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.

Getting started

Install, then read, run and try to understand

examples/basic_{1,2,3}.py

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

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

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

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

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

grep -r "xp.*iEnKS" dapper/mods

Test cases (models)

• *

  : Flexible; set as necessary

• **

  : Tangent Linear Model included?

The models are found as subdirectories within

dapper/mods

__init__.py

demo.py

authorYEAR.py

HMM

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

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

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.

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

The

EnKF-Matlab

IEnKS

*

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.

<!-- markdownlint-restore -->

Publication list

• https://www.geosci-model-dev-discuss.net/gmd-2019-136/
• https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3386
• https://www.nonlin-processes-geophys-discuss.net/npg-2019-10
• https://link.springer.com/article/10.1007/s11004-021-09937-x
• https://arxiv.org/abs/2010.07063