scholarly journals Changes in extreme high water levels based on a quasi-global tide-gauge data set

2010 ◽  
Vol 115 (C10) ◽  
Author(s):  
Melisa Menéndez ◽  
Philip L. Woodworth
Keyword(s):  
Tide Gauge ◽  
High Water ◽  
Water Levels ◽  
Tide Gauge Data ◽  
Data Set ◽  
Gauge Data

scholarly journals A 10-Year Comparison of Water Levels Measured with a Geodetic GPS Receiver versus a Conventional Tide Gauge

2017 ◽  
Vol 34 (2) ◽  
pp. 295-307 ◽  
Author(s):  
Kristine M. Larson ◽  
Richard D. Ray ◽  
Simon D. P. Williams
Keyword(s):  
Tide Gauge ◽  
Signal Propagation ◽  
Propagation Delay ◽  
Terrestrial Reference Frame ◽  
Water Levels ◽  
Sea Surface ◽  
Ocean Tide ◽  
Gps Receiver ◽  
Tide Gauge Data ◽  
Gauge Data

AbstractA standard geodetic GPS receiver and a conventional Aquatrak tide gauge, collocated at Friday Harbor, Washington, are used to assess the quality of 10 years of water levels estimated from GPS sea surface reflections. The GPS results are improved by accounting for (tidal) motion of the reflecting sea surface and for signal propagation delay by the troposphere. The RMS error of individual GPS water level estimates is about 12 cm. Lower water levels are measured slightly more accurately than higher water levels. Forming daily mean sea levels reduces the RMS difference with the tide gauge data to approximately 2 cm. For monthly means, the RMS difference is 1.3 cm. The GPS elevations, of course, can be automatically placed into a well-defined terrestrial reference frame. Ocean tide coefficients, determined from both the GPS and tide gauge data, are in good agreement, with absolute differences below 1 cm for all constituents save K1 and S1. The latter constituent is especially anomalous, probably owing to daily temperature-induced errors in the Aquatrak tide gauge.

scholarly journals First evaluation of MyOcean altimetric data in the Arctic Ocean

2012 ◽  
Vol 9 (1) ◽  
pp. 291-314 ◽  
Author(s):  
Y. Cheng ◽  
O. B. Andersen ◽  
P. Knudsen
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
The Arctic ◽  
Data Sets ◽  
Tide Gauge Data ◽  
Data Set ◽  
Altimetric Data ◽  
Gauge Data ◽  
Sea Level Trend ◽  
Annual Signal

Abstract. The MyOcean V2 preliminary (V2p) data set of weekly gridded sea level anomaly (SLA) maps from 1993 to 2009 over the Arctic region is evaluated against existing altimetric data sets and tide gauge data. Compared with DUACS V3.0.0 (Data Unification and Altimeter Combination System) data set, MyOcean V2p data set improves spatial coverage and quality as well as maximum temporal correlation coefficient between altimetry and tide gauge data. The estimated amplitude of sea level annual signal and linear sea level trend from MyOcean data set are evaluated against altimetry from DUACS and RADS (Radar Altimeter Database System), the SODA (Simple Ocean Data Assimilation) ocean reanalysis and tide gauge data sets from PSMSL (Permanent Service for Mean Sea Level). The results show that the MyOcean data set fits in-situ measurements better than DUACS data set with respect to amplitude of annual signal and linear sea level trend. However, the MyOcean V2p data set exhibits an unrealistic large linear sea level trend compared with that from other data sources.

scholarly journals A newly reconciled data set for identifying sea level rise and variability in Dublin Bay

2021 ◽  
Author(s):  
Amin Shoari Nejad ◽  
Andrew C. Parnell ◽  
Alice Greene ◽  
Peter Thorne ◽  
Brian P. Kelleher ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Tide Gauge ◽  
High Water ◽  
Data Set ◽  
Quality Checks ◽  
Bayesian Linear Regression ◽  
The Mean ◽  
Water Values ◽  
Water Level Measurements

Abstract. We provide an updated sea level dataset for Dublin for the period 1938 to 2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the Mean Low Water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of 1.08 mm/yr sea level rise at Dublin Port between 1953–2016 (95 % CI from 0.62 to 1.55 mm/yr), and a rate of 6.48 mm/yr between 1997–2016 (95 % CI 4.22 to 8.80 mm/yr). Overall sea level rise is in line with expected trends but large multidecadal varaibility has led to higher rates of rise in recent years.

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