Notes from the MEOPAR Winter School in Marine Environmental Prediction – README.md

README.md

MEOPAR Winter School in Environmental Modeling Notes

Participant intros

Jenna Joyce: UoOttawa w/ Jackie Dawson

  • Shipping in the Arctic (Corridors)
  • Corridors & Environment/Culturally significant areas coexistence

Ewelina Luczko: Baird & associates "where land meets water"

  • Coastal engineering - wave modeling
  • Wave energy generation in SWAN

Lindsay Chipman: UoColorado - Boulder

  • Cycling of O2 and C in permeable sediment
  • O2 flux from

Lei Ren: NUI Galway

  • Surface flow fields from CODAR & Model
  • Focus area: West Coast of Ireland

Russel Glazer: UoFlorida - Tallahassee

  • Saturation over ice & water in models
  • Cloud formation scheme

Blanche St-Béat: ULaval - Qc

  • Food Webs: distinguish ecosystemséestablish stability
  • Resilience vs Resistance: Wood house is better than hay or bricks?!

Feifei Sun: Andrea Scott student

  • Predict ice thickness from data
  • New assimilation methods

Dennis Monteban: Denmark university

  • Study of Fjord west of Greenland / Wave-Ice damping
  • Validated & Calibrated MIKE model

Ben Moore-Maley: UBC w/ Susan Allen

  • 3D modeling of Salish Sea @ 500m resolution
  • Wind driven circulation/interactions

Nancy Chen: DFO - St-John's

  • Satellite SSH anomaly -> water transport
  • Labrador & Scotian Shelf

Deborah Benkort: PhD Laval

  • Krill aggregation & dispersion in GSL/GSE
  • Effect of envt. on growth, distribution, reproduction

Becky Segal: MSc UVic

  • Permafrost thaw sumps work before
  • Ice prediction/obs and creating relevant products for communities

Charles Brunette: McGill with Bruno

  • Predictability using Lagrangian methods
  • Seasonal & Regional
  • Later formation -> Thinner ice -> Minimum ice extent

Onur Bora: Coastal engineer/PhD student in Istanbul

  • Hydrodynamics effects from shipping -> impact on sediment
  • Water cooling/intake system design

Olivier Asselin:

  • Figure out the atmosphere
  • Energy constant vs \lambda for different processes: 2 slopes for everything?!
  • CFD

Nathan Grivault: UoAlberta w/ Paul Myers

  • Freshwater in the Arctic: Arctic -> Lower latitudes
  • Impact on circulation: Export vs Forcing

UQAR People

Claudia Carascal:

  • Satellite validation of reflectance
  • Adjust for atmosphere/ocean to get buoy value

Jean-Luc Shaw: w/ Daniel and Dany

  • Hydrodynamics of the Bay of Sept-Iles
  • Numerical model & validation to support ecological indicators

Eliott Bismuth: w/ Dany

  • Wave-ice interactions & shore protection
  • Now erosion as an RA
  • Wave data/modeling/validation + XBeach

Sandy Gregorio: w/ Zhigang Xu (DFO)

  • Modeling storm surge & tsunami
  • Atmosphere: frequency of storms from low P events
  • ocean to get impact

Gwenaëlle Gremion: w/ Dany

  • Biological carbon pump in NOW polynia
  • Understand/represent carbon flux under water (and increased C at depth?)
  • BGCModel + PO to assess Bio + Physics effects

Jeremy Beaudry: w/ Dany

  • Wave attenuation / ice breakup
  • 1D floe size & thickness distribution for 2D WW3

Manu John:

  • Wave-Ice interactions for regional modeling
  • HF radar

Sebastien Dugas:

  • Wave-floe size/concentration relationship

Michel Tantare:

  • Drift prediction/comparison
  • Eullarian vs Lagrangian methods for drift?

Jean Chavy:

  • Energy transfer/KE cascade
  • Combine radar wind obs to get winds

Essi Aboyo:

  • Dynamics of the NOW polynia
  • Idealized model for climate change

Software configuration management

Main goals:

  • Track changes
  • Manage contributions
  • Review evolution

NEWTON Demonstration

Items (= source files) but no instructions, no build methods (= makefiles) and no support uses a lot of time/effort. With all that, you can build something efficient and effective.

Management hierarchy: Code Keeper -> Feature Keepers --> Coders

Git manages:

  • commit objects
  • trees (= directories)
  • blobs (Binary Large OBjectS)

References:

  • Pro Git
  • Git From The Bottom Up

Code Automation

Computers are good for tedious, error-prone, complicated and/or repetitive tasks.

Applicable to:

  • Version control
  • Back ups
  • Model management
  • Visualization
  • Code
  • Manuscripts
  • Pre/Post-processing tools

Assignement: Create a note repository, host, share and write down after each session

Your most important collaborators are your past and future selves

Separate code repo from:

  • Big binary files (Version control software would just make a copy)
  • Source code (to update model source code more easily/differentiate files that have been changed)

History is messy. It's better to see the whole history than to see a pretty history.

Readme files are important for new users and collaborators, including your future self.

Use scripts, not GUIs, to automate

Jupyter notebooks are great for demonstration and documentation

tree shows ASCII art of a directory tree (https://www.phys.ocean.dal.ca/~jpaucl/pmwiki.php/Bash/Tree)

More info at: https://bitbucket.org/douglatornell/uqar-winter-school

Exercise: Basic code automation: Using python, write a script that handles arguments, creates directories and moves/renames files.

Ocean Dynamics

  • Global model: look like Perpetual Ocean, generally for climate simulations
  • Regional model: see GoSL Observatory, generally for immediate use

Based on the Navier-Stokes Equations for incompressible fluid in a rotating frame of reference

  • 3 momentum equations (one for each component)
  • 1 continuity equation
  • Boussinesq approximation (ρ = ρ0 + ρ', ρ ~ ρ0 except for gravity)

DNS: Direct Numerical Simulation

  • Solve equations down to the smallest eddies
  • Typically with periodic boundary conditions

RANS: Reynolds Averaged NS equations

  1. Decompose u = u_avg + u', u'_avg = 0
  2. Plug in NS, get NS_avg + (u' · ∇ u')_avg