scholarly journals Evaluation of altimetry satellite data products and sea level trends in the Indonesian maritime continent

2021 ◽  
Vol 944 (1) ◽  
pp. 012041
Author(s):  
J Lumban-Gaol ◽  
S Vignudelli ◽  
I W Nurjaya ◽  
N M N Natih ◽  
M E Sinurat ◽  
...  
Keyword(s):  
Sea Level ◽  
Time Series Data ◽  
Tide Gauge ◽  
Sea Surface ◽  
Height Anomaly ◽  
Series Data ◽  
Maritime Continent ◽  
Global Trend ◽  
Data Products ◽  
Sea Surface Height Anomaly

Abstract This study examines the accuracy of the sea surface height anomaly (SSHA) altimetry data products of Copernicus, Colorado University (CU), and X-TRACK-Centre for Topographic studies of the Ocean and Hydrosphere (X-TRACk-CTOH). The SSHA derived from altimetry accuracy was tested by comparison with tide gauge (TG) observations. Taking measurements along the IMC coast demonstrates the excellent agreement between the SSHA derived from altimetry and the TG observations, with an average root mean square deviation (RMSD) as low as 10 cm and a strong correlation. The study’s findings revealed that the Copernicus data products could be used to monitor sea-level variability and trends in the IMC accurately. The 25-year time series data from SSHA demonstrated that the sea-level trend in the IMC is higher than the global trend.

Validation of sea surface heights from satellite altimetry along the Indian coast

2021 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanian ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Atmospheric Correction ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Indian Coast ◽  
Vertical Land Motion ◽  
And Performance

<p>Satellite altimetry provides measurements of sea surface height of centimeter-level accuracy over open oceans. However, its accuracy reduces when approaching the coastal areas and over land regions. Despite this downside, altimetric measurements are still applied successfully in these areas through altimeter retracking processes. This study aims to calibrate and validate retracted sea level data of Envisat, ERS-2, Topex/Poseidon, Jason-1, 2, SARAL/AltiKa, Cryosat-2 altimetric missions near the Indian coastline. We assessed the reliability, quality, and performance of these missions by comparing eight tide gauge (TG) stations along the Indian coast. These are Okha, Mumbai, Karwar, and Cochin stations in the Arabian Sea, and Nagapattinam, Chennai, Visakhapatnam, and Paradip in the Bay of Bengal. To compare the satellite altimetry and TG sea level time series, both datasets are transformed to the same reference datum. Before the calculation of the bias between the altimetry and TG sea level time series, TG data are corrected for Inverted Barometer (IB) and Dynamic Atmospheric Correction (DAC). Since there are no prior VLM measurements in our study area, VLM is calculated from TG records using the same procedure as in the Technical Report NOS organization CO-OPS 065. </p><p>Keywords— Tide gauge, Sea level, North Indian ocean, satellite altimetry, Vertical land motion</p>

Performance Assessment of GNSS-R Polarimetric Observations for Sea Level Monitoring

2021 ◽  
Author(s):  
Mahmoud Rajabi ◽  
Mstafa Hoseini ◽  
Hossein Nahavandchi ◽  
Maximilian Semmling ◽  
Markus Ramatschi ◽  
...  
Keyword(s):  
Time Series ◽  
Circular Polarization ◽  
Sea Level ◽  
Tide Gauge ◽  
Satellite System ◽  
Sea Surface ◽  
Analysis Tool ◽  
Lower Accuracy ◽  
Coastal Sea ◽  
Global Navigation Satellite

<p>Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge.  </p><p>We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15 minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.</p><p>Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.</p>

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

Regional Mean Sea Surface Model over the Eastern Mediterranean Sea

2020 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanium ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sea Level ◽  
Tide Gauge ◽  
Eastern Mediterranean ◽  
Sea Surface ◽  
Surface Model ◽  
Eastern Mediterranean Sea ◽  
Mean Sea Level ◽  
Mean Sea Surface

&lt;p&gt;The Mean Sea Level is not an equipotential surface because it is subject to several variations, e.g., the tides, currents, winds, etc. Mean Sea Level can be measured either by tide gauges near to coastlines relative to local datum or by satellite altimeter above the reference ellipsoid. From this observable quantity, one can derive a non-observable quantity at which the potential is constant called geoid and differs from mean sea surface by amount of &amp;#177;1 m. This separation is called Sea Surface Topography. In this research, the data of nine altimetric Exact Repeat Missions (Envisat, ERS_1 of 35 days (phase C and G), ERS_2, GFO, Jason_1, Jason_2, Jason_3, Topex/Poseidon and SARAL) were used for computing the regional mean sea surface model over the eastern Mediterranean Sea. The data of all missions together span approximately 25 years from September -1992 to January-2017 and referenced to Topex ellipsoid.&amp;#160; Which is later transformed to WGS84 ellipsoid, as it is chosen to be a unified datum in this study. Prior to computing the altimetric MSS,&amp;#160; altimetric sea surface height measurements were validated&amp;#160; by comparing&amp;#160; time series of altimetric-MSL with mean sea level time series calculated from three in-situ tide gauge measurements.&amp;#160; The sea surface heights values of the derived MSS model is between 15.6 and 26.7 m. And the linear trend slope is between -3.02 to 6.53 mm/year.&lt;/p&gt;&lt;p&gt;Keywords: Mean Sea Level, Satellite Altimetry, Tide Gauge, Exact Repeat Missions&lt;/p&gt;

scholarly journals INVESTIGATIONS ON VERTICAL LAND MOVEMENTS ALONG THE NORTH SEA AND BALTIC SEA COAST IN GERMANY WITH PS INTERFEROMETRY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1945-1949
Author(s):  
A. Riedel ◽  
B. Riedel ◽  
D. Tengen ◽  
M. Gerke
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
North Sea ◽  
Time Series Data ◽  
Coastal Areas ◽  
Series Data ◽  
Homogeneous Distribution ◽  
Northern Germany ◽  
Movement Model ◽  
The Absolute

<p><strong>Abstract.</strong> The project ‘Determinations on the absolute sea-level rise on the German North Sea and Baltic Sea coasts’, funded by the Federal Ministry of Education and Research (BMBF) , has the overall goal to estimate the absolute sea level change in those coastal areas. A major issue associated with detecting absolute sea level changes is the relative character of tidal records. To calibrate the tidal records, a spatial vertical land movement model for northern Germany has been set up. To this end we combined a network from German Height Reference Systems (Deutsches Haupthöhennetz, DHHN 95 and DHHN 2016), reprocessed data from 180 permanent GNSS stations and results from Persistent Scatterer (PS) Interferometry.</p><p>PS processing covers an approximately 50&amp;thinsp;km wide strip along the 1200&amp;thinsp;km long German coast. We processed two tracks from Sentinel-1A and -1B from October 2014 to September 2018 and generated a combined spatial solution for the estimation of vertical land movement. In general, vertical velocities from PS Interferometry range between &amp;plusmn;2&amp;thinsp;mm/a and show a homogeneous distribution for coastal areas. Therefore we consider them as stable. We observe subsidence in the area around Groningen and Emden through hydrocarbon extraction. In Wilhelmshaven and Etzel subsidence associated with cavern storage is visible.</p><p>Processed GNSS data and PSI results overlap in time from 2014 to 2016. The integration of the spatial multi-temporal PS results with point-wise GNSS time series data are required, as they form the main input data for the further development of our vertical displacement model of northern Germany.</p>

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