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Description

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 and an observational model, and use these to generate a synthetic truth (multivariate time series), and then estimate that truth given the models and noisy observations.

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Getting started

Install, then read, run and try to understand

examples/basic_{1,2,3}.py
. Some of the examples can also be opened in Jupyter, and thereby run in the cloud (i.e. without installation, but requiring Google login): Open In Collab. This screencast provides an introduction. The documentation includes general guidelines and the API, but for any serious use you will want to read and adapt the code yourself. If you use it in a publication, please cite, e.g., The experiments used (inspiration from) DAPPER [ref], version 1.2.1, where [ref] points to DOI. Lastly, for an introduction to DA theory also using Python, see these tutorials.

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>=3.7

If you're an expert, setup a python environment however you like. Otherwise: Install Anaconda, then open the Anaconda terminal and run the following commands:

conda create --yes --name dapper-env python=3.8
conda activate dapper-env
python --version

Ensure the printed version is 3.7 or more.
Keep using the same terminal for the commands below.

Install

Either: Install for development (recommended)

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.

Or: 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 dapper==1.0.0
    to get version
    1.2.3
    (as an example).

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 dapper-env

DA methods

Method

Literature reproduced
EnKF 1 Sakov08, Hoteit15, Grudzien2020
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
<!-- 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

iEnKS
), use:
grep -r "xp.*iEnKS" dapper/mods

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
Lorenz96s No Yes No 1d 10 * 4 Grudzien
LorenzUV No Yes No 2x 1d 256 + 8 * ≈60 Raanes
LorenzIII No No No 1d 960 * ≈164 Raanes
Vissio-Lucarini 20 No Yes No 1d 36 * 10 Yumeng
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. A list of these files can be obtained using
find dapper/mods -iname '[a-z]*[0-9]*.py'

Some files contain settings used by several papers. Moreover, 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. 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 or 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 General
PDAF AWI General
JEDI JCSDA (NOAA, NASA, ++) General
OpenDA TU Delft General
EMPIRE Reading (Met) General
ERT Statoil History matching (Petroleum DA)
PIPT CIPR History matching (Petroleum DA)
MIKE DHI Oceanographic
OAK Liège Oceanographic
Siroco OMP Oceanographic
Verdandi INRIA Biophysical DA
PyOSSE Edinburgh, Reading Earth-observation DA

Below is a list of projects with a purpose more similar to DAPPER's (research in DA, and not so much using DA):

Name

Developers Notes
DAPPER Raanes, Chen, Grudzien Python
SANGOMA Conglomerate* Fortran, Matlab
hIPPYlib Villa, Petra, Ghattas Python, adjoint-based PDE methods
FilterPy R. Labbe Python. Engineering oriented.
DASoftware Yue Li, Stanford Matlab. Large inverse probs.
Pomp U of Michigan R
EnKF-Matlab Sakov Matlab
EnKF-C Sakov C. Light-weight, off-line DA
pyda Hickman Python
PyDA Shady-Ahmed Python
DasPy Xujun Han Python
DataAssim.jl Alexander-Barth Julia
DataAssimilationBenchmarks.jl Grudzien Julia, Python
Datum Raanes Matlab
IEnKS code Bocquet Python

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

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, the UK National Centre for Earth Observation (NCEO), and the University of Nevada, Reno.

NORCE NERSC <!-- markdownlint-restore -->

Publication list

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