroue model, [34] | Not Applied | NASA |

DataSet3 | Ruf TMR model,[35] | Chambers model, [27] | Not Applied | NASA |

DataSet4 | Scharroo TMR model, [26] | Chambers model, [27] | Constant reference atmospheric pressure of 1013.3, [24] | NASA |

DataSet5 | Scharroo TMR model, [25 | Chambers model, [27] | Non-constant reference surface atmospheric pressure. [36] | NASA |

DataSet6 | Scharroo TMR model, [26] | Chambers model, [27] | Combined-MOG2D model [37] | NASA |

DataSet7 | Scharroo TMR model, [26] | Chambers model, [27] | Not Applied | CNES |

**Table 3.**Linear trends (mm/yr) and corresponding standard errors (mm/yr) obtained by OLS fit to the time series of the interannual SLA signal.

Tropical | Sub-Tropical | Sub-Arctic | ||||
---|---|---|---|---|---|---|

trend | s. e. | trend | s. e. | trend | s. e. | |

DataSet1 | 3.24 | 0.08 | 1.97 | 0.11 | 3.26 | 0.18 |

DataSet2 | 3.07 | 0.08 | 1.86 | 0.11 | 3.22 | 0.18 |

DataSet3 | 3.35 | 0.08 | 2.10 | 0.11 | 3.38 | 0.18 |

DataSet4 | 3.14 | 0.08 | 1.82 | 0.09 | 4.14 | 0.13 |

DataSet5 | 2.99 | 0.08 | 1.68 | 0.08 | 3.99 | 0.12 |

DataSet6 | 3.00 | 0.08 | 1.69 | 0.08 | 4.01 | 0.13 |

DataSet7 | 3.25 | 0.09 | 1.99 | 0.09 | 3.19 | 0.17 |

**Table 4.**Linear trends (mm/yr) and corresponding standard errors (mm/yr) obtained by OLS fit to the original SLA time series (after removing the seasonal signal).

Tropical | Sub-Tropical | Sub-Arctic | ||||
---|---|---|---|---|---|---|

trend | s. e. | trend | s. e. | trend | s. e. | |

DataSet1 | 3.22 | 0.20 | 2.04 | 0.40 | 3.40 | 0.74 |

DataSet2 | 3.05 | 0.20 | 1.93 | 0.40 | 3.37 | 0.72 |

DataSet3 | 3.33 | 0.21 | 2.16 | 0.41 | 3.52 | 0.73 |

DataSet4 | 3.14 | 0.17 | 1.83 | 0.18 | 4.14 | 0.23 |

DataSet5 | 3.00 | 0.16 | 1.68 | 0.17 | 4.00 | 0.23 |

DataSet6 | 3.00 | 0.16 | 1.69 | 0.17 | 4.02 | 0.22 |

DataSet7 | 3.23 | 0.20 | 2.05 | 0.40 | 3.33 | 0.74 |

**Table 5.**Linear trends differences (mm/yr) between various datasets (obtained by OLS fit to the time series of the interannual SLA signal).

Tropical | Sub-Tropical | Sub-Arctic | |
---|---|---|---|

DataSet1 - DataSet2 | 0.17 | 0.11 | 0.04 |

DataSet1 - DataSet3 | -0.11 | -0.13 | -0.12 |

DataSet1 - DataSet4 | 0.10 | 0.15 | -0.88 |

DataSet1 - DataSet5 | 0.25 | 0.29 | -0.73 |

DataSet1 - DataSet6 | 0.24 | 0.28 | -0.75 |

DataSet1 - DataSet7 | -0.01 | -0.02 | 0.07 |

DataSet5 - DataSet4 | -0.15 | -0.14 | -0.15 |

DataSet5 - DataSet6 | -0.01 | -0.01 | -0.02 |

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Fernandes, M.J.; Barbosa, S.; Lázaro, C.
Impact of Altimeter Data Processing on Sea Level Studies. *Sensors* **2006**, *6*, 131-163.
https://doi.org/10.3390/s6030131

**AMA Style**

Fernandes MJ, Barbosa S, Lázaro C.
Impact of Altimeter Data Processing on Sea Level Studies. *Sensors*. 2006; 6(3):131-163.
https://doi.org/10.3390/s6030131

**Chicago/Turabian Style**

Fernandes, M. Joana, Susana Barbosa, and Clara Lázaro.
2006. "Impact of Altimeter Data Processing on Sea Level Studies" *Sensors* 6, no. 3: 131-163.
https://doi.org/10.3390/s6030131