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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Spatial data interpolation</title>
<meta name="description" content="Spatial data interpolation">
<meta name="author" content="Charles">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
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var link = document.createElement( 'link' );
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<script src="lib/js/html5shiv.js"></script>
<![endif]-->
</head>
<body>
<div class="reveal">
<div class="slides">
<section>
<section>
<h2>Generating ocean climatologies from in situ observations</h2>
<p>
<i class="fab fa-github"></i> gher-ulg<br>
<i class="fab fa-twitter"></i> @GHER_ULiege <br>
<i class="ai ai-orcid"></i> 0000-0002-0265-1021
</p>
<p><img src="images/logo_gher.png" height="100px"/><img src="images/logo_uliege.jpeg" height="100px"/> <img src="images/logo_seadatacloud.png" height="100px"/> <img src="images/logo_imdis.png" height="100px"/></p>
<p>Alexander Barth, Charles Troupin, Sylvain Watelet,<br> Aida Alvera-Azcárate and Jean-Marie Beckers</p>
</section>
<section class="center">
<h2>
<p>Collect once,</p>
<p class="fragment fade-up">Use many times</p>
<p class="fragment fade-up">And create products with DIVA</p>
</h2>
</section>
<section>
<h1> Conclusions </h1>
<ol>
<li> `DIVA` is a software tool written in Fortran </li>
<li> `DIVAnd` is a software tool written in Julia </li>
<li> Both are designed for the spatial interpolation of data </li>
</p>
</section>
</section>
<section>
<section class="center">
<h1>Methodology:<br> spatial interpolation</h1>
</section>
<section data-state="diva-map">
<h2>Gridding problem</h2>
<div style="text-align:left" id="diva-map" class="map"></div>
</section>
<section data-markdown>
### Constraints
1. **Closer** observations have a **stronger** influence
2. Different **confidence** in some measurements
3. **Physical** barriers and currents
4. Deal with up to **millions** of points
5. Many sources of **errors** on observations
6. Need an associated **error field**
</section>
<section>
<h2>DIVA</h2>
Data-Interpolating Variational Analysis<br>
<a href="https://github.com/gher-ulg/DIVA">https://github.com/gher-ulg/DIVA</a><br>
<img src="https://zenodo.org/badge/80114691.svg"/> <br>
<img src="images/logo_diva.png"/> <br>
</section>
<section>
<h2>DIVAnd</h2>
<it>n</it>-dimensional generalisation of DIVA<br>
<a href="https://github.com/gher-ulg/DIVAnd.jl">https://github.com/gher-ulg/DIVAnd.jl</a><br>
<img src="https://zenodo.org/badge/79277337.svg"/> <br>
<img src="images/DIVAnd_github.jpg" width="80%"/> <br>
</section>
<section>
<h2>DIVAnd</h2>
<a href="https://www.geosci-model-dev.net/7/225/2014/">https://www.geosci-model-dev.net/7/225/2014/</a><br>
<img src="images/divand_paper.png"/> <br>
</section>
<section>
<h2>How to use it?</h2>
Jupyter notebooks as a guideline for the climatologies<br>
<img src="images/divand_notebooks.jpg"/> <br>
<a href="https://github.com/gher-ulg/Diva-Workshops">https://github.com/gher-ulg/Diva-Workshops</a><br>
</section>
<section style="text-align: left;"</section>
<h2>Fortran - MATLAB - Julia...</h2>
<img src="images/logo_julia.png" height="75px"/><br>
<p s>
Creation: 2012<br>
1.0.0 released: Aug 9, 2018<br>
Simplicity of Python + speed of C or Fortran<br>
<br>
<a href="http://julialang.org/">http://julialang.org/</a><br>
<a href="https://github.com/JuliaLang/julia">https://github.com/JuliaLang/julia</a>
</p>
</section>
<section >
<h2>Who is Julia?</h2>
<p class="fragment">
Julia Child (1912-2004)<br>
<img src="images/JuliaChild2.jpg" height="400px"/><br>
<h6 class="fragment">By Lynn Gilbert - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=51678880
</h6>
</p>
</section>
<section>
<h3> Why did we chose Julia? </h3>
<img src="images/harder_better.jpg" height="500px"/><br>
<h5>Source: http://daftpunk.wikia.com, No copyright infringement is intended</h5>
</section>
<section>
<h2>Better...</h2>
<br>
<p style="text-align:left">
Multiple dispatch<br>
Math-friendly syntax<br>
Unicode support: π, η, ∫ϵα
</p>
<pre><code class="julia">
julia> 🐟 = 1.
julia> 🐢 = 2.
julia> N = 🐟 + 🐢
3.0
</code></pre>
</section>
<section>
<h2>Faster</h2>
<p style="text-align:left">
Just-in-time (JIT) compiled<br>
Parallelism
</p>
<pre><code class="julia">
function fib(n::Int)
f=Vector{Int}(undef, n+1)
f[1]=f[2]=1;
for i=3:n+1
f[i]=f[i-1]+f[i-2]
end
return f
end
ff = @time fib(400000000);
1.158971 seconds (18.52 k allocations: 2.981 GiB, 0.84% gc time)
</code></pre>
</section>
<section>
<h2>Stronger</h2>
<p style="text-align:left">
<b>Metaprogramming:</b><br>
Julia programs can read, analyse, generate other Julia programs<br>
<br>
"Easy" interfacing: R, Python, ...
</p>
<pre><code class="julia">
@pyimport numpy.random as nr
nr.rand(3,4)
</code></pre>
</section>
<section>
<h2>Harder</h2>
<p style="text-align:left">
Learning a new and evolving language<br>
Transition from 0.6 to 1.0<br>
</p>
<img src="images/julia_release.jpg" height="400px"/><br>
</section>
</section>
<section>
<section class="center">
<h1>DIVAnd<br> in the VRE</h1>
</section>
<section class="center">
<h2>In short...</h2>
<p style="text-align:left"><br>
1. Ingest data from webODV (netCDF)<br>
2. Set the analysis parameters<br>
3. Apply DIVAnd interpolation<br>
4. Export the results in a new netCDF<br>
5. Visualise using Deltares toolbox<br>
</p>
</section>
<section class="center">
<img src="images/restAPI3.jpg" height="350px"/><br>
</section>
<section class="center">
<h2>Implementation</h2>
<p style="text-align:left"><br>
1. Julia using HTTP and JSON modules<br>
2. Deployment as a Docker container<br>
</p>
<img src="images/DIVAnd-REST.jpg" height="350px"/><br>
</section>
<section>
<video autoplay="true" loop="true" muted="true" width="640">
<source data-src="images/DIVAnd-REST-VRE-GUI.mp4" type="video/mp4"/>
</video>
</section>
</section>
<section>
<section class="center">
<h1>Applications</h1>
</section>
<section>
<h2>SeaDataCloud climatologies</h2>
<img src="images/snd_clim.jpg" height="450px"/><br>
<a href="https://www.seadatanet.org/Products/Climatologies">https://www.seadatanet.org/Products/Climatologies</a>
</section>
<section>
<h2>EMODnet Chemistry gridded fields</h2>
<img src="images/EMODnetChemi_prod.jpg" height="450px"/><br>
<a href="http://www.emodnet-chemistry.eu/products">http://www.emodnet-chemistry.eu/products</a>
</section>
<section>
<h2>EMODnet Biology products</h2>
<img src="images/EMODnetBio_prod.png" height="450px"/><br>
<a href="http://www.emodnet-biology.eu/data-products">http://www.emodnet-biology.eu/data-products</a>
</section>
</section>
<section>
<section class="center">
<h1>#Innovations</h1>
</section>
<section data-markdown data-transition="fade-in slide-out">
## High-frequency radar interpolation
Synthetic velocity field, red arrow = measurement
</section>
<section data-markdown data-transition="fade-in slide-out">
## High-frequency radar interpolation
Adding the influence of the coast
</section>
<section data-markdown data-transition="fade-in slide-out">
## High-frequency radar interpolation
Low horizontal divergence of currents
</section>
<section data-markdown data-transition="fade-in slide-out">
## High-frequency radar interpolation
Including Coriolis force and geostrophically balanced mean flow<br>
Test areas: Ibiza Channel, Gulf of Trieste
</section>
<section>
<h2>Neural network</h2>
<div class="tweet" data-src="https://twitter.com/xaprb/status/930674776317849600"></div>
</section>
<section>
<h2>Neural network</h2>
<p style="text-align:left"> From univariate to multivariate...</p>
<p style="text-align:left" ><b>Principle:</b> <br>
Use other co-variables to improve the interpolation<br>
Use neural network to derive the relationships between the variables</p>
</section>
<section data-markdown>
### Application: zooplankton count in the Baltic Sea
**Covariables:**
* [Dissolved oxygen](http://www.emodnet-chemistry.eu/products/catalogue#/metadata/087a72c0-c243-11e8-bac2-5ce0c5469bc7) → EMODnet Chemistry
* [Salinity](https://www.seadatanet.org/Products#/metadata/bf35a7c5-c843-4a23-8040-07ddcf3d8e71) → SeaDataCloud
* [Temperature](https://www.seadatanet.org/Products#/metadata/bf35a7c5-c843-4a23-8040-07ddcf3d8e71) → SeaDataCloud
* [Chlorophyll concentration](https://oceancolor.gsfc.nasa.gov/l3/) → MODIS-Aqua from NASA
* [Bathymetry](https://www.gebco.net/) → EMODnet Bathymetry, GEBCO
* [Distance from coast](https://gcmd.nasa.gov/KeywordSearch/Metadata.do?Portal=idn_ceos&KeywordPath=%5BData_Center%3A+Short_Name%3D%27PacIOOS%27%5D&OrigMetadataNode=GCMD&EntryId=dist2coast_1deg&MetadataView=Full&MetadataType=0&lbnode=mdlb1) → GSFC, NASA
</section>
<section>
<h3> Application: zooplankton count in the Baltic Sea</h3>
<p class="fragment step-fade-in-then-out"><img src="images/KeratellaCruciformis-2007.png" height="450px"/></p>
<p class="fragment step-fade-in-then-out"><img src="images/KeratellaCruciformis-2008.png" height="450px"/></p>
<p class="fragment step-fade-in-then-out"><img src="images/KeratellaCruciformis-2009.png" height="450px"/></p>
<p class="fragment step-fade-in-then-out"><img src="images/KeratellaCruciformis-2010.png" height="450px"/></p>
<p class="fragment step-fade-in-then-out"><img src="images/KeratellaCruciformis-2011.png" height="450px"/></p>
<p class="fragment step-fade-in-then-out"><img src="images/KeratellaCruciformis-2012.png" height="450px"/></p>
</section>
</section>
<section>
<section>
<h1> Conclusions </h1>
<ol>
<li> `DIVA` is a software tool written in Fortran </li>
<li> `DIVAnd` is a software tool written in Julia </li>
<li> Both are designed for the spatial interpolation of data </li>
<li> We are open and willing to improve and adapt the code for different data types</li>
</p>
</section>
<section class="center">
<h1> Thanks for your attention </h1>
<h6> (and use `DIVA{nd}` many times)</h6>
</section>
</section>
</div>
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