/
global_ocean_circulation.jl
2826 lines (2323 loc) · 101 KB
/
global_ocean_circulation.jl
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### A Pluto.jl notebook ###
# v0.19.40
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
end
# ╔═╡ 104ce9b0-3fd1-11ec-3eff-3b029552e3d9
begin
using IndividualDisplacements, CairoMakie, PlutoUI
using OceanStateEstimation, MITgcmTools, CSV, JLD2
include("ECCO_FlowFields.jl")
"Done with Loading Packages"
end
# ╔═╡ c9e9faa8-f5f0-479c-bc85-877ff7114883
md"""# Global Climatology
Simulate the displacement of a set of individuals (or particles) according to
climatological monthly mean flow at selected depth level
(or in 3D). The flow fields are from a global ocean state estimate ([ECCO v4 r2](https://eccov4.readthedocs.io/en/latest/) ; see also <https://ecco-group.org>). It is here repeated for `ny` years to simulate trajectories.
For additional documentation see :
[1](https://JuliaClimate.github.io/IndividualDisplacements.jl/dev/),
[2](https://JuliaClimate.github.io/MeshArrays.jl/dev/),
[3](https://docs.juliadiffeq.org/latest/solvers/ode_solve.html),
[4](https://en.wikipedia.org/wiki/Displacement_(vector))
[](https://youtu.be/W5DNqJG9jt0)
"""
# ╔═╡ 171fa252-7a35-4d4a-a940-60de77327cf4
TableOfContents()
# ╔═╡ 7fec71b4-849f-4369-bec2-26bfe2e00a97
begin
bind_k = (@bind ktxt Select(["0","1","10","30","40"],default="1"))
bind_ny = (@bind nytxt Select(["1/12","1","2"],default="1/12"))
bind_np = (@bind nptxt Select(["10","100","500"],default="10"))
md"""## 1. Simulation Parameters
The following parameters are used:
- k = $(bind_k) = vertical level (for 2D; or 0 for 3D)
- ny = $(bind_ny) = similated period (1/12 = 1 month)
- np = $(bind_np) = number of individuals (100 by default)
"""
end
# ╔═╡ 94ca10ae-6a8a-4038-ace0-07d7d9026712
md"""## 2. Configure `FlowFields`
####
`global_ocean_circulation` reads in the global Ocean model grid and flow fields. It returns the `FlowFields` data structure `𝑃`. Ancillary variables are stored in NamedTuple `𝐷`.
!!! note
The global grid is handled via `MeshArrays.jl` and monthly climatology fields accessed via `OceanStateEstimation.jl`.
"""
# ╔═╡ 218b9beb-68f2-4498-a96d-08e0719b4cff
begin
k=parse(Int,ktxt) #vertical level or 0
np=parse(Int,nptxt) #number of individuals
if nytxt=="1/12"
ny=1 #number of years
nm=1 #number of months
else
ny=parse(Int,nytxt) #number of years
nm=12 #number of months
end
OceanStateEstimation.get_ecco_velocity_if_needed()
𝑃,𝐷=ECCO_FlowFields.global_ocean_circulation(k=k)
"Done with Setting Up FlowFields"
end
# ╔═╡ f1215951-2eb2-490b-875a-91c1205b8f63
md"""## 3. Trajectory Computation
### 3.1 Initialization
- initialize individual positions (`init_from_file`)
- initial integration from time 0 to 0.5 month
"""
# ╔═╡ f727992f-b72a-45bc-93f1-cc8daf89af0f
begin
df = ECCO_FlowFields.init_from_file(np)
𝐼=Individuals(𝑃,df.x,df.y,df.f,(𝐷=𝐷,))
📌_reference=deepcopy(𝐼.📌)
𝐼
end
# ╔═╡ a3e45927-5d53-42be-b7b7-489d6e7a6fe5
begin
𝑇=(0.0,𝐼.𝑃.𝑇[2])
∫!(𝐼,𝑇)
✔1="Done with Initial Integration"
end
# ╔═╡ 6158a5e4-89e0-4496-ab4a-044d1e3e8cc0
md""" ### 3.2 Monthly Step Function
Since the Ocean circulation varies from month to month, the `𝐼.𝐷.🔄` function is used to update flow fields from one month to the next.
Time variable flow fields are easily handled by defining a `step!` function that wraps around `∫!`. Here it does three things
- `𝐼.𝐷.🔄` resets the velocity inputs (`𝐼.𝐷.u0` etc) to bracket time `t_ϵ=𝐼.𝑃.𝑇[2]+eps(𝐼.𝑃.𝑇[2])`
- `reset_📌!` randomly selects a fraction of the individuals and resets their positions. This can be useful to maintain homogeneous coverage of the domain by the fleet of individuals.
- `∫!(𝐼)` solves for the individual trajectories over one month (`𝐼.𝑃.𝑇`) with updated velocity fields (`𝐼.𝑃.u0` etc). It also adds diagnostics to the `DataFrame` used to record variables along the trajectory (`𝐼.🔴`).
"""
# ╔═╡ a2375720-f599-43b9-a7fb-af17956309b6
function step!(𝐼::Individuals)
t_ϵ=𝐼.𝑃.𝑇[2]+eps(𝐼.𝑃.𝑇[2])
𝐼.𝐷.🔄(𝐼.𝑃,𝐼.𝐷,t_ϵ)
ECCO_FlowFields.reset_📌!(𝐼,𝐼.𝐷.frac,📌_reference)
∫!(𝐼)
end
# ╔═╡ 7efadea7-4542-40cf-893a-40a75e9c52be
md"""### 3.3 Monthly Simulation Loop
!!! note
`add_lonlat!` derives geographic locations (longitude and latitude) from local grid coordinates (x, y, etc).
"""
# ╔═╡ 1044c5aa-1a56-45b6-a4c6-63d24eea878d
begin
✔1
[step!(𝐼) for y=1:ny, m=1:nm]
add_lonlat!(𝐼.🔴,𝐼.𝐷.XC,𝐼.𝐷.YC,𝑃.update_location!)
✔2="Done with Main Computation"
end
# ╔═╡ 6e43a2af-bf01-4f42-a4ba-1874a8cf4885
let
✔2
using DataFrames, Statistics
gdf = groupby(𝐼.🔴, :ID)
sgdf= combine(gdf,nrow,:lat => mean)
end
# ╔═╡ fc16b761-8b1f-41de-b4fe-7fa9987d6167
𝐼.🔴
# ╔═╡ c5ba37e9-2a68-4448-a2cb-dea1fbf08f1e
md"""## 4. Visualize Displacements"""
# ╔═╡ b4841dc0-c257-45e0-8657-79121f2c9ce8
plot(𝐼)
# ╔═╡ 15077957-64d5-46a5-8a87-a76ad619cf38
md"""## 5. Summary Statistics
Here we briefly demontrate the use of [DataFrames.jl](https://juliadata.github.io/DataFrames.jl/latest/) to analyze the output (𝐼.🔴) of our simulation.
"""
# ╔═╡ de8dbb43-68bc-4fb2-b0c8-07100b8a97a0
md"""## Appendix : Plotting Function"""
# ╔═╡ e1cdcac9-c3cc-4ce4-a477-452ca460a3d5
"""
plot(𝐼::Individuals)
Plot initial and final positions, superimposed on a globalmap of ocean depth log.
"""
function plot(𝐼::Individuals)
𝐵=𝐼.𝐷.ODL
xlims=extrema(𝐵.lon)
ylims=extrema(𝐵.lat)
fig=Figure()
ax=Axis(fig[1,1])
plt=contourf!(ax,𝐵.lon,𝐵.lat,permutedims(𝐵.fld),colorrange=𝐵.rng,colormap = :ice)
🔴_by_t = groupby(𝐼.🔴, :t)
lo=deepcopy(🔴_by_t[1].lon); lo[findall(lo.<xlims[1])]=lo[findall(lo.<xlims[1])].+360
scatter!(ax,lo,🔴_by_t[1].lat,markersize=12,color=:red,marker = :circle)
lo=deepcopy(🔴_by_t[end].lon); lo[findall(lo.<xlims[1])]=lo[findall(lo.<xlims[1])].+360
scatter!(ax,lo,🔴_by_t[end].lat,markersize=12,color=:black,marker = :star5)
return fig
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
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JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
MITgcmTools = "62725fbc-3a66-4df3-9000-e33e85b3a198"
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# ╔═╡ 00000000-0000-0000-0000-000000000002
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