DIVAnd performs an n-dimensional variational analysis of arbitrarily located observations. Observations will be interpolated on a curvilinear grid in 2, 3 or more dimensions.
Please cite this paper as follows if you use
DIVAnd in a publication:
Barth, A., Beckers, J.-M., Troupin, C., Alvera-Azcárate, A., and Vandenbulcke, L.: DIVAnd-1.0: n-dimensional variational data analysis for ocean observations, Geosci. Model Dev., 7, 225-241, doi:10.5194/gmd-7-225-2014, 2014.
(click here for the BibTeX entry).
Under Linux you will also need the packages
nlopt which you can install under Debian/Ubuntu with:
apt-get install make gcc libnlopt0 libnetcdf-dev netcdf-bin
You need Julia (version 0.6 or 1.0) to run
DIVAnd. The command line version is sufficient for
Inside Julia, you can download and install the package by issuing:
using Pkg Pkg.clone("https://github.com/gher-ulg/DIVAnd.jl")
It is not recommended to download the source of
DIVAnd.jl directly (using the green Clone or Download button above) because this by-passes Julia's package manager and you would need to install the dependencies of
A test script is included to verify the correct functioning of the toolbox.
The script should be run in a Julia session.
Make sure to be in a directory with write-access (for example your home directory).
You can change the directory to your home directory with the
All tests should pass without error.
INFO: Testing DIVAnd Test Summary: | Pass Total DIVAnd | 100 100 INFO: DIVAnd tests passed
The test suite will download some sample data. You need to have Internet access and run the test function from a directory with write access.
The main routine of this toolbox is called
DIVAnd which performs an n-dimensional variational analysis of arbitrarily located observations. Type the following in Julia to view a list of parameters:
using DIVAnd ?DIVAndrun
DIVAnd_simple_example_4D.jl is a basic example in fours dimensions. The call to
DIVAndrun looks like this:
fi,s = DIVAndrun(mask,(pm,pn,po,pq),(xi,yi,zi,ti),(x,y,z,t),f,len,epsilon2);
mask is the land-sea mask, usually obtained from the bathymetry/topography,
(pm,pn,po,pq) is a n-element tuple (4 in this case) containing the scale factors of the grid,
(xi,yi,zi,ti) is a n-element tuple containing the coordinates of the final grid,
(x,y,z,t) is a n-element tuple containing the coordinates of the observations,
f is the data anomalies (with respect to a background field),
len is the correlation length and
epsilon2 is the error variance of the observations.
The call returns
fi, the analyzed field on the grid
DIVAndrun is the core analysis function in n dimensions. It does not know anything about the physical parameters or units you work with. Coordinates can also be very general. The only constraint is that the metrics
(pm,pn,po,...) when multiplied by the corresponding length scales
len lead to non-dimensional parameters. Furthermore the coordinates of the output grid
(xi,yi,zi,...) need to have the same units as the observation coordinates
DIVAndgo is only needed for very large problems when a call to
DIVAndrun leads to memory or CPU time problems. This function tries to decide which solver (direct or iterative) to use and how to make an automatic domain decomposition. Not all options from
DIVAndrun are available.
diva3D is a higher-level function specifically designed for climatological analysis of data on Earth, using longitude/latitude/depth/time coordinates and correlations length in meters. It makes the necessary preparation of metrics, parameter optimizations etc you normally would program yourself before calling the analysis function
If zero is not a valid first guess for your variable (as it is the case for e.g. ocean temperature), you have to subtract the first guess from the observations before calling
DIVAnd and then add the first guess back in.
epsilon2 and parameter
len are crucial for the analysis.
epsilon2 corresponds to the inverse of the signal-to-noise ratio.
epsilon2 is the normalized variance of observation error (i.e. divided by the background error variance). Therefore, its value depends on how accurate and how representative the observations are.
len corresponds to the correlation length and the value of
len can sometimes be determined by physical arguments. Note that there should be one correlation length per dimension of the analysis.
One statistical way to determine the parameter(s) is to do a cross-validation.
You can repeat all steps with a different validation data set to ensure that the optimal parameter values are robust. Tools to help you are included in (DIVAnd_cv.jl).
An arbitrary number of additional constraints can be included to the cost function which should have the following form:
J(x) = ∑i (Ci x - zi)ᵀ Qi-1 (Ci x - zi)
For every constrain, a structure with the following fields is passed to
yo: the vector zi
H: the matrix Ci
R: the matrix Qi (symmetric and positive defined)
Internally the observations are also implemented as constraint defined in this way.
On the server, launch the notebook with:
~/.julia/v0.6/Conda/deps/usr/bin/jupyter-notebook --no-browser --ip='0.0.0.0' --port=8888
where the path to
jupyter-notebook might have to be adapted, depending on your installation. The
port parameters can also be modified.
Then from the local machine it is possible to connect to the server through the browser.
Thanks to Lennert and Bart (VLIZ) for this trick.
Some examples in
DIVAnd.jl use a quite large data set which cannot be efficiently distributed through
git. This data can be downloaded from the URL https://dox.ulg.ac.be/index.php/s/Bo01EicxnMgP9E3/download. The zip file should be decompressed and the directory
DIVAnd-example-data should be placed on the same level than the directory
An educational web application has been developed to reconstruct a field based on point "observations". The user must choose in an optimal way the location of 10 observations such that the analysed field obtained by
DIVAnd based on these observations is as close as possible to the original field.
1 day ago