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Data Assimilation with Python: a Package for Experimental Research

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DAPPER is a set of templates for benchmarking the performance of data assimilation (DA) methods. The tests provide experimental support and guidance for new developments in DA.

The typical set-up is a synthetic (twin) experiment, where you

  • specify a
    • dynamic model
    • observational model
  • use these to generate a synthetic
    • "truth"
    • and observations thereof
  • assess how different DA methods perform in estimating the truth, given the above starred (
    ) items.

Docs Travis Coveralls pre-commit PyPI - Version PyPI - Downloads <!-- Inspiration: It seems is overloaded at the moment -->


DAPPER enables the numerical investigation of DA methods through a variety of typical test cases and statistics. It (a) reproduces numerical benchmarks results reported in the literature, and (b) facilitates comparative studies, thus promoting the (a) reliability and (b) relevance of the results. For example, this figure is generated by

and is a reproduction from this book on DA.

Comparative benchmarks with Lorenz'96 plotted as a function of the ensemble size (N)

DAPPER is (c) open source, written in Python, and (d) focuses on readability; this promotes the (c) reproduction and (d) dissemination of the underlying science, and makes it easy to adapt and extend. It also comes with a battery of diagnostics and statistics, and live plotting (on-line with the assimilation) facilities, including pause/inspect options, as illustrated below

EnKF - Lorenz'63

In summary, it is well suited for teaching and fundamental DA research. Also see its drawbacks.


Works on Linux/Windows/Mac.

Prerequisite: Python>=3.8

If you're not an admin or expert:

  • Install Anaconda.
  • Open the Anaconda terminal and run the following commands:

    conda create --yes --name my-env python=3.8 conda activate my-env python -c 'import sys; print("Version:", sys.version.split()[0])'

Ensure the output at the end gives a version bigger than 3.8.
Keep using the same terminal for the commands below.


Either: Install as library

Do you just want to run a script that requires DAPPER? Then

  • If the script comes with a
    file, then do
    pip install -r path/to/requirements.txt
  • If not, hopefully you know the version of DAPPER needed. Run
    pip install DA-DAPPER==1.0.0
    to get version
    (as an example).

Or: Install for development

Do you want the DAPPER code available play around with? Then

  • Download and unzip (or
    git clone
    ) DAPPER.
  • Move the resulting folder wherever you like,
    into it (ensure you're in the folder with a
  • pip install -e .[dev]

    You can omit
    if you don't need to do serious development.

Finally: Test the installation

You should now be able to do run your script with

python path/to/
For example, if you are in the DAPPER dir,
python examples/

If you've closed the terminal (or shut down your computer), you first need to open the (anaconda) terminal and run this:

conda activate my-env


Read, run, and understand the scripts

. Then, get familiar with the code.

The documentation provides more information, and the API reference.

Alternatively, DA-tutorials provides a python-based introduction to DA.

DA methods


Literature reproduced
EnKF 1 Sakov08, Hoteit15
EnKF-N Bocquet12, Bocquet15
EnKS, EnRTS Raanes2016
iEnKS / iEnKF / EnRML / ES-MDA 2 Sakov12, Bocquet12, Bocquet14
LETKF, local & serial EAKF Bocquet11
Sqrt. model noise methods Raanes2014
Particle filter (bootstrap) 3 Bocquet10
Optimal/implicit Particle filter 3 Bocquet10
NETF Tödter15, Wiljes16
Rank histogram filter (RHF) Anderson10
Extended KF
Optimal interpolation
<!-- markdownlint-restore -->

1: Stochastic, DEnKF (i.e. half-update), ETKF (i.e. sym. sqrt.). Serial forms are also available.
Tuned with inflation and "random, orthogonal rotations".
2: Also supports the bundle version, and "EnKF-N"-type inflation.
3: Resampling: multinomial (including systematic/universal and residual).
The particle filter is tuned with "effective-N monitoring", "regularization/jittering" strength, and more.

For a list of ready-made experiments with suitable, tuned settings for a given method (e.g. the

), use gnu's grep:
cd dapper/mods
grep -r "iEnKS.*("

Test cases (models)


Lin? TLM+? PDE? Phys.dim. State len Lyap≥0 Implementer
Linear Advect. (LA) Yes Yes Yes 1d 1000 * 51 Evensen/Raanes
DoublePendulum No Yes No 0d 4 2 Matplotlib/Raanes
Ikeda No Yes No 0d 2 1 Raanes
LotkaVolterra No Yes No 0d 5 * 1 Wikipedia/Raanes
Lorenz63 No Yes "Yes" 0d 3 2 Sakov
Lorenz84 No Yes No 0d 3 2 Raanes
Lorenz96 No Yes No 1d 40 * 13 Raanes
LorenzUV No Yes No 2x 1d 256 + 8 * ≈60 Raanes
Kuramoto-Sivashinsky No Yes Yes 1d 128 * 11 Kassam/Raanes
Quasi-Geost (QG) No No Yes 2d 129²≈17k ≈140 Sakov

  • *
    : Flexible; set as necessary
  • +
    : Tangent Linear Model included?

The models are found as subdirectories within

. A model should be defined in a file named
, and illustrated by a file named
. Most other files within a model subdirectory are usually named
and define a
object, which holds the settings of a specific twin experiment, using that model, as detailed in the corresponding author/year's paper. At the bottom of each such file should be (in comments) a list of suitable, tuned settings for various DA methods, along with their expected, average rmse.a score for that experiment. The complete list of included experiment files can be obtained with gnu's
cd dapper/mods
find . -iname '[a-z]*[0-9]*.py'

Some of these files contain settings that have been used in several papers. As mentioned above, DAPPER reproduces literature results. There are also results in the literature that DAPPER does not reproduce. Typically, this means that the published results are incorrect.

Similar projects

DAPPER is aimed at research and teaching (see discussion up top). Example of limitations:

  • It is not suited for very big models (>60k unknowns).
  • Time-dependent error covariances and changes in lengths of state/obs (although the Dyn and Obs models may otherwise be time-dependent).
  • Non-uniform time sequences not fully supported.

Also, DAPPER comes with no guarantees/support. Therefore, if you have an operational (real-world) application, such as WRF, you should look into one of the alternatives, sorted by approximate project size.


Developers Purpose (approximately)
DART NCAR Operational, general
PDAF AWI Operational, general
JEDI JCSDA (NOAA, NASA, ++) Operational, general (in develpmt?)
ERT Statoil Operational, history matching (Petroleum)
OpenDA TU Delft Operational, general
Verdandi INRIA Biophysical DA
PyOSSE Edinburgh, Reading Earth-observation DA
SANGOMA Conglomerate* Unify DA research
EMPIRE Reading (Met) Research (high-dim)
MIKE DHI Oceanographic. Commercial?
OAK Liège Oceaonagraphic
Siroco OMP Oceaonagraphic
FilterPy R. Labbe Engineering, general intro to Kalman filter
DASoftware Yue Li, Stanford Matlab, large-scale
Pomp U of Michigan R, general state-estimation
PyIT CIPR Real-world petroleum DA (?)
EnKF-Matlab Sakov Matlab, personal publications and intro
EnKF-C Sakov C, light-weight EnKF, off-line
pyda Hickman Python, personal publications
Datum Raanes Matlab, personal publications
IEnKS code Bocquet Python, personal publications


codes have been inspirational in the development of DAPPER.

*: AWI/Liege/CNRS/NERSC/Reading/Delft


Patrick N. Raanes, Colin Grudzien, Maxime Tondeur, Remy Dubois

If you use this software in a publication, please cite as follows.

  author = {Patrick N. Raanes and others},
  title  = {nansencenter/DAPPER: Version 0.8},
  month  = December,
  year   = 2018,
  doi    = {10.5281/zenodo.2029296},
  url    = {}


DAPPER is developed and maintained at NORCE (Norwegian Research Institute) and the Nansen Environmental and Remote Sensing Center


Publication list

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