# Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{−1}[3,4]. Tide gauge measurements have some limitations due to their density of distribution, local impacts and are particularly affected by vertical land movements such as land subsidence [5,6].

^{2}of sea area. More than 60% of the Indonesian population lives in coastal zones [21]. Due to the unique characteristics of Indonesia, understanding sea level changes in this area is crucial. Considering the potential impacts of sea-level rise in this region, the continuous monitoring of sea level variability becomes urgent.

_{obs}is the range between the sea surface and the spacecraft antenna, which is determined from the travel time of the radar pulse using a ultra-stable oscillator (USO) and the speed of the radar pulse, ΔRGeo

_{Corrs}includes all range and geophysical corrections and MSS is the sea surface height above the same ellipsoid given by a known mean sea surface model. ${R}_{Obs}$ is the observed range already corrected for all required instrument corrections. $\Delta RGe{o}_{Corrs}$ refers to the set of range and geophysical corrections: dry and wet tropospheric corrections, ionospheric correction, sea state bias, dynamic atmospheric correction, tides (solid earth, ocean, load and pole) and reference frame offset, as shown in Equation (2) [23].

_{dry}, ΔR

_{wet}, ΔR

_{iono}and ΔR

_{SSB}are range corrections due to the interaction between the radar signal with the atmosphere and with the sea surface, respectively. ΔR

_{DAC}and ΔR

_{tides}are corrections related with geophysical phenomena that must be accounted for in order to separate them from the signal of interest. R

_{RFO}is the reference frame offset, only required when multi mission data are used.

## 2. Materials and Methods

**Dry troposphere**: The dry tropospheric correction (DTC) can be determined from several sources of surface pressure data, such as in situ surface pressure measurements, a Numerical Weather Model (NWM) and a climatology such as the Global Pressure and Temperature Model (GPT) [30]. In this paper, NWM is used to model the DTC due to the limited number and distribution of in situ surface pressure measurements. The most common sources of NWM are the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP). For all missions (TOPEX/Poseidon (TP), Jason-1 (J1) and Jason-2 (J2)) the corrections are those derived from the following atmospheric models: (i) ECMWF Re-Analysis (ERA) Interim [31]; (ii) NCEP [32].**Wet troposphere**: the on-board near-nadir-looking microwave radiometer is the best source of information to compute the wet tropospheric correction for altimeter data. The main problem of the MWR-derived WTC in coastal areas is associated with the invalid MWR data measurements due to the large footprint of the instrument that is contaminated by land. Meteorological models are alternative sources to determine the WTC. From these, the ERA-Interim model provides better quality than ECMWF operational model, particularly before 2004 [28,33]. Another WTC source in the coastal zone is the GNSS-derived Path Delay (GPD) corrections provided by the University of Porto. The GPD is an algorithm primarily implemented to estimate an improved WTC in the coastal regions, which combines zenith wet path delay (ZWD) derived from GNSS coastal and island stations, valid onboard microwave radiometer measurements and total column water vapour observations from scanning imaging MWR (SI-MWR) on board remote sensing satellites [27,28,34]. In this paper, the following corrections were used: (i) from the onboard microwave radiometer (MWR); (ii) from the ERA-Interim model; (iii) from the GNSS-derived Path Delay Plus (GPD+) algorithm, the latest GPD version [34].**Ionosphere:**the altimeter satellites TOPEX/Poseidon, Jason-1 and Jason-2 operate in two different frequencies (Ku-band: ~13.6 GHz and C-band: ~5.3 GHz) to determine the ionospheric correction. Three ionospheric models are available in RADS: the JPL (Jet Propulsion Laboratory) GIM (Global Ionosphere Map), NIC09 (NOAA Ionosphere Climatology 2009) and IRI (International Reference Ionosphere). The JPL GIM provides the vertical total electron content (TEC) based on the dual-frequency GNSS data. The GIM is available in two-hourly global grids with a spatial resolution of 2.5° in latitude and 5° in longitude [35]. The NIC09 is a climatology based on the JPL GIM for the period 1998–2008 and variation of solar activity; see details in [36]. The IRI model is a climatology mainly based on ionosonde data measurement since the 1930s. The IRI model has a good accuracy for the northern-mid latitudes, but is less accurate for low and high latitudes, due to poor spatial distribution of ionosode stations [37,38]. According to [36,38], JPL GIM and NIC09 are better than IRI model. Therefore, the ionospheric corrections used in this study for all missions are the smoothed dual frequency and JPL-GIM ionospheric corrections. However, for TOPEX/Poseidon, since GIM is only available since cycle 220, NIC2009 was used for all cycles prior to 220 (1–219).**Sea State Bias (SSB)**: In this study, two major types of SSB models usually used in altimetric studies were compared: parametric models, based on three or four parameters and non-parametric models. The SSB correction is commonly estimated as a function of two input parameters: significant wave height and wind speed. In the SBB Tran model [39,40], ocean wave period data are used within a three-input estimator. For TOPEX, the parameter model BM-4 based on [41] and the non-parametric CLS model [42] were applied. The non-parametric CLS and Tran2012 SSB models were applied for Jason-1 and Jason-2.**Ocean and Load tides**: Two main models for ocean and load tides are available in RADS: Finite Element Solution (FES) and Global Ocean Tide (GOT) [43] models. The last version of the GOT ocean and load tide models is GOT 4.10, while one of the latest available FES ocean tide model is FES2012 [44]. The GOT 4.10 load tide should be used to complement FES2012 model since tide loading effects have not yet been computed for FES2012. Therefore, in order to assess the ocean and load tide models, the GOT 4.10 (GOT 4.10 ocean and load tides) and FES2012 (FES2012 ocean tide and GOT4.10 load tide) models were used.

## 3. Results

#### 3.1. Dry Tropospheric Correction

^{2}. Thus, for consistency, the DTC from ERA Interim should be adopted.

#### 3.2. Wet Tropospheric Correction

#### 3.3. Ionospheric Correction

^{16}electrons/m

^{2}. As a dispersive medium, the ionosphere refraction is frequency-dependent. The ionosphere delay can be determined as function of the frequency [22]:

^{2}/TECU, TEC is the total electron content, f is the frequency in GHz and $\Delta {R}_{iono}$ is given in meters.

#### 3.4. Sea State Bias

_{10}). Parametric models are usually given by three or four parameters, the function of SWH and U

_{10}. The coefficients are derived by, e.g., the least square fit of the crossover height differences [41]. The BM4 parametric model is given by the following equation (see also Table 2):

_{1}, …, a

_{4}are coefficients of BM4.

_{10}and the mean gravity wave period (T

_{m}) from a numerical ocean wave model, NOAA’s WAVEWATCH III (NWW3). The Tran model gives a good result by reducing SSH variance at global and regional scales.

_{m}) from a numerical ocean wave model (NWW3), the Tran model (3 Parameters) gives an improvement by reducing sea surface height variance, particularly for Jason-2.

#### 3.5. Tides Correction

#### 3.6. Mean Sea Surface

^{2}) (Figure 20) but mainly positive (indicated by yellow to red colours). Due to the nature of the MSS errors, which are time invariant, the along-track SLA variance differences are larger than the corresponding differences at crossovers but also reinforce that CNESCLS2011 is generally better than DTU13 in the coastal regions. The large differences on the coastal areas are mainly due to differences in the methods adopted by each model to extrapolate the MSS values from the sea to land regions.

#### 3.7. Sea Level Variability around the Indonesian Seas

## 4. Discussion

^{2}near the coast and less than 0.4 cm

^{2}in the open ocean, indicating that the DTC impact on SLA estimation is negligible. These results are in agreement with [22] who illustrate that the DTC is unaffected by the presence of land and is not degraded close to the coast. For consistency, DTC from ERA Interim was adopted for all satellites.

^{2}). For Jason-1 and Jason-2, the GPD+ WTC significantly reduces the SLA variance with respect to the ERA model (about 6 cm

^{2}). Regarding the comparison with the onboard MWR-derived WTC, for Jason-1, the GPD+ correction shows a significant improvement (average differences 4 cm

^{2}). For Jason-2, the GPD+ correction shows a smaller, still significant improvement (less than 3 cm

^{2}), in spite of the fact that this correction is already improved in coastal regions [26].

^{2}. Although the ionosphere is insensitive to the coastline, for Jason-1 and Jason-2 the smoothed dual frequency reduces the SLA variance with respect to the JPL-GIM, except for the distances less than 25 km close to the coast. This degradation of the dual-frequency correction in the coastal regions may be due to sea state bias and short-wavelength in the wind field [22]. This may also be due to remaining land contamination still present in the smoothed dual frequency correction. The figures with the along-track SLA variance differences (Figure 8) and at crossovers (Figure 10) show that for TOPEX (cycles 1 to 219), Jason-1 and Jason-2, the smoothed dual frequency does not improve the results homogeneously for the whole Indonesian region, particularly in the Banda Sea, Ceram Sea and Western Pacific and in the Indian Ocean which coincide with unstable regions of the ionosphere [22].

^{2}; in contrast, TOPEX_B non-parametric CLS sea state bias significantly reduces the SLA variance with respect to BM4 parametric model by about 1 cm

^{2}with a larger reduction near the coast. For Jason-1 and Jason-2, Tran2012 model shows a significant reduction of the SLA variance when compared to non-parametric CLS SSB, indicated by negative values. The average of SLA variance differences between Tran2012 and CLS SSB (Tran2012 minus CLS SSB) in coastal areas, for Jason-1 is less than 1 cm

^{2}and for Jason-2 is about 2.5 cm

^{2}. Figure 11 and Figure 13 also indicate that the differences between the non-parametric CLS and parametric BM4 SSB have a coastal signature. For TOPEX/Poseidon, SSB CLS slightly increases the SLA variance near the coast (indicated by colours in the yellow to red range). For Jason-1, the SLA variance differences at crossover show that Tran2012 model is a small improvement with respect to CLS SSB. A significant reduction in SLA variance at crossovers is shown for Jason-2 when SSB Tran2012 model replaces the SSB CLS model (indicated by light to dark blue colours). In addition, for Poseidon (PN), BM4 SSB reduces the SLA variance with respect to BM3 SSB by about 3 cm

^{2}(the result is not shown).

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Nicholls, R.J. Impact of and responses to sea-level rise. In Understanding Sea-Level Rise and Variability; Church, J.A., Woodworth, P.L., Aarup, T., Wilson, W.S., Eds.; John Wiley & Sons: Chichester, UK, 2010; pp. 17–44. [Google Scholar]
- McGranahan, G.; Balk, D.; Anderson, B. The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environ. Urban.
**2007**, 19, 17–37. [Google Scholar] [CrossRef] - Church, J.A.; White, N.J. Sea-level rise from the late 19th to the early 21st century. Surv. Geophys.
**2011**, 32, 585–602. [Google Scholar] [CrossRef] - Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
- Wöppelmann, G.; Marcos, M. Vertical land motion as a key to understanding sea level change and variability. Rev. Geophys.
**2016**, 54, 64–92. [Google Scholar] [CrossRef] - Yildiz, H.; Andersen, O.B.; Simav, M.; Aktug, B.; Ozdemir, S. Estimates of vertical land motion along the southwestern coasts of Turkey from coastal altimetry and tide gauge data. Adv. Space Res.
**2013**, 51, 1572–1580. [Google Scholar] [CrossRef] - Chelton, D.B.; Ries, J.C.; Haines, B.J.; Fu, L.L.; Callahan, P.S. Satellite altimetry. In Satellite Altimetry and Earth Sciences: A Handbook of Techniques and Applications; Fu, L.L., Cazenave, A., Eds.; Academic Press: San Diego, CA, USA, 2001; pp. 1–132. [Google Scholar]
- Wingham, D.J.; Francis, C.R.; Baker, S.; Bouzinac, C.; Brockley, D.; Cullen, R.; de Chateau-Thierry, P.; Laxon, S.W.; Mallow, U.; Mavrocordatos, C.; et al. Cryosat: A mission to determine the fluctuations in earth’s land and marine ice fields. Adv. Space Res.
**2006**, 37, 841–871. [Google Scholar] [CrossRef] - Ricker, R.; Hendricks, S.; Helm, V.; Gerdes, R. Classification of CryoSat-2 radar echoes. In Towards an Interdisciplinary Approach in Earth System Science; Lohmann, G., Meggers, H., Unnithan, V., Wolf-Gladrow, D., Notholt, J., Bracher, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 149–158. [Google Scholar]
- Ray, C.; Martin-Puig, C.; Clarizia, M.P.; Ruffini, G.; Dinardo, S.; Gommenginger, C.; Benveniste, J. SAR altimeter backscattered waveform model. IEEE Trans. Geosci. Remote Sens.
**2015**, 53, 911–919. [Google Scholar] [CrossRef] - Rey, L.; Chateau-Thierry, P.D.; Phalippou, L.; Mavrocordatos, C.; Francis, R. SIRAL, a high spatial resolution radar altimeter for the Cryosat mission. In Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, Ausralia, 9–13 July 2001; Volume 3087, pp. 3080–3082.
- European Space Agency (ESA); University College of London (UCL). Cryosat Product Handbook; ESRIN-ESA and Mullard Space Science Laboratory, University College London: London, UK, 2012. [Google Scholar]
- Cazenave, A.; Dieng, H.-B.; Meyssignac, B.; von Schuckmann, K.; Decharme, B.; Berthier, E. The rate of sea-level rise. Nat. Clim. Chang.
**2014**, 4, 358–361. [Google Scholar] [CrossRef] - Church, J.A.; Clark, P.U.; Cazenave, A.; Gregory, J.M.; Jevrejeva, S.; Levermann, A.; Merrifield, M.A.; Milne, G.A.; Nerem, R.S.; Nunn, P.D.; et al. Sea level change. In Climate Change 2013: The Physical Science Basis; Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013; pp. 1137–1216. [Google Scholar]
- Nicholls, R.J.; Cazenave, A. Sea-level rise and its impact on coastal zones. Science
**2010**, 328, 1517–1520. [Google Scholar] [CrossRef] [PubMed] - Church, J.A.; White, N.J.; Hunter, J.R. Sea-level rise at tropical Pacific and Indian Ocean Islands. Glob. Planet. Chang.
**2006**, 53, 155–168. [Google Scholar] [CrossRef] - Cazenave, A.; Henry, O.; Munier, S.; Delcroix, T.; Gordon, A.L.; Meyssignac, B.; Llovel, W.; Palanisamy, H.; Becker, M. Estimating ENSO influence on the global mean sea level, 1993–2010. Mar. Geod.
**2012**, 35, 82–97. [Google Scholar] [CrossRef] - Fenoglio-Marc, L.; Schöne, T.; Illigner, J.; Becker, M.; Manurung, P.; Khafid. Sea level change and vertical motion from satellite altimetry, tide gauges and GPS in the Indonesian region. Mar. Geod.
**2012**, 35, 137–150. [Google Scholar] [CrossRef] - Strassburg, M.W.; Hamlington, B.D.; Leben, R.R.; Manurung, P.; Lumban Gaol, J.; Nababan, B.; Vignudelli, S.; Kim, K.Y. Sea level trends in southeast Asian seas. Clim. Past
**2015**, 11, 743–750. [Google Scholar] [CrossRef] - Passaro, M.; Dinardo, S.; Quartly, G.D.; Snaith, H.M.; Benveniste, J.; Cipollini, P.; Lucas, B. Cross-calibrating ales envisat and cryosat-2 delay–doppler: A coastal altimetry study in the Indonesian seas. Adv. Space Res.
**2016**, 58, 289–303. [Google Scholar] [CrossRef] - Dahuri, R.; Rais, J.; Ginting, S.P.; Sitepu, M.J. Pengelolaan Sumber Daya Pesisir Dan Lautan Secara Terpadu, 1st ed.; Balai Pustaka: Jakarta, Indonesia, 2008. [Google Scholar]
- Andersen, O.B.; Scharroo, R. Range and geophysical corrections in coastal regions: And implications for mean sea surface determination. In Coastal Altimetry; Vignudelli, S., Kostianoy, A.G., Cipollini, P., Benveniste, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; pp. 103–145. [Google Scholar]
- Fernandes, M.; Lázaro, C.; Nunes, A.; Scharroo, R. Atmospheric corrections for altimetry studies over inland water. Remote Sens.
**2014**, 6, 4952–4997. [Google Scholar] [CrossRef] - Gommenginger, C.; Thibaut, P.; Fenoglio-Marc, L.; Quartly, G.; Deng, X.; Gómez-Enri, J.; Challenor, P.; Gao, Y. Retracking altimeter waveforms near the coasts. In Coastal Altimetry; Vignudelli, S., Kostianoy, A.G., Cipollini, P., Benveniste, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; pp. 61–101. [Google Scholar]
- Passaro, M.; Cipollini, P.; Vignudelli, S.; Quartly, G.D.; Snaith, H.M. ALES: A multi-mission adaptive subwaveform retracker for coastal and open ocean altimetry. Remote Sens. Environ.
**2014**, 145, 173–189. [Google Scholar] [CrossRef][Green Version] - Brown, S. A novel near-land radiometer wet path-delay retrieval algorithm: Application to the Jason-2/OSTM advanced microwave radiometer. IEEE Trans. Geosci. Remote Sens.
**2010**, 48, 1986–1992. [Google Scholar] [CrossRef] - Fernandes, M.J.; Lázaro, C.; Nunes, A.L.; Pires, N.; Bastos, L.; Mendes, V.B. GNSS-derived path delay: An approach to compute the wet tropospheric correction for coastal altimetry. IEEE Geosci. Remote Sens. Lett.
**2010**, 7, 596–600. [Google Scholar] [CrossRef] - Fernandes, M.J.; Lázaro, C.; Ablain, M.; Pires, N. Improved wet path delays for all ESA and reference altimetric missions. Remote Sens. Environ.
**2015**, 169, 50–74. [Google Scholar] [CrossRef] - Scharroo, R. RADS Version 3.1: User Manual and Format Specification; Delft University of Technology: Delft, The Netherlands, 2012. [Google Scholar]
- Boehm, J.; Werl, B.; Schuh, H. Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-range Weather Forecasts operational analysis data. J. Geophys. Res. Solid Earth
**2006**, 111. [Google Scholar] [CrossRef] - Dee, D.P.; Uppala, S.M.; Simmons, A.J.; Berrisford, P.; Poli, P.; Kobayashi, S.; andrae, U.; Balmaseda, M.A.; Balsamo, G.; Bauer, P.; et al. The ERA-interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc.
**2011**, 137, 553–597. [Google Scholar] [CrossRef] - Caplan, P.; Derber, J.; Gemmill, W.; Hong, S.Y.; Pan, H.L.; Parrish, D. Changes to the 1995 NCEP operational medium-range forecast model analysis–forecast system. Weather Forecast.
**1997**, 12, 581–594. [Google Scholar] [CrossRef] - Legeais, J.F.; Ablain, M.; Thao, S. Evaluation of wet troposphere path delays from atmospheric reanalyses and radiometers and their impact on the altimeter sea level. Ocean Sci.
**2014**, 10, 893–905. [Google Scholar] [CrossRef] - Fernandes, M.; Lázaro, C. GPD+ wet tropospheric corrections for CryoSat-2 and GFO altimetry missions. Remote Sens.
**2016**, 8, 851. [Google Scholar] [CrossRef] - Komjathy, A.; Sparks, L.; Wilson, B.D.; Mannucci, A.J. Automated daily processing of more than 1000 ground-based GPS receivers for studying intense ionospheric storms. Radio Sci.
**2005**, 40. [Google Scholar] [CrossRef] - Scharroo, R.; Smith, W.H.F. A Global Positioning System-based climatology for the total electron content in the ionosphere. J. Geophys. Res. Space Phys.
**2010**, 115. [Google Scholar] [CrossRef] - Bilitza, D. International Reference Ionosphere 1990; National Space Science Data Center: Lanham, MD, USA, 1990.
- Bilitza, D.; Reinisch, B.W. International reference ionosphere 2007: Improvements and new parameters. Adv. Space Res.
**2008**, 42, 599–609. [Google Scholar] [CrossRef] - Tran, N.; Vandemark, D.; Chapron, B.; Labroue, S.; Feng, H.; Beckley, B.; Vincent, P. New models for satellite altimeter sea state bias correction developed using global wave model data. J. Geophys. Res.
**2006**, 111. [Google Scholar] [CrossRef] - Tran, N.; Philipps, S.; Poisson, J.-C.; Urien, S.; Bronner, E.; Picot, N. Impact of GDR_d Standards on SSB Corrections; OSTST: Venice, Italy, 2012. [Google Scholar]
- Scharroo, R.; Lillibridge, J. Non-Parametric Sea-State Bias Models and Their Relevance to Sea Level Change Studies. In Proceedings of the 2004 Envisat & ERS Symposium (ESA SP-572), Salzburg, Austria, 6–10 September 2004.
- Gaspar, P.; Labroue, S.; Ogor, F.; Lafitte, G.; Marchal, L.; Rafanel, M. Improving nonparametric estimates of the sea state bias in radar altimeter measurements of sea level. J. Atmos. Ocean. Technol.
**2002**, 19, 1690–1707. [Google Scholar] [CrossRef] - Ray, R.D. A Global Ocean Tide Model from TOPEX/POSEIDON Altimetry: Got99.2; NASA Technical Memorandum: NASA/TM-1999-209478; Goddard Space Flight Center: Greenbelt, MD, USA, 1999; p. 58.
- Carrère, L.; Lyard, F.; Cancet, M.; Guillot, A.; Roblou, L. Fes 2012: A New Global Tidal Model Taking Advantage of Nearly 20 Years of Altimetry. In Proceedings of the 20 years of Progress in Radar Altimetry, Venice, Italy, 24–29 September 2012.
- Schaeffer, P.; Faugére, Y.; Legeais, J.F.; Ollivier, A.; Guinle, T.; Picot, N. The CNES_CLS11 global mean sea surface computed from 16 years of satellite altimeter data. Mar. Geod.
**2012**, 35, 3–19. [Google Scholar] [CrossRef] - Andersen, O.; Knudsen, P.; Stenseng, L. The DTU13 MSS (mean sea surface) and MDT (mean dynamic topography) from 20 years of satellite altimetry. In International Association of Geodesy Symposia; Springer: Berlin/Heidelberg, Germany, 2015. [Google Scholar]
- Carrère, L.; Lyard, F. Modeling the barotropic response of the global ocean to atmospheric wind and pressure forcing—Comparisons with observations. Geophys. Res. Lett.
**2003**, 30. [Google Scholar] [CrossRef] - Cleveland, R.B.; Cleveland, W.S.; McRae, J.E.; Terpenning, I. STL: A seasonal-trend decomposition procedure base on loess. J. Off. Stat.
**1990**, 6, 3–73. [Google Scholar] - Fu, L.-L.; Christensen, E.J.; Yamarone, C.A.; Lefebvre, M.; Ménard, Y.; Dorrer, M.; Escudier, P. TOPEX/Poseidon mission overview. J. Geophys. Res.
**1994**, 99, 24369. [Google Scholar] [CrossRef] - Menard, Y.; Fu, L.L.; Escudier, P.; Parisot, F.; Perbos, J.; Vincent, P.; Desai, S.; Haines, B.J.; Kunstmann, G. The Jason-1 mission. Mar. Geod.
**2003**, 26, 131–146. [Google Scholar] [CrossRef] - Lambin, J.; Morrow, R.; Fu, L.-L.; Willis, J.K.; Bonekamp, H.; Lillibridge, J.; Perbos, J.; Zaouche, G.; Vaze, P.; Bannoura, W.; et al. The OSTM/Jason-2 mission. Mar. Geod.
**2010**, 33, 4–25. [Google Scholar] [CrossRef] - Keihm, S.; Ruf, C.S. Role of water vapor radiometers for in-flight calibration of the TOPEX microwave radiometer. Mar. Geod.
**1995**, 18, 139–156. [Google Scholar] [CrossRef] - Haines, B.J.; Bar-Sever, Y.E. Monitoring the TOPEX microwave radiometer with GPS: Stability of columnar water vapor measurements. Geophys. Res. Lett.
**1998**, 25, 3563–3566. [Google Scholar] [CrossRef] - 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. Mar. Geod.
**2004**, 27, 279–297. [Google Scholar] [CrossRef] - Desai, S.D.; Haines, B.J. Monitoring measurements from the Jason-1 microwave radiometer and independent validation with GPS. Mar. Geod.
**2004**, 27, 221–240. [Google Scholar] [CrossRef] - Edwards, S.; Moore, P.; King, M. Assessment of the jason-1 and TOPEX/Poseidon microwave radiometer performance using GPS from offshore sites in the North Sea. Mar. Geod.
**2004**, 27, 717–727. [Google Scholar] [CrossRef] - Macmillan, D.S.; Beckley, B.D.; Fang, P. Monitoring the TOPEX and Jason-1 microwave radiometers with GPS and VLBI wet zenith path delays. Mar. Geod.
**2004**, 27, 703–716. [Google Scholar] [CrossRef] - Sibthorpe, A.; Brown, S.; Desai, S.D.; Haines, B.J. Calibration and validation of the Jason-2/OSTM advanced microwave radiometer using terrestrial GPS stations. Mar. Geod.
**2011**, 34, 420–430. [Google Scholar] [CrossRef] - Zlotnicki, V.; Desai, S.D. Assessment of the Jason microwave radiometer’s measurement of wet tropospheric path delay using comparisons to SSM/I and TMI. Mar. Geod.
**2004**, 27, 241–253. [Google Scholar] [CrossRef] - Desportes, C.; Obligis, E.; Eymard, L. On the wet tropospheric correction for altimetry in coastal regions. IEEE Trans. Geosci. Remote Sens.
**2007**, 45, 2139–2149. [Google Scholar] [CrossRef] - Fernandes, M.; Nunes, A.; Lázaro, C. Analysis and inter-calibration of wet path delay datasets to compute the wet tropospheric correction for CryoSat-2 over ocean. Remote Sens.
**2013**, 5, 4977–5005. [Google Scholar] [CrossRef] - Gommenginger, C.P.; Srokosz, M.A. Sea State Bias—20 Years on. In Proceedings of the 15 Years of Progress in Radar Altimetry, Venice, Italy, 13–18 March 2006.
- Pires, N.; Fernandes, M.; Gommenginger, C.; Scharroo, R. A conceptually simple modeling approach for Jason-1 sea state bias correction based on 3 parameters exclusively derived from altimetric information. Remote Sens.
**2016**, 8, 576. [Google Scholar] [CrossRef] - 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. J. Geophys. Res.
**2003**, 108. [Google Scholar] [CrossRef] - Le Provost, C.; Genco, M.L.; Lyard, F.; Vincent, P.; Canceil, P. Spectroscopy of the world ocean tides from a finite element hydrodynamic model. J. Geophys. Res.
**1994**, 99, 24777. [Google Scholar] [CrossRef] - Andersen, O.B.; Knudsen, P. DNSC08 mean sea surface and mean dynamic topography models. J. Geophys. Res.
**2009**, 114. [Google Scholar] [CrossRef] - Nerem, R.S.; Chambers, D.P.; Choe, C.; Mitchum, G.T. Estimating mean sea level change from the Topex and Jason Altimeter missions. Mar. Geod.
**2010**, 33, 435–446. [Google Scholar] [CrossRef] - Fernandes, M.J.; Barbosa, S.M.; Lázaro, C. Impact of altimeter data processing on sea level studies. Sensors
**2006**, 6, 131–133. [Google Scholar] [CrossRef] - Socquet, A.; Simons, W.; Vigny, C.; McCaffrey, R.; Subarya, C.; Sarsito, D.; Ambrosius, B.; Spakman, W. Microblock rotations and fault coupling in SE Asia triple junction (Sulawesi, Indonesia) from GPS and earthquake slip vector data. J. Geophys. Res.
**2006**, 111. [Google Scholar] [CrossRef] - Gordon, A.L. Oceanography of the Indonesian seas and their throughflow. Oceanography
**2005**, 18, 14–27. [Google Scholar] [CrossRef] - Merrifield, M.A.; Thompson, P.R.; Lander, M. Multidecadal sea level anomalies and trends in the western tropical pacific. Geophys. Res. Lett.
**2012**, 39. [Google Scholar] [CrossRef] - England, M.H.; Huang, F. On the interannual variability of the Indonesian throughflow and its linkage with ENSO. J. Clim.
**2005**, 18, 1435–1444. [Google Scholar] [CrossRef] - Potemra, J.T.; Schneider, N. Interannual variations of the Indonesian throughflow. J. Geophys. Res.
**2007**, 112. [Google Scholar] [CrossRef] - Wolter, K.; Timlin, M.S. El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Climatol.
**2011**, 31, 1074–1087. [Google Scholar] [CrossRef] - Gordon, A.L.; Huber, B.A.; Metzger, E.J.; Susanto, R.D.; Hurlburt, H.E.; Adi, T.R. South China Sea throughflow impact on the Indonesian throughflow. Geophys. Res. Lett.
**2012**, 39, L11602. [Google Scholar] [CrossRef] - Liu, Q.; Huang, R.; Wang, D.; Xie, Q.; Huang, Q. Interplay between the Indonesian throughflow and the south china sea throughflow. Chin. Sci. Bull.
**2006**, 51, 50–58. [Google Scholar] [CrossRef] - Abidin, H.Z.; Andreas, H.; Gumilar, I.; Brinkman, J.J. Study on the risk and impacts of land subsidence in Jakarta. Proc. Int. Assoc. Hydrol. Sci.
**2015**, 372, 115–120. [Google Scholar] [CrossRef]

**Figure 1.**Geographical setting and location of the local GNSS network around the Indonesian seas that was used in this study. Red points indicate Jason-1 tracks for cycle 018 (Credit: Scripps Institution of Oceanography; http://TOPEX.ucsd.edu/marine_topo/).

**Figure 2.**Along-track SLA variance differences (cm

^{2}), ERA − NCEP, for TOPEX/Poseidon cycles 1 to 364 (

**top-left**); Jason-1 cycles 1 to 259 (

**top-right**) and Jason-2 cycles 1 to 280 (

**bottom-middle**).

**Figure 3.**SLA variance differences (cm

^{2}) of DTC (ERA − NCEP), function of the distance from coast: for TOPEX/Poseidon cycles 1 to 364 (

**top**), Jason-1 cycles 1 to 259 (

**middle**) and Jason-2 cycles 1 to 280 (

**bottom**).

**Figure 4.**SLA variance differences at crossovers (cm

^{2}), ERA − NCEP, for TOPEX/Poseidon cycles 1 to 364 (

**top-left**); Jason-1 cycles 1 to 259 (

**top-right**) and Jason-2 cycles 1 to 280 (

**bottom-middle**).

**Figure 5.**Along-track SLA variance differences (cm

^{2}), GPD+ − ERA (

**left**) and GPD+ − MWR (

**right**), for TOPEX/Poseidon (

**top**) cycles 1 to 364; Jason-1 (

**middle**) cycles 1 to 259 and Jason-2 (

**bottom**) cycles 1 to 280.

**Figure 6.**SLA variance differences of WTC, function of distance from coast, GPD+ − ERA (orange) and GPD+ − MWR (blue), for TOPEX/Poseidon cycles 1 to 364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 7.**SLA variance differences at crossovers (cm

^{2}), GPD+ − ERA (

**left**) and GPD+ − MWR (

**right**); for TOPEX/Poseidon (

**top**) cycles 1 to 364; Jason-1 (

**middle**) cycles 1 to 259 and Jason-2 (

**bottom**) cycles 1 to 280.

**Figure 8.**Along-track SLA variance differences (cm

^{2}), between the smoothed dual-frequency and NIC2009 for TOPEX/Poseidon cycles 1–219 (

**top left**) and between the smoothed dual-frequency and JPL GIM for TOPEX/Poseidon cycles 220 to 364 (

**top right**); Jason-1 cycles 1 to 259 (

**bottom left**) and Jason-2 cycles 1 to 280 (

**bottom right**).

**Figure 9.**SLA variance differences (cm

^{2}) function of the distance from the coast;

**top**to

**bottom**: between the smoothed dual-frequency and NIC2009 (orange) for TOPEX/Poseidon cycles 1–219 and between the smoothed dual-frequency and JPL GIM (blue) for TOPEX/Poseidon cycles 220 to 364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 10.**SLA variance differences at crossovers (cm

^{2}), between the smoothed dual-frequency and NIC2009 for TOPEX/Poseidon cycles 1–219 (

**top left**) and between the smoothed dual-frequency and JPL GIM for TOPEX/Poseidon cycles 220 to 364 (

**top right**); Jason-1 cycles 1 to 259 (

**bottom left**) and Jason-2 cycles 1 to 280 (

**bottom right**).

**Figure 11.**Along-track SLA variance differences (cm

^{2}),

**Top**panels: between Sea State Bias Non-Parametric CLS and Sea State Bias Parametric BM4 for TOPEX ((

**left**) TOPEX-A cycles 1 to 235; (

**right**) TOPEX-B cycles 236 to 264);

**Bottom**panels: corresponding differences between Sea State Bias Non-Parametric Tran2012 and CLS SSB models for Jason-1 cycles 1 to 259 (

**left**) and Jason-2 cycles 1 to 280 (

**right**).

**Figure 12.**SLA variance differences (cm

^{2}) function of distance from the coast between Sea State Bias Non-Parametric CLS and Sea State Bias Parametric BM4 for TOPEX (orange): TOPEX-A cycles 1 to 235 and TOPEX-B cycles 236 to 264; and Sea State Bias Tran and Non-Parametric CLS (blue): Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 13.**

**Top**panels: SLA variance differences at crossovers (cm

^{2}), between Sea State Bias Non-Parametric CLS and Sea State Bias Parametric BM4 for TOPEX ((

**left**) TOPEX-A cycles 1 to 235; (

**right**) TOPEX-B cycles 236 to 264);

**Bottom**panels: corresponding differences between Sea State Bias Non-Parametric Tran2012 and CLS SSB models for Jason-1 cycles 1 to 259 (

**left**) and Jason-2 cycles 1 to 280 (

**right**).

**Figure 14.**Along-track SLA variance differences (cm

^{2}), between GOT4.10 and FES2012 tide model for TOPEX/Poseidon cycles 1–364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 15.**SLA variance differences (cm

^{2}) function of distance from the coast between GOT4.10 and FES2012 tide models for TOPEX cycles 1 to 364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 16.**SLA variance differences at crossovers (cm

^{2}), between GOT4.10 and FES2012 tide model for TOPEX/Poseidon cycles 1–364, Jason-1 cycles 1 to 259, and Jason-2 cycles 1 to 280.

**Figure 17.**EGM2008 geoid height around the Indonesia seas (Credit: NGA Office of Geomatics; http://earth-info.nga.mil/GandG/wgs84/gravitymod/egm2008/egm08_gis.html).

**Figure 18.**Along-track SLA variance differences (cm

^{2}), between DTU13 and CNESCLS2011 MSS models for TOPEX/Poseidon cycles 1 to 364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 19.**SLA variance differences (cm

^{2}) function of distance from the coast between DTU13 and CNESCLS2011 MSS models for TOPEX/Poseidon cycles 1 to 364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 20.**SLA variance differences at crossovers (cm

^{2}), between DTU13 and CNESCLS2011 MSS models for TOPEX/Poseidon cycles 1 to 364, Jason-1 cycles 1 to 259 and Jason-2 cycles 1 to 280.

**Figure 21.**Mean SLA values (mm) for the cycles of the TOPEX/Poseidon–Jason-1 tandem mission (

**left**) and for the Jason-1–Jason-2 tandem mission (

**right**). Jason-1 values corrected for the (TP, J1) bias and J2 values corrected for the (J1, J2) bias are also shown.

**Figure 22.**Sea level anomaly (mm) time series around the Indonesian seas from three different satellite altimeters (grey dots), with 60-day smoothing, semi-annual and annual seasonal signals removed (green line), annual variation (red line), and inter-annual variation (blue line) and SLA linear trend (dashed line). The two vertical lines refer to the separation of the periods of the various missions.

**Figure 23.**Inter-annual mean sea level variation in the Indonesian seas using the calibrated GPD+ (blue) and corresponding linear trend (black). Inter-annual mean sea level variation in the Indonesian seas using the non-calibrated GPD (cyan) and corresponding linear trend (grey).

**Figure 24.**The map of sea level trend (unit in mm/year) around the Indonesian seas, estimated from three different satellite altimeters (T/P, Jason-1 and Jason-2) over 23 years. The map has been computed from the SLA time series of mean cycle values, of 4° × 4° grid, and MSL slope has been determined using least squares.

**Figure 25.**Overlaying along-track SLA variance differences (cm

^{2}), between DTU13 and CNESCLS2011 MSS models for Jason-2 and Structure map of the Sunda-Australia-Philippine-Pacific plates junction area. The green lines are the plate boundaries and the arrows depict the far-field velocity of the plates with respect to Eurasia [69].

**Figure 26.**The detrended SLA around the Indonesian Seas (black line) and Multivariate ENSO Index (MEI): El Niño (red) and La Niña (blue).

**Table 1.**Various range and geophysical corrections and mean sea surface models assessed in this study, aiming at determining sea level variability around the Indonesian Seas.

Parameter | TOPEX/Poseidon | Jason-1 | Jason-2 |
---|---|---|---|

Cycles | 001–364 | 001–259 | 001–280 |

Dry Troposphere | ERA-Interim & NCEP | ERA-Interim & NCEP | ERA-Interim & NCEP |

Wet Troposphere | On-board MWR, ERA-Interim & GPD+ | On-board MWR, ERA-Interim & GPD+ | On-board MWR, ERA-Interim & GPD+ |

Ionosphere | Smoothed Dual Frequency & NIC09 (cycles: 1–219), GIM (cycles: 220–364) | Smoothed Dual Frequency & GIM | Smoothed Dual Frequency & GIM |

Sea State Bias | Parametric BM4 & Non-parametric: CLS | Non-parametric: CLS & Tran2012 | Non-parametric: CLS & Tran2012 |

Dynamic atmospheric correction (DAC) | MOG2D | MOG2D | MOG2D |

Ocean Tide and Loading Tide | FES2012 & GOT4.10 | FES2012 & GOT4.10 | FES2012 & GOT4.10 |

Mean Sea Surface | CNESCLS 2011 & DTU2013 | CNESCLS 2011 & DTU2013 | CNESCLS 2011 & DTU2013 |

**Table 2.**Coefficients of BM4 model for satellite altimetry [41].

Mission | a_{0} | a_{1} | a_{2} | a_{3} | a_{4} |
---|---|---|---|---|---|

TOPEX A | 0.012450 | −0.030578 | 0.002776 | −0.002962 | 0.000127 |

TOPEX B | 0.028889 | −0.032113 | 0.002992 | −0.002780 | 0.000101 |

Poseidon | 0.015731 | −0.062778 | 0.001894 | −0.001194 | 0.000057 |

TOPEX/Poseidon | Jason-1 | Jason-2 | |
---|---|---|---|

Dry Troposphere | ERA-Interim | ERA-Interim | ERA-Interim |

Wet Troposphere | GPD+ | GPD+ | GPD+ |

Ionosphere | Smoothed Dual Frequency | Smoothed Dual Frequency | Smoothed Dual Frequency |

Sea State Bias | TOPEX_A: BM4 | Tran2012 Model | Tran2012 Model |

TOPEX_B: NP-CLS | |||

Poseidon: BM4 | |||

Ocean and Load Tide | FES2012 for <60 km | FES2012 for <60 km | FES2012 for <60 km |

GOT 4.10 for >60 km | GOT 4.10 for >60 km | GOT 4.10 for >60 km | |

Mean Sea Surface | CNES CLS 2011 | CNES CLS 2011 | CNES CLS 2011 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Handoko, E.Y.; Fernandes, M.J.; Lázaro, C. Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas. *Remote Sens.* **2017**, *9*, 102.
https://doi.org/10.3390/rs9020102

**AMA Style**

Handoko EY, Fernandes MJ, Lázaro C. Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas. *Remote Sensing*. 2017; 9(2):102.
https://doi.org/10.3390/rs9020102

**Chicago/Turabian Style**

Handoko, Eko Yuli, Maria Joana Fernandes, and Clara Lázaro. 2017. "Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas" *Remote Sensing* 9, no. 2: 102.
https://doi.org/10.3390/rs9020102