Atmospheric correction of satellite altimetry observations and sea-level variability in the NE Atlantic

2012 ◽  
Vol 50 (8) ◽  
pp. 1077-1084 ◽  
Author(s):  
Susana M. Barbosa
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Atmospheric Correction ◽  
Ne Atlantic ◽  
Sea Level Variability ◽  
And Sea Level

scholarly journals Studying Sea Level Variability in Indonesia Sea Based on Satellite Altimetry

2020 ◽  
Vol 618 ◽  
pp. 012040
Author(s):  
Dina A Sarsito ◽  
Muhammad Syahrullah ◽  
Dudy D Wijaya ◽  
Dhota Pradipta ◽  
Heri Andreas
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Sea Level Variability

Impact of Satellite Altimetry on Simulations of Sea Level Variability by an Indian Ocean Model

2001 ◽  
Vol 24 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. K. Singh ◽  
Sujit Basu ◽  
Raj Kumar ◽  
Vijay K. Agarwal
Keyword(s):  
Indian Ocean ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Ocean Model ◽  
Sea Level Variability

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>

scholarly journals Tides and sea level variability decomposition in the Port of Santos Waterway

2019 ◽  
Vol 67 ◽  
Author(s):  
Carlos A.F. Schettini ◽  
Eliane C. Truccolo ◽  
José A.D. Mattos ◽  
Daniel C.D.A. Benevides
Keyword(s):  
Sea Level ◽  
Sea Level Variability ◽  
And Sea Level

scholarly journals Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 67-82 ◽  
Author(s):  
M. Ablain ◽  
A. Cazenave ◽  
G. Larnicol ◽  
M. Balmaseda ◽  
P. Cipollini ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Climate Model ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Processing System ◽  
Change Initiative ◽  
Global Mean

Abstract. Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs.

scholarly journals Non-tidal aliasing in seasonal sea-level variability and annual Rossby waves as observed by satellite altimetry

1997 ◽  
Vol 15 (11) ◽  
pp. 1478-1488 ◽  
Author(s):  
G. Chen ◽  
R. Ezraty
Keyword(s):  
Numerical Simulations ◽  
Systematic Error ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Rossby Waves ◽  
Altimeter Data ◽  
Ocean Tides ◽  
Satellite Altimeter ◽  
Sea Level Variability ◽  
Satellite Altimeter Data

Abstract. It is becoming well known that aliasing associated with ocean tides could be a major source of systematic error in altimeter sea-level measurements, due to asynoptic sampling and imperfect tide modelling. However, it has been shown that signals of non-tidal origin may also contribute significantly to the observed aliasing. In this paper, numerical simulations are performed to demonstrate the full aliasing potential associated with altimeter observations of seasonal sea-level variability and annual Rossby waves. Our results indicate that ignorance of non-tidal aliasing may lead to the possibility of underestimating the total aliasing and misinterpreting or overlooking existing geophysical phenomena. Therefore, it is argued that an entire aliasing picture should be kept in mind when satellite altimeter data are analysed.

scholarly journals Deep learning of dynamic sea-level variability to investigate the relationship with the floods in Gothenburg

2021 ◽  
Author(s):  
Omid Memarian Sorkhabi
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Surface ◽  
Sea Level Variability ◽  
Annual Increase ◽  
The Relationship ◽  
Dynamic Sea Level ◽  
And Sea Level

Abstract It is important to study the relationship between floods and sea-level rise due to climate change. In this research, dynamic sea-level variability with deep learning has been investigated. In this research sea surface temperature (SST) from MODIS, wind speed, precipitation and sea-level rise from satellite altimetry investigated for dynamic sea-level variability. An annual increase of 0.1 ° C SST is observed around the Gutenberg coast. Also in the middle of the North Sea, an annual increase of about 0.2 ° C is evident. The annual sea surface height (SSH) trend is 3 mm on the Gothenburg coast. We have a strong positive spatial correlation of SST and SSH near the Gothenburg coast. In the next step dynamic sea-level variability is predicted with long short time memory. Root mean square error of wind speed, precipitation, and mean sea-level forecasts are 0.84 m/s, 48 mm and 2.4 mm. The annual trends resulting from 5-year periods, show a significant increase from 28 mm to 46 mm per year in the last 5 year periods. The rate of increase has doubled. The wavelet can be useful for detecting dynamic sea-level variability.

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