Lorenz-96 model with PDAF

The implementation of the Loren-96 model coupled to PDAF is in the directory testsuite/src/lorenz96 of the PDAF package. Provided is a full implementation of PDAF with the nonlinear Lorenz96 model (E. N. Lorenz (1996) Predictability - a problem partly solved. Proceedings Seminar on Predictability, ECMWF, READING, UK) providing various filter and smoother methods. We used this implementation for the different publications in which we studies the behavior of different data assimilation methods.

Next to the implementation of Lorenz-96 with PDAF, the test case provides tool programs and scripts that allow to run a test case and to display the outputs.

Running the test case

Runnning a data assimilation experimnet with the Lorenz-96 model is a two step process: First one runs the model without PDAF to generate a file holding the trajectory of a forward run. Then one generates files with observations and a covariance matrix for the initialization of the initial ensemble. In the second step, one compiles the Lorenz-96 with activated coupling to PDAF and runs the experiments.

1. Compile and run the forward model without assimilation

First change in the file make.arch/linux_gfortran.h the line

CPP_DEFS = -DUSE_PDAF

to

CPP_DEFS = #-DUSE_PDAF

to deactivate the coupling to PDAF in the model code. Now build the forward model program with

  cd testsuite/src
  make lorenz_96 PDAF_ARCH=linux_gfortran

in the directory testsuite/src/ of the PDAF package. You have to ensure that in the machine-specific make include file linux_gfortran.h -DUSE_PDAF is not defined for CPP_DEFS (such that calls to PDAF are not active). The executable is generated in testsuite/bin/.

Note: The implementation uses the NetCDF library for file outputs. If the compilation above fails, please ensure the netcdf-library ist installed. On computers running Linux, it is usually available as a package of the operating system.

To run the forward model use

  cd ../bin
  ./lorenz_96 -total_steps 10000

This runs Lorenz-96 model for 10000 time steps and the trajectory is written into a file state.nc.

2. Generate observations and a covariance matrix

To build the executables for the tool programs use

  cd ../src/lorenz96/tools
  make all PDAF_ARCH=linux_gfortran

Now run

  ./generate_obs

and

  ./generate_covar

to generate a file holding observations (obs.nc in testsuite/bin/) and a file holding the covariance matrix information (covar.nc in testsuite/bin/), which is used to generate an initial ensemble for the data assimilation experiments.

3. Build and run the assimilation program

Change in the make.arch/linux_gfortran.h the line

CPP_DEFS = #-DUSE_PDAF

back to

CPP_DEFS = -DUSE_PDAF

to activate the coupling to PDAF in the model code.

Now compile the Lorenz96 model with activated PDAF. First clean the directories for the main driver and the lorenz96

model using

  cd ../../
  make cleandriver PDAF_ARCH=linux_gfortran
  make cleanlorenz_96 PDAF_ARCH=linux_gfortran

(This removes object files that were compiled without support for PDAF) Then build the executable using

  make pdaf_lorenz_96 PDAF_ARCH=linux_gfortran

The program pdaf_lorenz_96 is generated in testsuite/bin.

To run the assimilation program, do

  cd ../bin
  ../src/lorenz96/tools/runasml.sh 

The script runsasml.sh runs 11 experiments with a fixed ensemble size, but different covariance inflations (forgetting factors). The execution can take about 2 minutes.

4. Plot output from the assimilation experiments

To display the output of the assimilation experiments we provide several plotting scripts for Matlab and Octave. To use them do

cd ../src/lorenz96/plotting/

and see the file README there for a description of the available plotting scripts.

For example,

plot_example.m

plots the true and estimated mean RMS errors as a function of the forgetting factor. For the usage of the other functions, use 'help' in Matlab.