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. Example diagnostics:

The typical set-up is a 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.

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

Installation

Works on Linux/Windows/Mac.

1. Prerequisite: python>=3.6.
   If you're not {admin | expert}:
   1a. Install it with Anaconda.
   1b. Use the Anaconda terminal to run the commands below.

2. Install:
   Download and extract (or

   git clone

   ) DAPPER,
   

   cd

   into the resulting folder (ensure you're at the level with a

   setup.py

   file):
   

   pip install -e .

   (don't forget the

   .

   ).

3. Test by running:

   python example_1.py

Step 2 can be replaced by running

pip install da-dapper

If the installation fails, you probably need to create a new Python environment.

Getting started

Read, run, and understand the scripts

example_{1,2,3}.py

The docs provide processed docstrings, but are far from complete.

Alternatively, see DA-tutorials for an intro to DA.

Methods

References

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.

Models

*: flexible; set as necessary

Other reproductions

As mentioned above, DAPPER reproduces literature results. There are also plenty of results in the literature that DAPPER does not reproduce. Typically, this means that the published results are incorrect.

A list of experimental settings that can be compared with literature papers can be obtained using gnu's

find

        $ find . -iname "[a-z]*[0-9].py" | grep mods

Some of these files contain settings that have been used in several papers.

Alternative projects

DAPPER is aimed at research and teaching (see discussion on 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.

The

EnKF-Matlab

IEnKS

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

Contributors

Patrick N. Raanes, 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}
}

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

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