Jason-1 Data Research Articles

KENAIKAN MUKA AIR LAUT PERAIRAN SUMATERA BARAT BERDASARKAN DATA SATELIT ALTIMETRI JASON-2

<p><em>The phenomenon of the sea level rise caused by many factors, one of which is global warming. Coastal areas are most vulnerable regions affected by sea level rise. Therefore, the information of sea level rise are used as consideration and policy-making on development plans for coastal areas like in West Sumatera Waters. The aims of this research are to identify the quality of Satelit Altimetry Jason-2 Data in West Sumatera Waters and to analysis the information of sea level rise of West Sumatera sea based on satelit altimetry Jason-2 data. Sea Level Rise in West Sumatera Water are identified by several steps, begin with collecting satellite altimetry Jason-2 data from 2008 to 2015 years. Then extraction Sea Surface Height (SSH) value of binary GDR data from Jason-2 by post processing to eliminate the geophysic errors, furthermore extraction undulation geoid value and calculating the Sea Level Anomaly (SLA) value. To identify the sea level rise value used linear regression analysis on the SLA data. The results of this research shown the existence of satellite altimetry Jason-2 data is 92.91%. The mean sea level rise in West Sumatera Waters during th period 8 year is 6.88 mm, and mean sea level rise of West Sumatera sea is 0.86 mm/year.</em></p>

Validation of the Significant Wave Height Product of HY-2 Altimeter

HY-2 was launched by China on August 2011, which has provided continuous wave height measurements to monitor ocean dynamic environments for more than 5 years. Before using these data, however, the measurements need to be validated. Based on the in situ buoy data from the National Data Buoy Center (NDBC) and the Jason-2 altimeter data, the HY-2 Ku-band significant wave height (SWH) measurements were validated. The comparisons showed that a linear regression with NDBC measurements can be used to improve the accuracy of the HY-2 SWH measurements. Compared with the NDBC SWH data, the validation results of the HY-2 SWH data show an RMS (root mean square) of 0.33 m, which is similar to that of the Jason-1 and Jason-2 data; the RMS of the HY-2 SWH is 0.30 m, which, corrected via linear regression, is similar to that of the corrected Jason-1 and Jason-2 data (0.27 m and 0.23 m, respectively). Therefore, the accuracy of the HY-2 SWH products is close to that of the Jason-1/2 SWH data.

Automated Generation of Lakes and Reservoirs Water Elevation Changes From Satellite Radar Altimetry

Limited access to in-situ water level data for lakes and reservoirs have been a major setback for regional and global studies of reservoirs, surface water storage changes, and monitoring the hydrologic cycle. Processing satellite radar altimetry data over inland water bodies on a large scale has been a cumbersome task primarily due to the removal of contaminated measurements as a result of surrounding land. In this study, we proposed a new algorithm to automatically generate time series from raw satellite radar altimetry data without user intervention. With this method, users with a little knowledge on the field can now independently process radar altimetry for diverse applications. The method is based on K-means clustering, interquartile range, and statistical analysis of the dataset for outlier detection. Jason-2 and Envisat radar altimetry data were used to demonstrate the capability of this algorithm. A total of 37 satellite crossings over 30 lakes and reservoirs located in the U.S., Brazil, and Nigeria were used based on the availability of in-situ data. We compared the results against in-situ data and root-mean-square error values ranged from 0.09 to 1.20 m. We also confirmed the potential of this algorithm over rivers and wetlands using the southern Congo River and Everglades wetlands in Florida, respectively. Finally, the different retracking algorithms in Envisat; Ice-1, Ice-2, Ocean, and Sea-Ice were compared using the proposed algorithm. Ice-1 performed best for generating water level time series for in-land water bodies and the result is consistent with previous studies.

Improved Sea Surface Height From Satellite Altimetry in Coastal Zones: A Case Study in Southern Patagonia

High-resolution 20-Hz Jason-2 satellite altimetry data obtained from crossing tracks numbered 52 and 189 in San Matias Gulf, Argentina, are compared with a 22-month-long time series of sea level measured by a bottom pressure recorder. It was deployed 1.3 km from the nominal intersection of the two tracks and 0.9 km from the coast. Results show that by improving retracking and tidal modeling, satellite altimetry data become more accurate close to the coast. Indeed, a larger number of reliable data are obtained up to 1.6 km from the coast when satellite data are retracked using adaptive leading edge subwaveform retracker (ALES) rather than using the classic Brown model. The tidal model that showed the lowest root sum square (RSS) of the difference between the in situ and the modeled tidal amplitude and phase is TPXO8 (RSS 4.8 cm). Yet, the lowest difference from in situ tidal constituents is obtained by harmonic analysis of the available 23-year-long 1-Hz altimetry dataset (RSS 4.1 cm), highlighting the potential of altimetry data to compute tides. Considering ALES retracking and TPXO8 tidal correction for the 20-Hz Jason-2 data, we finally show that it is possible to retrieve 70% more data and to improve correlation with in situ measurements from 0.79 to 0.95. The sea level anomaly obtained this way has a root mean square difference from in situ data of only 13 cm as close as 4 km from the coast. Overall, the analysis performed indicates that satellite altimetry data can be greatly improved, even in complex macrotidal coastal regions.

Mean dynamic topography over Peninsular Malaysian seas using multimission satellite altimetry

The development of satellite altimeters (SALTs) has brought huge benefits, among which is the ability to more adequately sense ocean-surface topography. The radar altimeter database system was used to capture and process ENVISAT, CRYOSAT-2, SARAL, JASON-1, and JASON-2 SALT data of 5 years between 2011 and 2015. The time series of monthly multimission SALT data showed an estimated sea level trend of 1.0, 2.4, 2.4, 3.6, and 12.0 mm/year at Gelang, Port Kelang, Kukup, Cendering, and Keling. The correlation analysis for the selected tide gauge stations produced satisfying results of R-squared with 0.86, 0.89, 0.91, and 0.97 for Cendering, Sedili, Gelang, and Geting, respectively. The ITG-Grace2010s geoid model was used to compute the mean dynamic topography (MDT) and plot to a grid of 0.25 deg for the Malacca Strait and South China Sea of Peninsular Malaysia, with Keling, Port Kelang, Geting, Sedili, and Johor Bahru tide gauge stations having values determined by interpolation to be 1.14, 1.19, 1.26, 1.88, and 2.91 m, respectively. MDT is computed from the SALT with respect to Port Kelang, the north–south sea slope ranges between −0.64 and 0.29 m/50 km and −0.01 and 0.52 m/50 km along the east and west coasts of Peninsular Malaysia, respectively.

Evaluation of new CryoSat-2 products over the ocean

The CryoSat-2 satellite, primarily dedicated to precise monitoring of the Cryosphere, is demonstrating its capability to provide valuable altimetric data also over the ocean. Here we present the results of a global assessment and validation of the new Geophysical Ocean Product (GOP) distributed by the European Space Agency (ESA) since April 2014, focusing on the sea surface height anomaly (SSHA), the significant wave height (SWH), and the wind speed. Our assessment involves only Low Resolution Mode (LRM) and Pseudo LRM (PLRM) data, since full SAR processing is not already operationally implemented in the GOP. The global assessment is conducted on the basis of measurement noise and along-track spectral and crossover analysis, whereas the validation is performed against a variety of in situ observations such as tide gauges, buoys and Argo floats as well as data from the WaveWatch III (WWIII) model. The performance of the GOP is compared to that of Jason-2 and CryoSat-2 data from the Radar Altimeter Database System (RADS). The mean value of the 20-Hz SSHA noise at 2 m SWH is 6.3 cm for LRM and 10.2 cm for PLRM, and the standard deviation of the crossovers is ~ 5.4 cm. The mean 20-HZ SWH noise over the global oceans is 49.4 cm and 69.8 cm, for LRM and PLRM respectively. CryoSat-2 and Jason-2 show almost identical performance when SSHAs are validated against tide gauges, with a median correlation and root mean square difference (RMSD) of 0.78 and 7.1 cm for the GOP, 0.76 and 7.3 cm for Jason-2, and 0.79 and 7.8 cm for CryoSat-2 from RADS. The median correlation with Argo-derived steric heights is 0.68 for the GOP, 0.74 for Jason-2, and 0.67 for CryoSat-2 from RADS. However, the correlation shows a strong latitudinal dependence, with higher values at low latitudes (median value larger than 0.80 in the 10°S-10°N band). The median RMSD between the SSHAs and steric heights is 5.3 cm for the GOP, 4.6 cm for Jason-2, and 5.1 cm for CryoSat-2 from RADS. The GOP and Jason-2 show also identical performance when SWHs are compared to buoy data, with a slope and RMS error of 0.98 and 15 cm for GOP, 0.97 and 16 cm for Jason-2, and 1.05 and 17 cm for CryoSat-2 from RADS. On the other hand, the GOP wind speed exhibits a bias of about 2 m/s relative to both Jason-2 and to buoy data. Differences between the GOP and WWIII SWH are smaller than 20% of the SWH almost everywhere. In summary the GOP products are fit for oceanographic applications.

Calculating the Marine Gravity Anomaly of the South China Sea based on the Inverse Stokes Formula

Marine gravity field information has a great significance for the resource, environment and military affairs. As a new way to get marine gravity data, the satellite altimetry technique makes up for what ship measuring means lack. The paper carries out the researches on how altimeter data applied for calculating marine gravity anomaly based on inverse Stokes formula. In the article, the editing of 14-track Jason-1 data over South China Sea for 7 years is for collinear processing and cross-point adjustment. The inverse Stokes formula and fast Flourier transform technique are applied to calculate marine gravity anomaly of the region (0°∼23°N, 103°∼120°E), and to draw gravity anomaly map. Compared with the gravity anomaly by ship observation, RMS is 12.6mGal, and single altimetry satellite has a good precision.

Update of the corrective model for Jason-1 DORIS data in relation to the South Atlantic Anomaly and a corrective model for SPOT-5

Abstract After recalling the principle of the Jason-1 data corrective model in relation to the South Atlantic Anomaly (SAA) developed by Lemoine and Capdeville (2006), we present a model update which takes into account the orbit changes and the recent DORIS data. We propose also here a method to the International DORIS Service (IDS) Analysis Centers (ACs) in their contribution to the ITRF2014 for adding DORIS Jason-1 data into their solutions. When the Jason-1 satellite is added to the multi-satellite solution (orbit of inclination of 66° complements the polar-orbiting satellites), the stability of the geocenter Z-translation is improved (standard deviation of 11.5 mm against 16.5 mm). In a second part we take advantage of a high-energy particles dosimeter (CARMEN) on-board Jason-2 to improve the corrective model of Jason-1. We completed a correlation study showing that the CARMEN >87 MeV integrated proton flux map averaged over the period 2009–2011 is the energy band of the CARMEN maps which are the most coherent with the one obtained from Jason-1 DORIS measurements. The model based on the Jason-1 map and the one based on the CARMEN map are then compared in terms of orbit determination and station position estimation. We derive and validate a SPOT-5 data corrective model. We determine the SAA grid at the altitude of SPOT-5 from the frequency time derivative of the on-board frequency offsets and estimated the model parameters. We demonstrate the impact of the SPOT-5 data corrective model on the Precise Orbit Determination and the station position estimation from the weekly solutions, based on two individual Analysis Centers solutions, GOP (Geodetic Observatory Pecny) and GRG (Groupe de Recherche de Geodesie Spatiale). The SPOT-5 data corrective model significantly improves the Precise Orbit Determination (reduction of 1.4% in 2013 of RMS of the fit, reduction of 25% in normal direction of arc overlap RMS) and the overall statistics of the station position estimation (reduction of 2% of repeatability RMS, reduction of 3–7% of geocenter variations). Moreover, the application of the data corrective model strongly reduces the individual station bias in the North component for all the SAA-affected stations and the height bias for the most affected SAA stations. The East bias is, however, not reduced by this data corrective model.

Resolution of Seamount Geoid Anomalies Achieved by the SARAL/AltiKa and Envisat RA2 Satellite Radar Altimeters

The resolution of seamount geoid anomalies by the SARAL/AltiKa Ka-band radar altimeter is compared with the Envisat RA2 Ku-band altimeter using cross-spectral analysis of exact-repeat profiles. Noise spectra show white noise floors at root-mean-square levels around 8 mm per root-Hz for AltiKa and 19 mm per root-Hz for RA2, and are colored at wavelengths longer than a few km, with a spectral hump similar to that seen in Jason-2 data. The AltiKa noise level is lower than the RA2 noise level by more than one would expect from the ratio of their pulse repetition frequencies. Large outliers are present in data from both altimeters, always of one sign (range too long), and show little correlation with rain or other error flags. Seamount anomaly signal to noise ratios are 30 to 10 dB for AltiKa and 3 to 8 dB less for RA2, decreasing as seamount size decreases. Seamounts as small as 1.35 km tall are resolved by both instruments, with significantly better performance by AltiKa due to its lower noise level. If AltiKa can fly a geodetic mission, it will find many presently unknown seamounts.

Preliminary Assessment of SARAL/AltiKa Observations over the Ganges-Brahmaputra and Irrawaddy Rivers

Radar altimetry has demonstrated strong capabilities for the monitoring of water levels of lakes, rivers and wetlands over the last 20 years. The Indo-French SARAL/AltiKa mission, launched in February 2013, is the first satellite radar altimetry mission to carry onboard a Ka-band sensor. We propose here to evaluate the potential of this new instrument for land hydrology through comparisons with other altimetry-derived stages and discharges in the Ganges-Brahmaputra and Irrawaddy river basins using its first year of data. Due to the lack of concomitant in situ measurements for the current period, Jason-2 data, previously evaluated against in situ gauge records, were used as reference. Comparisons between Jason-2 and SARAL-derived water levels and discharges, and Jason-2 and Envisat (which flew the same orbit as SARAL from 2002 to 2010)-derived ones, was performed. Time-series of only one year of SARAL-derived water levels and discharges present better performances (lower RMSE and higher R, generally greater than 0.95) than the ones derived from Envisat when compared with Jason-2.

Validation of AltiKa Matching Pursuit Rain Flag

The major drawback of Ka band, operating frequency of the AltiKa altimeter on board SARAL, is its sensitivity to atmospheric liquid water. Even light rain or heavy clouds can strongly attenuate the signal and distort the signal leading to erroneous geophysical parameters estimates. A good detection of the samples affected by atmospheric liquid water is crucial. As AltiKa operates at a single frequency, a new technique based on the detection by a Matching Pursuit algorithm of short scale variations of the slope of the echo waveform plateau has been developed and implemented prelaunch in the ground segment. As the parameterization of the detection algorithm was defined using Jason-1 data, the parameters were re-estimated during the cal-val phase, during which the algorithm was also updated. The measured sensor signal-to-noise ratio is significantly better than planned, the data loss due to attenuation by rain is significantly smaller than expected (<0.1%). For cycles 2 to 9, the flag detects about 9% of 1Hz data, 5.5% as rainy and 3.5 % as backscatter bloom (or sigma0 bloom). The results of the flagging process are compared to independent rain data from microwave radiometers to evaluate its performances in term of detection and false alarms.

Assessment of SARAL/AltiKa Wave Height Measurements Relative to Buoy, Jason-2, and Cryosat-2 Data

SARAL/AltiKa GDR-T are analyzed to assess the quality of the significant wave height (SWH) measurements. SARAL along-track SWH plots reveal cases of erroneous data, more or less isolated, not detected by the quality flags. The anomalies are often correlated with strong attenuation of the Ka-band backscatter coefficient, sensitive to clouds and rain. A quality test based on the 1 Hz standard deviation is proposed to detect such anomalies. From buoy comparison, it is shown that SARAL SWH is more accurate than Jason-2, particularly at low SWH, and globally does not require any correction. Results are better with open ocean than with coastal buoys. The scatter and the number of outliers are much larger for coastal buoys. SARAL is then compared with Jason-2 and Cryosat-2. The altimeter data are extracted from the global altimeter SWH Ifremer data base, including specific corrections to calibrate the various altimeters. The comparison confirms the high quality of SARAL SWH. The 1 Hz standard deviation is much less than for Jason-2 and Cryosat-2, particularly at low SWH. Furthermore, results show that the corrections applied to Jason-2 and to Cryosat-2, in the data base, are efficient, improving the global agreement between the three altimeters.

LiquidEarth - river

Most of the world's population lives near a freshwater source or on the coast and are thus vulnerable to the rise in water level. There are around 260 trans-boundary water bodies that cover more than 40% of the Earth's land surface and account for 60% of global freshwater flow [1] [2]. Most developing countries located at the downstream end of a large river basin are often plagued with flooding problems that are trans-boundary because the flood waters are generated upstream in other nations. However, many large river basins in the developing world are mostly ungauged as they lack the necessary in-situ measurements required for the setting up of flood forecasting models. For such basins, using data from satellite may be the only alternative to overcome the lack of in-situ data. LiquidEarth-River [3] is a client-server based framework that aims to deliver surface-water relevant information such as river height, flood inundation, and reservoir storage directly to the user. LiquidEarth-River version 1.0 provides 8 days river height forecast at two in-situ points (Hardinge Bridge and Bahadurabad Ghat) in Bangladesh using satellite data at upstream locations in India where in-situ data is unavailable. The river levels in upstream locations are obtained from Jason-2 altimetry data (joint NASA-French Satellite). The value of LiquidEarth-River is in better understanding of the future (an 8 day lead time) of potential water levels near rivers, in light of the societal implications for water encroachment into homesteads and farmlands. LiquidEarth-River uses recent innovations in computer science such as multicore and GPU based desktops to solve computationally intensive problems such as river height forecasting. Typically, these problems are solved using traditional cluster or supercomputers. The server side of the application runs on a desktop that downloads Jason-2 data daily, and runs a forecasting scheme to provide the forecast. The client side of the application is a smart phone or a web app that provides a river height forecast to the user. The application has scope for expansion in two areas: 1) integration of new and multiple satellite altimeters, 2) addition of a more intuitive communication format that converts the information to knowledge (such as converting river level forecast to inundation and impact on villagers, houses, roads etc.). For example, farmers of Bangladesh need early warning of monsoonal floods to decide on early harvest or delayed sowing. Providing a visual map of what an X meter of river level means to a farmer, an 8 day forecast can provide a greater sense of empowerment for saving lives, and economic decision making. Currently, the warnings that farmers receive in most developing countries are not only lead-time limited, but are available only to highly skilled users, and are not so intuitive that makes it easy for a layman to understand the value of the warnings. LiquidEarth attempts to remove these barriers to interpretation by creating a highly intuitive and visual format readily accessible to the average farmer who has little time to get training or read a complicated manual.

Global Land Surface Backscatter at Ku-Band Using Merged Jason1, Envisat, and Jason2 Data Sets

A unique method for investigating continental surfaces uses backscatter data measured by a radar altimeter at the nadir point of a satellite, in contrast to other microwave sensors designed to work at oblique angles, such as scatterometers and synthetic aperture radar. To improve the altimetry resolution over land, we generated 0.5° × 0.5° merged altimetry backscatter maps covering 66° N to 66° S over the global land surface every six days for the period from January 2002 to June 2009 by combining three altimeter data sets (Jason1, Envisat, Jason2) in the Ku-band. The four backscatter products of Envisat RA2 from different retracking algorithms were evaluated prior to merging with the Jason1 and Jason2 data. The global pattern and the seasonal variation of the merged altimetry backscatter were examined, which show the merged results have better spatial sampling for the regional to global geophysical process due to the combination of three altimeters. To understand how the altimetry backscatter is related to land surface parameters, the advanced integral equation model for bare soil and water cloud model for vegetation are used to simulate the Ku-band backscatter response to soil and vegetation parameters at an incidence of 0°. Furthermore, we compared the time series of merged altimetry backscatter with the leaf area index (LAI) determined by an optical sensor and the backscatter coefficients obtained from a scatterometer (QuikSCAT) over seven selected vegetated areas over six years. The results confirm the sensitivity of ocean altimetry to vegetation. Further studies relating altimetry backscatter to geophysical parameter are needed.

Radar altimetry backscattering signatures at Ka, Ku, C, and S bands over West Africa

This study presents a comprehensive comparison of radar altimetry signatures at Ka-, Ku-, C-, and S-bands using SARAL, ENVISAT and Jason-2 data over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, with an emphasis on the new information at Ka-band provided by the recently launched SARAL–Altika mission. Spatio-temporal variations of the radar altimetry responses were related to changes in surface roughness, land cover and soil wetness. Analysis of time series of backscattering coefficients along the West African bioclimatic gradient shows that radar echoes at nadir incidence are well correlated to soil moisture in semi-arid savannah environments. Radar altimeters are able to detect the presence of water even under a dense canopy cover at all frequencies. But only measurements at Ka-band are able to penetrate underneath the canopy of non-inundated tropical evergreen forests.

Retracking Cryosat data in the SARIn mode and robust lake level extraction

Abstract Lake levels are an indicator for the hydrological state of an area. Lake levels are directly and indirectly affected by meteorological variations and are both indicators for and caused by water management. Therefore, it is important to monitor the changes of water levels in lakes. As an alternative to water level gauges, satellite radar altimetry is an established method. The problem of radar altimetry so far is that it only gives reliable lake levels for relatively large lakes. The recent Cryosat mission passes many lakes in the so-called SARIn mode at improved along-track resolution. Still, the accuracy and robustness of standard level 2 is limited due to pollution of the waveform by surrounding land topography. We propose to actively extract the water surface component by a novel retracking method based on cross-correlation of observed level 1b waveforms with a generic simulated waveform. Different scatterers will result in different cross-correlation solutions, but, as the water level is contributing to each consecutive waveform, it can efficiently be extracted by a majority voting scheme. To demonstrate the performance of the proposed retracker, we compared the estimated lake levels with accurate Jason-2 estimates over Lake Nasser in Egypt. The proposed retracking algorithm performs better than the level 2 product, and has a RMS of differences with Jason-2 lake levels of 0.30 m. The remaining differences with Jason-2 data are correlated to the amount of topographic pollution, but also show some remaining surface slope artifact. By using the proposed method, one can optimally profit from Cryosat measurements for lake level measurements. Not only can we retrieve lake levels closer to the shore, but due to its dense ground track spacing (7–8 km at the Equator) it is possible to estimate lake levels over many more lakes than with other altimeters. Cryosat lake level estimates are therefore a promising extension of existing time series as measured by notably Envisat, Jason-1 & 2 and GFO.

Demonstrating the complementarity of observations in an operational ocean forecasting system

We have performed a series of near-real-time observing system experiments with FOAM, the Met Office operational ocean forecasting system. These were conducted in parallel to the operational suite and identical to it except that certain observation types were excluded. At the start of each month the parallel system was reset to the operational restart and a run started with a different observation type excluded: in February –XBT; March –TAO/TRITON; April –Jason-2 altimeter; May –all altimeter; June –AVHRR sea-surface temperature (SST) data; and July –Argo data. We show that the existing ocean observing systems offer a good deal of complementary information. All components of the observing system offer unique, independent information that help initialise, and improve, operational ocean forecasts. Withholding XBTs causes little impact on globally averaged metrics, for example RMS innovations. However, locally we see long-lasting temperature impacts (∼1 °C) near XBT transects. Withholding TAO/TRITON data has a regional impact increasing the tropical Pacific RMS temperature and salinity innovations by 39 and 60%, respectively. Withholding Jason-2 data results in a global 4% increase in the RMS sea-surface height (SSH) innovations, and small-scale changes in temperature and salinity of about 2 °C at 100 m depth, and ∼0.2 psu near the surface. Withholding all altimeter data leads to a 16% increase of the RMS SSH innovations. We also see changes in other model variables similar in magnitude to those from withholding Jason-2, but more widespread. Withholding AVHRR SST data produces significant changes around 1 °C in model temperature at the surface and within the surface mixed layer, but there is little or no effect below this. Withholding Argo data for 1 month leads to a 5% increase in the temperature and salinity innovations, and changes in SSH of up to 5 cm.

Temporal and spatial variations in ionospheric electron density profiles over South Africa during strong magnetic storms

Abstract. Observations from the South African TrigNet global navigation satellite system (GNSS) and vertical total electron content (VTEC) data from the Jason-1 satellite were used to analyze the variations in ionospheric electron density profiles over South Africa before and after the severe geomagnetic storms on 15 May 2005. Computerized ionospheric tomography (CIT) was used to inverse the 3-D structure of ionospheric electron density and its response to the magnetic storms. Inversion results showed that electron density significantly increased at 10:00 UT, 15 May compared with that at the same period on 14 May. Positive ionospheric storms were observed in the inversion region during the magnetic storms. Jason-1 data show that the VTEC observed on descending orbits on 15 May significantly increased, whereas that on ascending orbits only minimally changed. This finding is identical to the CIT result.

Preprocessing of Coastal Satellite Altimetry, Tide Gauges, and GNSS Data: Towards the Possibility of Detected Vertical Deformation of South Java Island

Abstract The Combination of sea surface height (SSH) of satellite altimetry and tide gauge (TG) data with control of GNSS data is proposed as an alternative method of detecting vertical movement of South Java island due to the subduction process. Before the combination, both SSHs have to be preprocessed, especially the satellite altimetry data over coastal area which is noisy due to land contamination signal and fast change of meteorological condition as the location shifts from land to ocean. Preprocessing data was done through retracking waveform altimetry data using two retracking methods. The first processing result of Jason-1 data shows that several complex patterns have occured on the altimetry waveform over the coast of Java Island. It also indicates that the position of the leading edge (PLE) has shifted for about 1.01 - 1.7 from the value of on-board PLE, resulting in the amount of correction of within ± 0.68m. At the same time, the TG data have also been preprocessed to address the gap, spike and change in reference before re-sample following the altimetry sampling time. Using a matlab developed-program, three main problems on TG data could be resolved, and the final TG data has adequate quality for the study. Finally, the GNSS measurement at the tide gauges stations have been processed using GAMIT software to compute the velocity of the crustal movement in 3D, as the control of the proposed-method. Based on statistical testing, it is identified that there is a significant difference in all 3D components of four stations with velocity of 2 – 5 cm/year except for Tanjung Mas station, city of Semarang, that has been experiecning rapid land subsidence.

원격탐사를 이용한 한반도 주변해역의 해수면/해수온의 시·공간변동 특성 연구

NOAA/AVHRR, Topex/Poseidon, and Jason-1 data were used to analyze sea surface temperatures and thermal fronts in the North East Asia Seas. Temporal and spatial analyses were based on data from 1993 to 2008. The amplitude and phase for the annual mode on SL and SST were investigated with harmonic analysis. The geographical distribution of amplitudes for comparison of SL and SST are slightly reverse in southwest-northeast tilted direction. The time series analysis conducted on the entire researched area presented consistent pattern. Peak of Sea Level was presented 1~2 months after the peak of the surface sea temperature was shown. This explains that Sea Level change occurs after the generation of surface sea temperature change in sea. The Sobel edge detection method delineated four fronts. Thermal fronts generally occurred over steep bathymetric slopes. Annual amplitudes and phases were bounded within these frontal areas.