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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

Highlights

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 examples/basic_3.py and is a reproduction of figure 5.7 of these lecture notes.

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.

Installation

Works on Linux/Windows/Mac.

Prerequisite: Python>=3.8

If you're not admin and 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.

Install

Either: Install as library

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

  • If the script comes with a requirements.txt 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 1.0.0 (as an example).

Or: Install for development

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

  • Download and unzip (or git clone) DAPPER.
  • Move the resulting folder wherever you like,
    and cd into it (ensure you're in the folder with a setup.py file).
  • pip install -e .[dev]
    You can omit [dev] 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/script.py.
For example, if you are in the DAPPER dir,

python examples/basic_1.py

PS: If you closed the terminal (or shut down your computer), you'll first need to run conda activate my-env

Quickstart

Read, run, and understand the scripts examples/basic_{1,2,3}.py. 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

Method 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
4D-Var
3D-Var
Extended KF
Optimal interpolation
Climatology

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 iEnKS), use gnu's grep:

cd dapper/mods
grep -r "iEnKS.*("

Test cases (models)

Model 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
Vissio-Lucarino 20 No Yes No 1d 36 * 10 Yumeng
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 dapper/mods. A model should be defined in a file named __init__.py, and illustrated by a file named demo.py. Most other files within a model subdirectory are usually named authorYEAR.py and define a HMM 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 find:

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. You will also find results that were not reproduced by DAPPER.

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.

The scope of DAPPER is restricted because

framework_to_language

Moreover, even straying beyond basic configurability appears unrewarding when already building on a high-level language such as Python. Indeed, you may freely fork and modify the code of DAPPER, which should be seen as a set of templates, and not a framework.

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.

Name 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
PyDA Shady-Ahmed Python, Academic, research
DasPy Xujun Han Python, Land applications
Datum Raanes Matlab, personal publications
IEnKS code Bocquet Python, personal publications

The EnKF-Matlab and IEnKS codes have been inspirational in the development of DAPPER.

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

Contributors

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

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

@misc{raanes2018dapper,
  author = {Patrick N. Raanes and others},
  title  = {nansencenter/DAPPER: Version 0.8},
  month  = December,
  year   = 2018,
  doi    = {10.5281/zenodo.2029296},
  url    = {https://doi.org/10.5281/zenodo.2029296}
}

DOI

DAPPER is developed and maintained at NORCE (Norwegian Research Institute) and the Nansen Environmental and Remote Sensing Center (NERSC), in collaboration with the University of Reading and the UK National Centre for Earth Observation (NCEO)

NORCE NERSC

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