Ocean Model Validation

AMM7 — Analysis Overview

Sat, 10 Oct 2026 00:00:00 +0000

AMM7 Analysis Overview

This page summarises the analyses available for the AMM7 area, organised by type. Use the table of contents to jump to a specific section.

Tidal Analysis

TPXO9 — 2019-2022

Constituent	Amp err (m)	Phase err (°)
M2	0.121	14.5
S2	0.038	14.3
K1	0.023	27.1
O1	0.013	18.0

2019-2022

Horizontal Surface Validation

📊 View Horizontal Surface Validation rankings for AMM7 →

NS — Analysis Overview

Sat, 10 Oct 2026 00:00:00 +0000

NS Analysis Overview

This page summarises the analyses available for the NS area, organised by type. Use the table of contents to jump to a specific section.

Tidal Analysis

No tidal analysis has been performed for this area yet.

Horizontal Surface Validation

CMEMS — 2016-2023

Variable	Dataset	RMSE	Bias	Corr
SALT	CCI-SSS	2.233	-0.101	0.92
TEMP	CCI-SST	0.854	+0.402	0.92
TEMP	OSTIA	0.860	+0.197	0.90

2016-2023

Horizontal Bottom Layer Validation

CMEMS — 2016-2023

NSe — Analysis Overview

Sat, 10 Oct 2026 00:00:00 +0000

NSe Analysis Overview

This page summarises the analyses available for the NSe area, organised by type. Use the table of contents to jump to a specific section.

Tidal Analysis

📊 View Tidal Analysis rankings for NSe →

CMEMS/tidal — 2011

Constituent	Amp err (m)	Phase err (°)
M2	0.432	22.1
S2	0.146	23.8
K1	0.015	36.9
O1	0.028	24.2

2011

CMIP6/tidal — 2011-2011

Tidal Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Tidal Analysis Guide

Overview

tidal-analysis extracts tidal harmonic constituents from model output and validates them against satellite tidal atlases (FES2014, TPXO9) or tide gauge records from the GESLA database.

The harmonic analysis uses utide to fit sinusoidal components at each grid point or station location. Model amplitudes and phases are then compared to the reference atlas or gauge data.

Analysis modes

Mode	Flag	Description
`gesla`	`--mode gesla`	Harmonic analysis at GESLA tide gauge locations (fast; recommended; config default)
`spatial`	`--mode spatial`	Harmonic analysis on the full model grid, compared to a tidal atlas
`both`	`--mode both`	Run spatial and GESLA together
`stations`	`--mode stations`	Named stations listed under `stations:` in config

Model source: `--source` / `--experiment`

The model file to read no longer lives in the per-run config. It comes from config/sources.yaml, keyed by --source, with --experiment naming the run folder under that source:

Gridded 2D Validation

Mon, 01 Jan 0001 00:00:00 +0000

Gridded 2D Validation Guide

Overview

gridded-2d-validation compares any horizontal 2-D model field against a gridded observation product. Three layer modes are supported:

Layer	Description	Typical use
`surface`	2-D field or top of 3-D output	SST, SSS, SSH
`bottom`	Deepest wet cell of 3-D output	Bottom temperature / salinity
`<depth_m>`	Horizontal slice at a fixed depth (m)	Depth-layer climatology

Multiple layers can be run in a single invocation.

There are four shipped configs: gridded_2d_validation.yaml is the generic one, with any combination of layers via --layers. gridded_2d_validation_surface.yaml, _bottom.yaml and _depths.yaml are the dedicated single-purpose variants used for NSe.

Gridded 3D Validation

Mon, 01 Jan 0001 00:00:00 +0000

Gridded 3D Validation Guide

Overview

gridded-3d-validation validates model temperature and salinity (or any 3-D variable) against a gridded observation product — WOA23 climatology, CMIP6 ensembles, Copernicus reanalysis, or any dataset that provides values at every horizontal grid point and a set of depth levels.

The observations are regridded onto the model grid and compared cell-by-cell, enabling:

Spatial statistics maps (RMSE, bias, MAE, correlation per grid point)
Time-mean comparison maps at selected depth levels
Monthly statistics bar charts
Statistics text file (overall + monthly breakdown)

When to use this tool vs profile validation: Use gridded-3d-validation for climatologies or reanalyses that cover the full model domain at all depths. Use argo-profiles, cruise-ctd-profiles, or fixed-platform for sparse in-situ observations scattered in space and time.

Profile Validation

Mon, 01 Jan 0001 00:00:00 +0000

Profile Validation Guide

Overview

Five scripts share the same profile validation workflow:

Command	Observation source
`argo-profiles`	Argo floats (Ifremer ERDDAP)
`glodap-profiles`	GLODAP v2.2023 bottle data
`wod-profiles`	World Ocean Database (NOAA ERDDAP)
`cruise-ctd-profiles`	ICES / EMODnet cruise CTD casts
`ices-profiles`	ICES/ECOVAL profiles (local Feather files)
`fixed-platform`	EMODnet fixed-platform (mooring/buoy) profiles

All six follow the same config structure, output layout, and command-line flags.

Config structure

Annotated example configs: config/argo_profiles.yaml · config/glodap_profiles.yaml · config/cruise_ctd_profiles.yaml · config/wod_profiles.yaml · config/ices_profiles.yaml · config/fixed_platform.yaml

MLE Comparison

Mon, 01 Jan 0001 00:00:00 +0000

MLE Cross-Experiment Comparison Guide

Overview

Standard validation metrics (RMSE, bias, correlation) compare each experiment against observations in isolation. When you have three or more experiments that share the same observation dataset — sensitivity runs, ensemble members, or physics variants — a scalar metric gives no information about the joint structure of the errors.

MLE comparison in reduced-rank (SVD) space addresses this. The joint residual matrix across all experiments is decomposed with SVD, and each experiment is ranked by its Gaussian log-likelihood in the leading modes. This is the same “optimal fingerprinting” framework used in climate attribution (Allen & Tett 1999): the leading SVD modes capture the dominant joint variance pattern, and the log-likelihood in that low-dimensional space is a principled, multi-dimensional distance from perfect.

Run Validation

Mon, 01 Jan 0001 00:00:00 +0000

run-analyses Guide

This guide used to document a run-validation command. That command no longer exists — it was replaced by run-analyses (cli/run_analyses.py), which has a different config layout and CLI. This page now documents the current tool; the title and URL are unchanged.

Overview

run-analyses is the master orchestrator. It calls each step’s own CLI entry point as a subprocess, reusing one YAML config per step (config/run_analyses.yaml maps step names to those per-step config files).

Scenario Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Scenario Analysis Guide

Overview

The scenario workflow has three stages:

CMIP6 forcing  ──► bc correct ──► bias-corrected forcing
                                        │
                               ocean model (future run)
                                        │
                         ┌─────────────┴─────────────┐
                   single-scenario              multi-scenario
                  (trends, changes)       (SSP comparison)

Bias-correct CMIP6 atmospheric forcing against ERA5 (or equivalent) so its statistical properties match the observational reference over the calibration period.
Run the ocean model with the corrected forcing for each SSP scenario.
Analyse the ocean output — trend maps, time series, regional means — for one scenario at a time or across multiple scenarios simultaneously.

Step 1 — Bias correction

Note: bc correct, bc diagnostics, and bc check have moved to ocean-prep. Install and run them from the ocean-prep repo. Config and guides are at ocean-prep/config/bc_correct_example.yaml and ocean-prep/guides/.

Push To Remote

Mon, 01 Jan 0001 00:00:00 +0000

push-to-remote Guide

Overview

push-to-remote is the other half of the post-simulation handoff, alongside update-from-remote. Run it on the simulation server (e.g. an HPC login node) right after a batch of analyses finishes there, to send the results to the machine that actually generates and deploys the Hugo site (the “analysis server”, e.g. bb-server1).

It exists because the two machines are not symmetrically reachable: the simulation server typically has outgoing network access only, and the analysis server has no route back to it at all. Any transfer must be initiated from the simulation server, pushing out — never initiated from the analysis server trying to pull in. push-to-remote wraps manage-analyses sync ... --put, which is exactly that: an rsync, run from here, pushing local files out.

River Flow and Nutrient Load Projection

Mon, 01 Jan 0001 00:00:00 +0000

River Flow and Nutrient Load Projection Guide

river-projection (stage 1, below) lives in ocean-prep, alongside bc-correct/bc-diagnostics — install and run it from the ocean-prep repo (cli/river_projection.py), with its config at ocean-prep/config/river_projection_example.yaml. river-diagnostics (stage 2) stays in ocean-post. Confirmed 2026-10-07; this page previously referenced a config/river_projection.yaml in ocean-post that was never there, and the real tool and config were found in ocean-prep instead. No project-specific copy of the config exists yet in this checkout — the only one found is ocean-prep’s own example.

Update From Remote

Mon, 01 Jan 0001 00:00:00 +0000

update-from-remote Guide

Overview

update-from-remote is a one-command post-simulation pipeline. After a model run finishes on a remote machine and the analysis scripts have written their outputs there, this command:

sync — rsyncs the remote analyses/ tree to the local machine
merge — flushes staging YAML records into simulation_list.db
scan — registers any experiments found on the filesystem but not yet in the DB
report — regenerates all Hugo pages via ocean-reporting
deploy — builds and pushes the Hugo site to gh-pages (only with --deploy)

Both REMOTE and LOCAL_ANALYSES are required positional arguments. There are no defaults, which prevents accidentally syncing into the wrong directory.

Manage Analyses

Mon, 01 Jan 0001 00:00:00 +0000

manage-analyses Guide

Overview

manage-analyses is the housekeeping tool for the analyses/ output tree. All subcommands default to a dry run — add --apply to actually execute.

Subcommand	Purpose
`clean`	Remove files at the wrong directory depth (non-canonical layout)
`wipe`	Remove all regeneratable outputs so scripts can be re-run from scratch
`sync`	Rsync the remote analyses tree to a local directory
`import-stats`	Import existing CSV statistics files into `statistics.db`

clean

Removes files that do not conform to the canonical layout — for example, plots written directly under validations/<experiment>/plots/ instead of the correct plots/<domain>/<model>/<period>/<type>/ path. Canonical files are left untouched.

Simulation List

Mon, 01 Jan 0001 00:00:00 +0000

Simulation List Guide

Overview

simulation-list manages the experiment registry — a SQLite database that tracks every analysis run, its parameters, and its status. The registry is used by ocean-reporting to populate the Hugo site and by update-from-remote after each sync.

The registry uses a two-step write pattern to avoid race conditions when multiple analysis scripts run in parallel:

Each script writes a small staging YAML file under analyses/staging/.
simulation-list merge atomically flushes all staging files into the DB.

Subcommands

# Show DB contents and pending staging files
simulation-list status [--base-dir DIR]

# Flush staging → DB
simulation-list merge  [--base-dir DIR]

# Export DB to a human-readable YAML snapshot
simulation-list to-yaml out.yaml [--base-dir DIR]

# Migrate a legacy YAML registry into the DB
simulation-list to-sqlite old_registry.yaml [--base-dir DIR]

--base-dir defaults to ./analyses.

Statistics Database

Mon, 01 Jan 0001 00:00:00 +0000

Statistics Database Guide

Overview

Every analysis script writes its results to human-readable TXT/CSV files inside the analyses/ tree and records the same numbers in a SQLite database at analyses/statistics.db. The database makes it easy to compare experiments, track how metrics change over time, and query across areas and variables without parsing text files.

The database is managed by lib/stats_db.py.

Tables

Table	Purpose
`statistics`	Core metrics from 2-D gridded, 3-D, and profile analyses
`monthly_statistics`	Month-by-month breakdown (child of `statistics`)
`tidal_statistics`	Tidal constituent summary aggregated across stations
`mle_rankings`	MLE cross-experiment ranking rows
`bc_statistics`	Bias-correction calibration and future-period metrics

statistics

One row per (area, experiment, variable, period, layer, model, obs_source, depth_label) combination. A UNIQUE constraint on those eight columns means re-running an analysis upserts the row rather than duplicating it.

Reporting

Mon, 01 Jan 0001 00:00:00 +0000

Reporting Guide

Overview

ocean-reporting reads the analyses/ directory tree produced by the validation scripts and generates two types of output:

Hugo Markdown pages — a structured static website showing plots, statistics tables, and summary text for every area and experiment.
PDF report — a self-contained document with the same content, built with reportlab.

Both outputs can be produced in a single run by combining --hugo and --pdf.

Prerequisite: the analyses/ tree

All validation scripts write to a canonical tree under output.analyses_dir (resolved from OCEANICU_ANALYSES_FOLDER — see the tidal/gridded guides). The exact layout is defined in lib/layout.py:

Hugo Deployment

Mon, 01 Jan 0001 00:00:00 +0000

Hugo Deployment Guide

Overview

Reporting explains ocean-reporting’s options and the analyses/ tree it reads. This guide is about what happens after that: getting the generated Hugo content actually live, and the real mistakes that have already happened doing it.

There are two entry points to the exact same underlying code — pick whichever fits the situation:

update-from-remote — sync a remote cluster’s analyses/ down, merge/scan into the DB, then report + deploy, all in one command. Right when there’s fresh data to pull in first.
Manual two-step (used directly against data that’s already local, e.g. on the reporting host itself): the calling project’s own regenerate_hugo.py --apply, then this repo’s own deploy_ghpages.py --apply. Both relay over ssh to the reporting host automatically if run from elsewhere. regenerate_hugo.py isn’t part of this repo — it lives in the site’s own repo (e.g. oceanicu_3d/regenerate_hugo.py), and its own docs/web-regeneration.md has the full detail for that side.

Either path ends up calling cli.reporting:main (as ocean-reporting in one case, python3 -m cli.reporting in the other) and this repo’s own deploy_ghpages.py directly. A bug or fix in lib/reporting.py affects both equally.

All Areas Rankings

Sat, 10 Oct 2026 00:00:00 +0000

All Areas — Experiment Rankings

Composite-score ranking pooling experiments from every area together, one chart per validation category, labelled “Area: Experiment”. Only categories with experiments from two or more areas or runs end up here with a real comparison; see each area’s own rankings page for within-area comparisons.

Tidal Analysis

Horizontal Surface Validation

Horizontal Bottom Layer Validation

AMM7

Sat, 10 Oct 2026 00:00:00 +0000

AMM7 modeling area — validation results and experiment overview.

AMM7 - Experiment Baseline

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: AMM7 Experiment: Baseline Validation Type: Horizontal Validation Variables: SALT_SURFACE, TEMP_SURFACE

Overview

Metric	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST
RMSE	1.0030	0.9160	0.9018
Bias	0.2756	0.3920	0.3841
Corr	0.6019	0.9651	0.9649
N points	195,106,513	151,821,276	197,655,640

Annual Statistics Trends

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

AMM7 - Experiment CMEMS

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: AMM7 Experiment: CMEMS Validation Type: Horizontal Validation Variables: SALT_SURFACE, TEMP_SURFACE

Overview

Metric	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST
RMSE	1.0607	1.1413	1.0988
Bias	-0.0323	0.6880	0.6777
Corr	0.8367	0.9610	0.9630
N points	195,106,513	151,821,276	197,655,640

Annual Statistics Trends

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

AMM7 - Experiment CMEMS_prof

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: AMM7 Experiment: CMEMS_prof Validation Type: Horizontal Validation Variables: SALT_SURFACE, TEMP_SURFACE

Overview

Metric	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST
RMSE	0.8866	1.0272	1.0031
Bias	0.0802	0.4192	0.4755
Corr	0.8318	0.9479	0.9550
N points	170,735,828	132,856,606	172,965,608

Annual Statistics Trends

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

AMM7 - Experiment NetSW_LW

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: AMM7 Experiment: NetSW_LW Validation Type: Horizontal Validation Variables: SALT_SURFACE, TEMP_SURFACE

Overview

Metric	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST
RMSE	1.0077	0.9415	0.9283
Bias	0.2858	0.4208	0.4006
Corr	0.6003	0.9627	0.9621
N points	195,106,513	151,821,276	197,655,640

Annual Statistics Trends

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

AMM7 - Experiment ObsKd

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: AMM7 Experiment: ObsKd Validation Type: Horizontal Validation Variables: SALT_SURFACE, TEMP_SURFACE

Overview

Metric	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST
RMSE	1.0175	0.9880	0.9801
Bias	0.3028	0.5735	0.5491
Corr	0.5923	0.9654	0.9639
N points	195,106,513	151,821,276	197,655,640

Annual Statistics Trends

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

AMM7 - TPXO9 - Tidal Analysis

Sat, 10 Oct 2026 00:00:00 +0000

Harmonic analysis of sea surface elevation compared with satellite products and tide gauge observations.

Analysis Summary

Analyzed Constituents: K1, K2, M2, N2, O1, P1, Q1, S2
Analysis Date: 2026-10-10 Satellite Product: FES2014/TPXO9

Plots

Tide Gauge Station Map

GESLA tide gauge station locations

K₁ (lunar-solar diurnal) — amplitude/phase comparison with GESLA tide gauges

O₁ (principal lunar diurnal) — amplitude/phase comparison with GESLA tide gauges

AMM7 Rankings

Sat, 10 Oct 2026 00:00:00 +0000

AMM7 Experiment Rankings

Composite-score ranking of every experiment validated for AMM7, one chart per validation category. See the AMM7 overview for the full per-experiment statistics these are built from. See All Areas to compare across areas.

Tidal Analysis

Only one experiment validated so far for this category — ranking needs at least two.

Horizontal Surface Validation

Gridded 3D Validation

MLE Comparison — AMM7

Sat, 10 Oct 2026 00:00:00 +0000

MLE Cross-Experiment Comparison — AMM7

SVD-reduced maximum-likelihood ranking of model experiments against a common observation footprint.

Ranking

experiment	log_likelihood	rank	delta_log_L	aic	explained_variance_captured
Baseline	-0.49748439587068466	1	0.0	2.9949687917413694	0.9972383853443495
NetSW_LW	-0.5007573186360548	2	-0.003272922765370112	3.0015146372721095	0.9972383853443495
ObsKd	-0.5017582854932605	3	-0.004273889622575855	3.003516570986521	0.9972383853443495

Diagnostic Plots

MLE Comparison — NS

Sat, 10 Oct 2026 00:00:00 +0000

MLE Cross-Experiment Comparison — NS

SVD-reduced maximum-likelihood ranking of model experiments against a common observation footprint.

Ranking

experiment	log_likelihood	rank	delta_log_L	aic	explained_variance_captured
ObsKd	-0.8997876421014234	1	0.0	3.799575284202847	0.9923041270768755
Adaptive_1	-1.0049792344553714	2	-0.10519159235394804	4.009958468910742	0.9923041270768755
Adaptive_3	-1.0061267306983028	3	-0.10633908859687946	4.012253461396606	0.9923041270768755
Adaptive_2	-1.0103562758376738	4	-0.11056863373625048	4.020712551675348	0.9923041270768755
SSRD_STRD	-1.0108718935484369	5	-0.1110842514470135	4.021743787096874	0.9923041270768755
Baseline	-1.011708243472961	6	-0.11192060137153759	4.023416486945922	0.9923041270768755
SSR_STR	-1.0188474815428445	7	-0.11905983944142118	4.0376949630856895	0.9923041270768755
CMEMS	-1.0373224983429865	8	-0.13753485624156314	4.074644996685973	0.9923041270768755

Diagnostic Plots

NS

Sat, 10 Oct 2026 00:00:00 +0000

NS modeling area — validation results and experiment overview.

NS - Experiment Adaptive_1

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: Adaptive_1 Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.5983
Bias	+0.5417
MAE	1.2525
Corr	0.9533
Model mean	32.7137
Obs mean	32.1720

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:32:32
  Area:         NS
  Experiment:   Adaptive_1
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:32:44

--- ICES point observations ---
  RMSE:          2.5983
  Bias:         +0.5417
  MAE:           1.2525
  Correlation:   0.9533
  N points:      2,236,819
  N profiles:    27,980
  Model mean:   32.7137
  Obs mean:     32.1720
  Model std:    4.4837
  Obs std:      6.4251

  Error distribution:
    Min:         -17.2622
    5th pct:      -1.1804
    25th pct:     -0.5350
    Median:       -0.1916
    75th pct:     +0.1352
    95th pct:     +7.2982
    Max:         +33.6566

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,114     +3.2667      5.2914    0.9252
  10-25m        411,753     +0.7607      2.7948    0.9533
  25-50m        407,320     -0.2130      1.3397    0.9564
  50-100m       463,310     -0.3512      0.5545    0.5926
  100-200m      324,064     -0.2796      0.3866    0.5915
  200-500m      224,455     -0.1640      0.2229    0.6268
  500-1000m      14,803     -0.1505      0.1743    0.6283

📄 Download Statistics Report (txt) · 📄 YAML

NS - Experiment Adaptive_2

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: Adaptive_2 Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.6006
Bias	+0.5477
MAE	1.2463
Corr	0.9533
Model mean	32.7196
Obs mean	32.1719

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:35:41
  Area:         NS
  Experiment:   Adaptive_2
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:35:46

--- ICES point observations ---
  RMSE:          2.6006
  Bias:         +0.5477
  MAE:           1.2463
  Correlation:   0.9533
  N points:      2,236,727
  N profiles:    27,980
  Model mean:   32.7196
  Obs mean:     32.1719
  Model std:    4.4828
  Obs std:      6.4252

  Error distribution:
    Min:         -17.2593
    5th pct:      -1.2089
    25th pct:     -0.5187
    Median:       -0.1690
    75th pct:     +0.1343
    95th pct:     +7.3084
    Max:         +33.6466

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,112     +3.2676      5.2989    0.9250
  10-25m        411,753     +0.7546      2.7957    0.9535
  25-50m        407,304     -0.2225      1.3425    0.9563
  50-100m       463,301     -0.3469      0.5548    0.5939
  100-200m      324,019     -0.2533      0.3624    0.5883
  200-500m      224,437     -0.1277      0.1848    0.6149
  500-1000m      14,801     -0.1068      0.1311    0.5827

📄 Download Statistics Report (txt) · 📄 YAML

NS - Experiment Adaptive_3

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: Adaptive_3 Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.6013
Bias	+0.5436
MAE	1.2563
Corr	0.9532
Model mean	32.7156
Obs mean	32.1720

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:38:51
  Area:         NS
  Experiment:   Adaptive_3
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:38:44

--- ICES point observations ---
  RMSE:          2.6013
  Bias:         +0.5436
  MAE:           1.2563
  Correlation:   0.9532
  N points:      2,236,795
  N profiles:    27,980
  Model mean:   32.7156
  Obs mean:     32.1720
  Model std:    4.4810
  Obs std:      6.4250

  Error distribution:
    Min:         -17.2535
    5th pct:      -1.1663
    25th pct:     -0.5393
    Median:       -0.1987
    75th pct:     +0.1378
    95th pct:     +7.3066
    Max:         +33.6562

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,106     +3.2766      5.2981    0.9250
  10-25m        411,736     +0.7713      2.7977    0.9532
  25-50m        407,350     -0.2055      1.3373    0.9566
  50-100m       463,309     -0.3517      0.5543    0.5911
  100-200m      324,029     -0.2897      0.3961    0.5930
  200-500m      224,457     -0.1794      0.2395    0.6292
  500-1000m      14,808     -0.1664      0.1901    0.6165

📄 Download Statistics Report (txt) · 📄 YAML

NS - Experiment Baseline

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: Baseline Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.6065
Bias	+0.5403
MAE	1.2611
Corr	0.9532
Model mean	32.7075
Obs mean	32.1672

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:23:14
  Area:         NS
  Experiment:   Baseline
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:23:48

--- ICES point observations ---
  RMSE:          2.6065
  Bias:         +0.5403
  MAE:           1.2611
  Correlation:   0.9532
  N points:      2,239,481
  N profiles:    27,982
  Model mean:   32.7075
  Obs mean:     32.1672
  Model std:    4.4762
  Obs std:      6.4284

  Error distribution:
    Min:         -17.2384
    5th pct:      -1.2003
    25th pct:     -0.5472
    Median:       -0.1946
    75th pct:     +0.1334
    95th pct:     +7.3167
    Max:         +33.6631

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,140     +3.2820      5.3064    0.9254
  10-25m        412,998     +0.7575      2.8053    0.9533
  25-50m        407,979     -0.2234      1.3511    0.9556
  50-100m       463,619     -0.3534      0.5594    0.5896
  100-200m      324,278     -0.2827      0.3908    0.5918
  200-500m      224,657     -0.1674      0.2268    0.6291
  500-1000m      14,810     -0.1502      0.1737    0.6219

📄 Download Statistics Report (txt) · 📄 YAML

NS - Experiment CMEMS

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: CMEMS Validation Type: Horizontal Validation Variables: SALT_BOTTOM, TEMP_BOTTOM, SALT_SURFACE, TEMP_SURFACE

Overview

Metric	SALT_BOTTOM / NWS-salinity	TEMP_BOTTOM / NWS-bottomT	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST
RMSE	0.4879	1.4122	2.2332	0.8598	0.8541
Bias	0.0749	-0.6136	-0.1010	0.1970	0.4024
Corr	0.8398	0.9208	0.9219	0.9010	0.9213
N points	58,068	58,068	16,531,480	2,003,850	17,213,415

Annual Statistics Trends

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

NS - Experiment ObsKd

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: ObsKd Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.6434
Bias	+0.5740
MAE	1.2680
Corr	0.9531
Model mean	32.7412
Obs mean	32.1672

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:51:39
  Area:         NS
  Experiment:   ObsKd
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:41:44

--- ICES point observations ---
  RMSE:          2.6434
  Bias:         +0.5740
  MAE:           1.2680
  Correlation:   0.9531
  N points:      2,239,479
  N profiles:    27,982
  Model mean:   32.7412
  Obs mean:     32.1672
  Model std:    4.4321
  Obs std:      6.4284

  Error distribution:
    Min:         -17.1605
    5th pct:      -1.1617
    25th pct:     -0.5281
    Median:       -0.1850
    75th pct:     +0.1386
    95th pct:     +7.4418
    Max:         +33.6755

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,138     +3.3694      5.3929    0.9248
  10-25m        412,998     +0.8098      2.8412    0.9538
  25-50m        407,979     -0.2014      1.3491    0.9566
  50-100m       463,619     -0.3414      0.5469    0.5932
  100-200m      324,278     -0.2727      0.3814    0.5929
  200-500m      224,657     -0.1603      0.2193    0.6298
  500-1000m      14,810     -0.1424      0.1656    0.6084

📄 Download Statistics Report (txt) · 📄 YAML

NS - Experiment SSR_SRT

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: SSR_SRT Validation Type: Horizontal Validation Variables:

Argo Float Profiles

Plots

Argo float profile overview (NE Atlantic)

NA_ARGO — Hovmöller diagram

Observations

ARGO Observations — 2016-2023

ARGO Station Map

ARGO Observation Density

ARGO Hovmoller

ICES Observations — 2016-2023

ICES Station Map

NS - Experiment SSR_STR

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: SSR_STR Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.6128
Bias	+0.5482
MAE	1.2606
Corr	0.9532
Model mean	32.7154
Obs mean	32.1672

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:26:18
  Area:         NS
  Experiment:   SSR_STR
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:26:47

--- ICES point observations ---
  RMSE:          2.6128
  Bias:         +0.5482
  MAE:           1.2606
  Correlation:   0.9532
  N points:      2,239,481
  N profiles:    27,982
  Model mean:   32.7154
  Obs mean:     32.1672
  Model std:    4.4704
  Obs std:      6.4284

  Error distribution:
    Min:         -17.2323
    5th pct:      -1.1848
    25th pct:     -0.5399
    Median:       -0.1937
    75th pct:     +0.1353
    95th pct:     +7.3387
    Max:         +33.6850

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,140     +3.3001      5.3217    0.9252
  10-25m        412,998     +0.7706      2.8114    0.9533
  25-50m        407,979     -0.2155      1.3495    0.9559
  50-100m       463,619     -0.3501      0.5543    0.5921
  100-200m      324,278     -0.2816      0.3887    0.5903
  200-500m      224,657     -0.1674      0.2266    0.6256
  500-1000m      14,810     -0.1529      0.1765    0.6274

📄 Download Statistics Report (txt) · 📄 YAML

NS - Experiment SSRD_STRD

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NS Experiment: SSRD_STRD Validation Type: Horizontal Validation Variables:

ICES Point Profiles

Statistics

PSAL

Metric	ICES point observations
RMSE	2.6506
Bias	+0.5833
MAE	1.2688
Corr	0.9531
Model mean	32.7505
Obs mean	32.1671

View Full Statistics Report

################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-04 11:29:24
  Area:         NS
  Experiment:   SSRD_STRD
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-04  12:29:45

--- ICES point observations ---
  RMSE:          2.6506
  Bias:         +0.5833
  MAE:           1.2688
  Correlation:   0.9531
  N points:      2,239,484
  N profiles:    27,982
  Model mean:   32.7505
  Obs mean:     32.1671
  Model std:    4.4246
  Obs std:      6.4285

  Error distribution:
    Min:         -17.0901
    5th pct:      -1.1438
    25th pct:     -0.5232
    Median:       -0.1833
    75th pct:     +0.1464
    95th pct:     +7.4716
    Max:         +33.7238

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         391,143     +3.3926      5.4090    0.9247
  10-25m        412,998     +0.8309      2.8483    0.9538
  25-50m        407,979     -0.1900      1.3492    0.9567
  50-100m       463,619     -0.3383      0.5432    0.5937
  100-200m      324,278     -0.2774      0.3844    0.5926
  200-500m      224,657     -0.1663      0.2250    0.6311
  500-1000m      14,810     -0.1490      0.1720    0.6332

📄 Download Statistics Report (txt) · 📄 YAML

NS Rankings

Sat, 10 Oct 2026 00:00:00 +0000

NS Experiment Rankings

Composite-score ranking of every experiment validated for NS, one chart per validation category. See the NS overview for the full per-experiment statistics these are built from. See All Areas to compare across areas.

Horizontal Surface Validation

Only one experiment validated so far for this category — ranking needs at least two.

Horizontal Bottom Layer Validation

Only one experiment validated so far for this category — ranking needs at least two.

NSe

Sat, 10 Oct 2026 00:00:00 +0000

NSe modeling area — validation results and experiment overview.

NSe - CMEMS/tidal - Tidal Analysis

Sat, 10 Oct 2026 00:00:00 +0000

Harmonic analysis of sea surface elevation compared with satellite products and tide gauge observations.

Analysis Summary

Analyzed Constituents: K1, K2, M2, N2, O1, P1, Q1, S2
Analysis Date: 2026-10-10 Satellite Product: FES2014/TPXO9

Plots

Tide Gauge Station Map

GESLA tide gauge station locations

Year 2011

K₁ (lunar-solar diurnal) — model amplitude and phase

K₁ (lunar-solar diurnal) — amplitude/phase comparison with GESLA tide gauges

NSe - CMIP6/tidal - Tidal Analysis

Sat, 10 Oct 2026 00:00:00 +0000

Harmonic analysis of sea surface elevation compared with satellite products and tide gauge observations.

Analysis Summary

Analyzed Constituents: K1, K2, M2, N2, O1, P1, Q1, S2
Analysis Date: 2026-10-10 Satellite Product: FES2014/TPXO9

Plots

Tide Gauge Station Map

GESLA tide gauge station locations

K₁ (lunar-solar diurnal) — model amplitude and phase

K₁ (lunar-solar diurnal) — amplitude/phase comparison with GESLA tide gauges

NSe - Experiment CMEMS/spinup

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NSe Experiment: CMEMS/spinup Validation Type: Horizontal Validation Variables: TEMP_SURFACE

Overview

Metric	TEMP_SURFACE / OSTIA
RMSE	1.1152
Bias	0.5067
Corr	0.9031
N points	10,685,740

Horizontal Validation

Statistics

TEMP_SURFACE

Metric	OSTIA
RMSE	1.1152
Bias	+0.5067
MAE	0.8360
Corr	0.9031
Model mean	11.3317
Obs mean	10.8250

View Full Statistics Report

################################################################################
TEMP_SURFACE Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-10-07 11:11:45
  Author:       K.B. & R.T.
  Project:      OceanICU
  Institute:    BB
  Area:         NSe
  Experiment:   CMEMS/spinup
################################################################################

================================================================================
Period: 2011-2013  |  Model: pyGETM
================================================================================
Analysed: 2026-10-07  13:59:52

--- OSTIA ---
  RMSE:          1.1152
  Bias:         +0.5067
  MAE:           0.8360
  Correlation:   0.9031
  N points:      10,685,740
  Model mean:   11.3317
  Obs mean:     10.8250
  Model std:    3.9211
  Obs std:      3.8866

  Error distribution:
    Min:         -7.8229
    5th pct:      -0.8018
    25th pct:     -0.0160
    Median:       +0.4534
    75th pct:     +0.8926
    95th pct:     +1.7141
    Max:         +8.4514

  Monthly breakdown:
  Month      RMSE      Bias      MAE    Corr
  --------------------------------------------
  Jan      1.9842   +1.6141   1.6611  0.8909
  Feb      1.0789   +0.7273   0.8487  0.9342
  Mar      0.8300   +0.4440   0.5908  0.9489
  Apr      0.7977   +0.3366   0.5978  0.9336
  May      0.9001   +0.4658   0.7189  0.8604
  Jun      1.3635   +1.0185   1.1503  0.8592
  Jul      1.4952   +1.1272   1.2696  0.8166
  Aug      1.2142   +0.8638   1.0129  0.8698
  Sep      0.7362   +0.2231   0.5553  0.9152
  Oct      0.6620   -0.1049   0.4921  0.9460
  Nov      0.6538   -0.1955   0.4983  0.9331
  Dec      0.8082   -0.4389   0.6194  0.9291
  --------------------------------------------
  All      1.1152   +0.5067   0.8360  0.9031

================================================================================
Period: 2011  |  Model: pyGETM
================================================================================
Analysed: 2026-10-07  13:59:52

--- OSTIA ---
  RMSE:          1.1152
  Bias:         +0.5067
  MAE:           0.8360
  Correlation:   0.9031
  N points:      10,685,740
  Model mean:   11.3317
  Obs mean:     10.8250
  Model std:    3.9211
  Obs std:      3.8866

  Error distribution:
    Min:         -7.8229
    5th pct:      -0.8018
    25th pct:     -0.0160
    Median:       +0.4534
    75th pct:     +0.8926
    95th pct:     +1.7141
    Max:         +8.4514

📄 Download Statistics Report (txt) · 📄 YAML

NSe - Experiment CMEMS/v01P

Sat, 10 Oct 2026 00:00:00 +0000

Horizontal Validation results comparing model output against observations.

Experiment Information

Area: NSe Experiment: CMEMS/v01P Validation Type: Horizontal Validation Variables: SALT_BOTTOM, TEMP_BOTTOM, SALT_SURFACE, TEMP_SURFACE Period: 2010–2013

Overview

Metric	SALT_BOTTOM / NWS-salinity	TEMP_BOTTOM / NWS-bottomT	SALT_SURFACE / CCI-SSS	TEMP_SURFACE / OSTIA	TEMP_SURFACE / CCI-SST	TEMP_SURFACE / OISST
RMSE	0.6971	1.4277	3.3841	1.0235	1.0134	1.0914
Bias	-0.3027	-0.5865	-1.5321	0.0815	0.1903	0.1890
Corr	0.8896	0.9169	0.8908	0.9080	0.9145	0.9076
N points	1,265,376	1,265,376	40,069,386	42,772,236	43,019,271	39,162,105

Horizontal Validation

Statistics

SALT_BOTTOM

Metric	NWS-salinity
RMSE	0.6971
Bias	-0.3027
MAE	0.3791
Corr	0.8896
Model mean	34.4084
Obs mean	34.7111

View Full Statistics Report

################################################################################
SALT_BOTTOM Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-10-08 20:11:59
  Area:         NSe
  Experiment:   CMEMS/v01P
################################################################################

================================================================================
Period: 2010-2013  |  Model: pyGETM
================================================================================
Analysed: 2026-10-08  20:37:04

--- NWS-salinity ---
  RMSE:          0.6971
  Bias:         -0.3027
  MAE:           0.3791
  Correlation:   0.8896
  N points:      1,265,376
  Model mean:   34.4084
  Obs mean:     34.7111
  Model std:    1.3446
  Obs std:      1.0655

  Error distribution:
    Min:         -16.5796
    5th pct:      -1.3642
    25th pct:     -0.4245
    Median:       -0.1510
    75th pct:     -0.0028
    95th pct:     +0.1602
    Max:         +8.8929

  Monthly breakdown:
  Month      RMSE      Bias      MAE    Corr
  --------------------------------------------
  Jan      0.6379   -0.2419   0.3419  0.8927
  Feb      0.6361   -0.2803   0.3705  0.8950
  Mar      0.6865   -0.3041   0.3890  0.8826
  Apr      0.6773   -0.3120   0.3933  0.8913
  May      0.6346   -0.2988   0.3775  0.8943
  Jun      0.6936   -0.3041   0.3820  0.8910
  Jul      0.7101   -0.3066   0.3834  0.8920
  Aug      0.7107   -0.3183   0.3826  0.9039
  Sep      0.7717   -0.3353   0.3948  0.8947
  Oct      0.7382   -0.3199   0.3808  0.8991
  Nov      0.7186   -0.3003   0.3734  0.8872
  Dec      0.7350   -0.3107   0.3800  0.8810
  --------------------------------------------
  All      0.6971   -0.3027   0.3791  0.8896

================================================================================
Period: 2010  |  Model: pyGETM
================================================================================
Analysed: 2026-10-08  20:37:04

--- NWS-salinity ---
  RMSE:          0.5176
  Bias:         -0.2093
  MAE:           0.2886
  Correlation:   0.9186
  N points:      316,344
  Model mean:   34.4712
  Obs mean:     34.6805
  Model std:    1.1958
  Obs std:      1.0703

  Error distribution:
    Min:         -13.2630
    5th pct:      -0.9487
    25th pct:     -0.3140
    Median:       -0.1237
    75th pct:     -0.0010
    95th pct:     +0.1504
    Max:         +6.7311

================================================================================
Period: 2011  |  Model: pyGETM
================================================================================
Analysed: 2026-10-08  20:37:04

--- NWS-salinity ---
  RMSE:          0.7867
  Bias:         -0.3657
  MAE:           0.4295
  Correlation:   0.8890
  N points:      316,344
  Model mean:   34.3441
  Obs mean:     34.7098
  Model std:    1.4523
  Obs std:      1.0837

  Error distribution:
    Min:         -14.9381
    5th pct:      -1.7103
    25th pct:     -0.4376
    Median:       -0.1632
    75th pct:     -0.0159
    95th pct:     +0.1437
    Max:         +8.4983


================================================================================
Period: 2012  |  Model: pyGETM
================================================================================
Analysed: 2026-10-08  20:37:04

--- NWS-salinity ---
  RMSE:          0.7846
  Bias:         -0.3346
  MAE:           0.4079
  Correlation:   0.8806
  N points:      316,344
  Model mean:   34.4468
  Obs mean:     34.7814
  Model std:    1.4133
  Obs std:      1.0098

  Error distribution:
    Min:         -16.5796
    5th pct:      -1.6229
    25th pct:     -0.4299
    Median:       -0.1581
    75th pct:     -0.0037
    95th pct:     +0.1572
    Max:         +8.8929


================================================================================
Period: 2013  |  Model: pyGETM
================================================================================
Analysed: 2026-10-08  20:37:04

--- NWS-salinity ---
  RMSE:          0.6646
  Bias:         -0.3012
  MAE:           0.3905
  Correlation:   0.8912
  N points:      316,344
  Model mean:   34.3717
  Obs mean:     34.6729
  Model std:    1.2980
  Obs std:      1.0928

  Error distribution:
    Min:         -12.5484
    5th pct:      -1.2125
    25th pct:     -0.4990
    Median:       -0.1618
    75th pct:     +0.0028
    95th pct:     +0.1810
    Max:         +7.1086

📄 Download Statistics Report (txt) · 📄 YAML

NSe - WOA/tidal - Tidal Analysis

Sat, 10 Oct 2026 00:00:00 +0000

Harmonic analysis of sea surface elevation compared with satellite products and tide gauge observations.

Analysis Summary

Analyzed Constituents: K1, K2, M2, N2, O1, P1, Q1, S2
Analysis Date: 2026-10-10 Satellite Product: FES2014/TPXO9

Plots

Year 2010

K₁ (lunar-solar diurnal) — model amplitude and phase

O₁ (principal lunar diurnal) — model amplitude and phase

P₁ (principal solar diurnal) — model amplitude and phase

NSe — Future Boundary Conditions

Sat, 10 Oct 2026 00:00:00 +0000

Cross-model, cross-scenario summary of NSe’s future open-boundary conditions: delta-change temperature/salinity (CMIP6, bias-corrected against the CMEMS Northwest Shelf reanalysis), tidal elevation/currents (TPXO9 plus the CMIP6 ensemble-mean sea-level trend), and (as of 2026-09-22) delta-change biogeochemistry – nitrate, phosphate, silicate, oxygen, dissolved inorganic carbon, and alkalinity – across all three CMIP6 models and both scenarios (see the bug-fix campaign section below for how that came together).

Delta-change (temperature / salinity)

Temperature (thetao)

Source: AMM7/AMM15 delta-change: GFDL-ESM4/ssp126 [1985-01-01–2014-12-31 vs 2015-01-01–2100-12-31]

NSe Rankings

Sat, 10 Oct 2026 00:00:00 +0000

NSe Experiment Rankings

Composite-score ranking of every experiment validated for NSe, one chart per validation category. See the NSe overview for the full per-experiment statistics these are built from. See All Areas to compare across areas.

Tidal Analysis

Horizontal Surface Validation

Horizontal Bottom Layer Validation

Only one experiment validated so far for this category — ranking needs at least two.

Gridded 3D Validation

Regridding

Sat, 10 Oct 2026 00:00:00 +0000

Regridding User Guide

ocean_data.regridding provides xESMF-based regridding with automatic weight-file caching, domain subsetting, and coordinate name normalisation. The regrid command (cli/regrid.py) exposes the same functionality as a command-line tool. ocean-post re-exports the same API from ocean_post.regridding for backward compatibility; import from ocean_data.regridding in new code.

1. Introduction

Regridding maps data from one spatial grid to another — for example interpolating a global 0.25° satellite observation product onto a regional 1 km model grid, or the other way round, before computing validation statistics.

Ocean Model Validation

AMM7 — Analysis Overview

AMM7 Analysis Overview

Tidal Analysis

Horizontal Surface Validation

NS — Analysis Overview

NS Analysis Overview

Tidal Analysis

Horizontal Surface Validation

Horizontal Bottom Layer Validation

NSe — Analysis Overview

NSe Analysis Overview

Tidal Analysis

Tidal Analysis

Tidal Analysis Guide

Overview

Analysis modes

Model source: --source / --experiment

Gridded 2D Validation

Gridded 2D Validation Guide

Overview

Gridded 3D Validation

Gridded 3D Validation Guide

Overview

Profile Validation

Profile Validation Guide

Overview

Config structure

MLE Comparison

MLE Cross-Experiment Comparison Guide

Overview

Run Validation

run-analyses Guide

Overview

Scenario Analysis

Scenario Analysis Guide

Overview

Step 1 — Bias correction

Push To Remote

push-to-remote Guide

Overview

River Flow and Nutrient Load Projection

River Flow and Nutrient Load Projection Guide

Update From Remote

update-from-remote Guide

Overview

Manage Analyses

manage-analyses Guide

Overview

clean

Simulation List

Simulation List Guide

Overview

Subcommands

Statistics Database

Statistics Database Guide

Overview

Tables

statistics

Reporting

Reporting Guide

Overview

Prerequisite: the analyses/ tree

Hugo Deployment

Hugo Deployment Guide

Overview

All Areas Rankings

All Areas — Experiment Rankings

Tidal Analysis

Horizontal Surface Validation

Horizontal Bottom Layer Validation

AMM7

AMM7 - Experiment Baseline

Experiment Information

Overview

Annual Statistics Trends

AMM7 - Experiment CMEMS

Experiment Information

Overview

Annual Statistics Trends

Model source: `--source` / `--experiment`