MétaCan
Menu
← Back to cohort
Record W4393492036 · doi:10.5281/zenodo.5113735

Nodal tide components for the PSMSL annual and monthly dataset (preliminary)

2021· dataset· en· W4393492036 on OpenAlexaboutno aff
Jelmer Veenstra, Fedor Baart, Martin Verlaan, Willem Stolte

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsNODALEnvironmental scienceClimatologyOceanographyGeologyBiology

Abstract

fetched live from OpenAlex

Nodal tide components for the PSMSL annual and monthly dataset (preliminary) This preliminary dataset contains the tidal component for the mean sea level for the tide gauges in the Permanent Service for Mean Sealevel dataset. These files can be used to subtract the long term tide of the mean sea level. Authors and contact This dataset is created by Jelmer Veenstra, Martin Verlaan, Fedor Baart, and Willem Stolte. You can contact Jelmer Veenstra or Fedor Baart for more information. Files You can find the following files in this dataset. monthlymean_gtsm_psmsl-{id}.csv: monthly tidal corrections for mean sea level. yearlymean_gtsm_psmsl-{id}.csv: annual tidal corrections for the mean sea level. yearlymeanOLS_gtsm_psmsl-{id}.csv: annual tidal corrections based on a harmonic analysis through the reanalysis data (see details below). df_OLSmodelstats_year.csv: an overview of the phase and amplitude of the harmonic analysis of all the stations, also includes the equilibrium tidal amplitude. All files are stored in .csv files, using a , as field separator and . as decimal separator. Time is stored as YYYY for the annual series and as YYYY-MM for the monthly series. The file df_OLSmodelstats_year.csv also contains the list of all stations for which we provide information. More information on these stations can be found at [1]. Data specific information The file monthlymean_gtsm_psmsl-{id}.csv contains the following columns: - time [year-month] : year-month for which the mean tidal level is determined - sea_surface_height_due_to_tide [m]: mean sea surface level due to tidal waves [m] The file yearlymean_gtsm_psmsl-{id}.csv contains the following columns: - time [year]: year for which the mean tidal level is determined - sea_surface_height_due_to_tide [m]: mean sea surface level due to tidal waves [m] The file yearlymeanOLS_gtsm_psmsl-{id}.csv contains the following columns: - time [year]: year for which the mean tidal level is determined - sea_surface_height_due_to_tide_fitted [m]: mean sea surface level due to tidal waves, harmonic fit [m] The file df_OLSmodelstats_year.csv contains the following columns: longitude [degrees east]: longitude of the station latitude [degrees north]: latitude of the station station_name: station name nodal tide U [m]: linearized fit of the nodal cycle (A/U/cos term) (relative to 1970) [m] nodal tide V [m]: linearized fit of the nodal cycle (B/V/sin term) (relative to 1970) [m] nodal amplitude [m]: amplitude of the fitted nodal cycle, sqrt(A2 + B2) [m] nodal phase [radians since 1970-01-01]: phase of the fitted nodal cycle arctan2(B, A) [rad, epoch 1970] mean sea surface height of nodal fit [m]: mean tidal level over the fitted time window [m] nodal amplitude fitted with nodal epoch [m]: amplitude of the fitted nodal tide with epoch at the start of the phase of the nodal tide. Note that the amplitude here can be negative. [m] nodal amplitude of equilibrium tide [m]: equilibrium amplitude of the nodal tide [m] Methods To generate this dataset we have run a tidal model (GTSM v4.0) for 19 years. This multi-decadal reanalysis of tides allows separating the tidal component from other sea-level fluctuations. The purpose of this computation is to correct yearly mean and monthly mean tide gauge records for this tide constituent. See [1, 2] for a discussion on this topic. The equilibrium ampltiude is computed as: abs(0.69 * 20 * (3 * sin(deg2rad(lat))**2 - 1)) / 1000 This assumes an all water, elastic earth, and no self attraction. In this simulation, all tidal forcings (~400) are active. Thus the estimates also contain indirect non-linear effects, such as the nodal modulation on the amplitude of M2 interacting with itself. This allows the computation to deviate from the equilibrium tide. The tidal potential, corrections for solid earth tide (through Love numbers), and self attraction and loading are included. Using this dataset we fit, using an ordinary least squares approach, the nodal tidal amplitude, and phase. Details of this analysis can be found in the corresponding notebook [3]. The results have not been validated or published, so please use this dataset with caution. See the details in the section preliminary results. Preliminary results These are preliminary results, intended for evaluation with other scientists. Make sure you take into account the following: These results are based on GTSM 4.0, we expect to create an updated version based on 4.1. Version 4.1 should have a better internal tide model, which should improve reanalysis results in general. The following regions are not reliable: Black Sea, due to a limited topological relation with the rest of the grid. These stations are excluded. Regions in inlets have not been validated The reanalysis amplitude is lower than expected from equilibrium tide (about a factor 2 lower). Research into the cause of this is pending (love numbers, self attraction can be considered). The phase of the nodal tide is not yet validated. Some stations have deviating mean sea levels. A few examples: psmsl-173: station in the river mound the St. Lawrence River in Quebec. Not enough resolution psmsl-1067: Anchorage in Alaska, in an inlet psmsl-495: inlet psmsl-1908: bathymetry/bridge psmsl-2285: bathymetry resolution/bridge General remark: resolution in narrow tidal inlets and stations up rivers are not accurate. In an updated version we might be able to use cells just outside the inlet. Tide in narrow inlets can be quite different from outside. Sharing and Access information This dataset is available under a CC-BY-SA license [3]. See the link below for details. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Please make sure you refer to the preliminary status of this dataset if you use it. [0] https://www.psmsl.org [1] https://doi.org/10.2112/JCOASTRES-D-11-00169.1 [2] https://doi.org/10.2112/JCOASTRES-D-11A-00023.1 [3] http://creativecommons.org/licenses/by-sa/4.0/

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.183
Threshold uncertainty score0.613

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1830.182

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.221
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicOceanographic and Atmospheric Processes→French-language works237,207→