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DART "jamaica release" Documentation

$Id: Jamaica_release.html 6671 2013-12-11 20:41:41Z nancy $

Overview of DART

The Data Assimilation Research Testbed (DART) is designed to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to allow increased understanding of all three. The DART programs have been compiled with several (many?) Fortran 90 compilers and run on linux compute-servers, linux clusters, OSX laptops/desktops, SGI Altix clusters, supercomputers running AIX ... a pretty broad range, really. You should definitely read the Customizations section.

DART employs a modular programming approach to apply an Ensemble Kalman Filter which nudges models toward a state that is more consistent with information from a set of observations. Models may be swapped in and out, as can different algorithms in the Ensemble Kalman Filter. The method requires running multiple instances of a model to generate an ensemble of states. A forward operator appropriate for the type of observation being used is applied to each of the states to generate the model's estimate of the observation. Comparing these estimates and their uncertainty to the observation and its uncertainty ultimately results in the adjustments to the model states. There's much more to it, described in detail in the tutorial directory of the package.

DART ultimately creates a few netCDF files containing the model states just before the adjustment (Prior_Diag.nc) and just after the adjustment (Posterior_Diag.nc) as well as a file obs_seq.final with the model estimates of the observations. There is a suite of Matlab® functions that facilitate exploration of the results, but the netCDF files are inherently portable and contain all the necessary metadata to interpret the contents.

What's New

download "jamaica" -- the latest release

The jamaica release has been tested on several supercomputers, including the NCAR IBM machines 'bluevista' and 'blueice', the SGI Altix machines 'columbia' and 'origin'. There are many more changes - much more thoroughly described - in the Jamaica_diffs_from_I.html document. What follows here is only meant to draw attention to the fact that experienced users should really read the change document.

The most visible changes in the jamaica release are:

Changes in the inflation strategies.

The changes in the inflation mechanism now provide the ability to perform inflation in observation-space, fixed-state-space, or spatially-varying-state-space in the prior and/or posterior with either a deterministic or stochastic algorithm.

Fundamental changes for parallel behavior

The optional use of MPI required a complete rewrite of many of the routines that previously governed attempts at parallel computations. This resulted in the removal of the async = 3 method of achieving parallel assimilations and model advances. As a consequence, assim_region.csh, advance_ens.csh, and filter_server.csh no longer exist.

Some assimilation strategies previously controlled by assim_tools_nml, (namely, whether to perform a parallel assimilation, how many domains to use, and a few others) have been replaced by a new parallel assimilation algorithm. Associated inflation computations are documented in the manuscript by Anderson and Collins that is to appear in the Journal of Atmospheric and Oceanic Technology and in section 22 of the tutorial. Namelist entries do_parallel, num_domains, and parallel_command no longer exist.

The (optional) MPI implementation allows for very large state vectors - some model configurations (i.e. state vectors too large to fit in core) are now possible. And yes, it scales rather nicely, thank you. There are references throughout the documents and code to MPI - keep in mind all the MPI interfaces are in one module mpi_utilities_mod.f90. If you are NOT using MPI - there is a simple equivalent module named null_mpi_utilities_mod.f90 which is the default.

Using MPI greatly increases the performance for large models and/or large numbers of observations. It also greatly decreases the shell script complexity. The use of MPI is enabled by giving mkmf_filter an optional argument -mpi . To learn more about DART's implementation of MPI - please refer to DART/doc/html/mpi_intro.html .

Vertical localization

It is now possible to perform localization in the vertical in 3D models, which necessitated a change in the model_mod interface. The model_mod:get_close_states() and model_mod:get_num_close_states() routines have been replaced by "pass-through" interfaces to routines in location_mod. An optional interface model_mod:ens_mean_for_model() is required only by some models when performing localization in the vertical. This routine provides the ensemble mean so that the same model state is used when computing the vertical localization.

3D plots of observation locations & type

It's more than just eye candy. It is also possible to plot (in 3D) the locations of observations by processing an observation sequence through obs_diag and running the resulting output file through the matlab function plot_observation_locations.

What's Required

  1. a working fortran compiler
  2. a working F90 interface to the netCDF libraries
  3. something that can run 'csh'

DART has been tested on many Fortran compilers and platforms. Our philosophy seems to be to burn away the 'impurities' by compiling on any architecture we can get our hands on. We completely buy into the use of the Fortran 'kind' strategy to declare variable types rather than relying on compiler autopromotion flags - which are a BAD idea in my opinion. There are some models in DART that come from outside developers and _may_ contain code segments that require the use of some special compiler flags - we are not so draconian as to mandate the use of the Fortran 'kind'. In general, the low-order models and all common portions of the DART code compile very cleanly.

DART uses the netCDF self-describing data format with a particular metadata convention to describe output that is used to analyze the results of assimilation experiments. These files have the extension .nc and can be read by a number of standard data analysis tools.

Since most (all?) of the models envisioned being used by DART are written in Fortran and run on various UNIX or *nix platforms, the development environment for DART is highly skewed to these machines. We do most of our development on a small linux workstation and a mac laptop running OSX 10.4.9 - but have an extensive test network. I've never built nor run DART on a Windows machine - so I don't even know if it's possible. If you have run it (under Cygwin?) please let me know how it went -- I'm curious.

What's nice to have

ncview: DART users have used ncview to create graphical displays of output data fields. The 2D rendering is good for 'quick-look' type uses, but I wouldn't want to publish with it.

NCO: The NCO tools are able to perform operations on netCDF files like concatenating, slicing, and dicing.

Matlab® A set of Matlab® scripts, designed to produce graphical diagnostics from DART netCDF output files are also part of the DART project.

MPI: The latest release of the DART system includes an MPI option. MPI stands for 'Message Passing Interface', and is both a library and run-time system that enables multiple copies of a single program to run in parallel, exchange data, and combine to solve a problem more quickly. The latest release of DART does *NOT* require MPI to run; the default build scripts do not need nor use MPI in any way. However, for larger models with large state vectors and large numbers of observations, the data assimilation step will run much faster in parallel, which requires MPI to be installed and used. However, if multiple ensembles of your model fit comfortably (in time and memory space) on a single processor, you need read no further about MPI.

Where's the (GUI) interface?

DART programs can require three different types of input. First, some of the DART programs, those for creating synthetic observational datasets, require interactive input from the keyboard. For simple cases, this interactive input can be made directly from the keyboard. In more complicated cases, a file containing the appropriate keyboard input can be created and this file can be directed to the standard input of the DART program. Second, many DART programs expect one or more input files in DART specific formats to be available. For instance, perfect_model_obs, which creates a synthetic observation set given a particular model and a description of a sequence of observations, requires an input file that describes this observation sequence. At present, the observation files for DART are in a custom format in either human-readable ascii or more compact machine-specific binary. Third, many DART modules (including main programs) make use of the Fortan90 namelist facility to obtain values of certain parameters at run-time. All programs look for a namelist input file called input.nml in the directory in which the program is executed. The input.nml file can contain a sequence of individual Fortran90 namelists which specify values of particular parameters for modules that compose the executable program. DART provides a mechanism that automatically generates namelists with the default values for each program to be run.

Document conventions

Anything underlined is a URL.

All filenames look like this -- (typewriter font, green).
Program names look like this -- (italicized font, green).
user input looks like this -- (bold, magenta).

commands to be typed at the command line are contained in an indented gray box.

And the contents of a file are enclosed in a box with a border:

&hypothetical_nml
  obs_seq_in_file_name = "obs_seq.in",
  obs_seq_out_file_name = "obs_seq.out",
  init_time_days = 0,
  init_time_seconds = 0,
  output_interval = 1
&end

Installation

This document outlines the installation of the DART software and the system requirements. The entire installation process is summarized in the following steps:

  1. Determine which F90 compiler is available.
  2. Determine the location of the netCDF library.
  3. Download the DART software into the expected source tree.
  4. Modify certain DART files to reflect the available F90 compiler and location of the appropriate libraries.
  5. Build the executables.

We have tried to make the code as portable as possible, but we do not have access to all compilers on all platforms, so there are no guarantees. We are interested in your experience building the system, so please email me (Tim Hoar) thoar 'at' ucar 'dot' edu (trying to cut down on the spam).

After the installation, you might want to peruse the following.

You should absolutely take the tutorial in the DART/tutorial directory.


Requirements: an F90 Compiler

The DART software has been successfully built on several Linux/x86 platforms with several versions of the Intel Fortran Compiler for Linux, which (at one point) is/was free for individual scientific use. It has also been built and successfully run with several versions of each of the following: Portland Group Fortran Compiler, Lahey Fortran Compiler, Pathscale Fortran Compiler, GNU Fortran 95 Compiler ("gfortran"), Absoft Fortran 90/95 Compiler (Mac OSX). Since recompiling the code is a necessity to experiment with different models, there are no binaries to distribute.


Requirements: the netCDF library

DART uses the netCDF self-describing data format for the results of assimilation experiments. These files have the extension .nc and can be read by a number of standard data analysis tools. In particular, DART also makes use of the F90 interface to the library which is available through the netcdf.mod and typesizes.mod modules. IMPORTANT: different compilers create these modules with different "case" filenames, and sometimes they are not both installed into the expected directory. It is required that both modules be present. The normal place would be in the netcdf/include directory, as opposed to the netcdf/lib directory.

If the netCDF library does not exist on your system, you must build it (as well as the F90 interface modules). The library and instructions for building the library or installing from an RPM may be found at the netCDF home page: http://www.unidata.ucar.edu/packages/netcdf/ Pay particular attention to the compiler-specific patches that must be applied for the Intel Fortran Compiler. (Or the PG compiler, for that matter.)

The location of the netCDF library, libnetcdf.a, and the locations of both netcdf.mod and typesizes.mod will be needed by the makefile template, as described in the compiling section.


Downloading the distribution.

HURRAY! The DART source code is now distributed through an anonymous Subversion server! The big advantage is the ability to patch or update existing code trees at your discretion. Subversion (the client-side app is 'svn') allows you to compare your code tree with one on a remote server and selectively update individual files or groups of files. Furthermore, now everyone has access to any version of any file in the project, which is a huge help for developers. I have a brief summary of the svn commands I use most posted at: http://www.image.ucar.edu/~thoar/svn_primer.html

The resources to develop and support DART come from our ability to demonstrate our growing user base. We ask that you register at our download site http://www.image.ucar.edu/DAReS/DART/DART_download and promise that the information will only be used to notify you of new DART releases and shown to our sponsers in an aggregated form: "Look - we have three users from Tonawanda, NY". After filling in the form, you will be directed to a website that has instructions on how to download the code.

svn has adopted the strategy that "disk is cheap". In addition to downloading the code, it downloads an additional copy of the code to store locally (in hidden .svn directories) as well as some administration files. This allows svn to perform some commands even when the repository is not available. It does double the size of the code tree ... so the download is something like 480MB -- pretty big. BUT - all future updates are (usually) just the differences, so they happen very quickly.

If you follow the instructions on the download site, you should wind up with a directory named DART. Compiling the code in this tree (as is usually the case) will necessitate much more space.

If you cannot use svn, just let me know and I will create a tar file for you. svn is so superior to a tar file that a tar file should be considered a last resort.

The code tree is very "bushy"; there are many directories of support routines, etc. but only a few directories involved with the customization and installation of the DART software. If you can compile and run ONE of the low-order models, you should be able to compile and run ANY of the low-order models. For this reason, we can focus on the Lorenz `63 model. Subsequently, the only directories with files to be modified to check the installation are:  DART/mkmf,  DART/models/lorenz_63/work, and  DART/matlab (but only for analysis).


Customizing the build scripts -- Overview.

DART executable programs are constructed using two tools: make and mkmf. The make utility is a very common piece of software that requires a user-defined input file that records dependencies between different source files. make then performs a hierarchy of actions when one or more of the source files is modified. The mkmf utility is a custom preprocessor that generates a make input file (named Makefile) and an example namelist input.nml.program_default with the default values. The Makefile is designed specifically to work with object-oriented Fortran90 (and other languages) for systems like DART.

mkmf requires two separate input files. The first is a `template' file which specifies details of the commands required for a specific Fortran90 compiler and may also contain pointers to directories containing pre-compiled utilities required by the DART system. This template file will need to be modified to reflect your system. The second input file is a `path_names' file which includes a complete list of the locations (either relative or absolute) of all Fortran90 source files that are required to produce a particular DART program. Each 'path_names' file must contain a path for exactly one Fortran90 file containing a main program, but may contain any number of additional paths pointing to files containing Fortran90 modules. An mkmf command is executed which uses the 'path_names' file and the mkmf template file to produce a Makefile which is subsequently used by the standard make utility.

Shell scripts that execute the mkmf command for all standard DART executables are provided as part of the standard DART software. For more information on mkmf see the FMS mkmf description.
One of the benefits of using mkmf is that it also creates an example namelist file for each program. The example namelist is called input.nml.program_default, so as not to clash with any exising input.nml that may exist in that directory.

Building and Customizing the 'mkmf.template' file

A series of templates for different compilers/architectures exists in the DART/mkmf/ directory and have names with extensions that identify the compiler, the architecture, or both. This is how you inform the build process of the specifics of your system. Our intent is that you copy one that is similar to your system into mkmf.template and customize it. For the discussion that follows, knowledge of the contents of one of these templates (i.e. mkmf.template.gfortran) is needed. Note that only the LAST lines are shown here, the head of the file is just a big comment (worth reading, btw).

...
MPIFC = mpif90
MPILD = mpif90
FC = gfortran
LD = gfortran
NETCDF = /usr/local
INCS = ${NETCDF}/include
FFLAGS = -O2 -I$(INCS)
LIBS = -L${NETCDF}/lib -lnetcdf
LDFLAGS = -I$(INCS) $(LIBS)

Essentially, each of the lines defines some part of the resulting Makefile. Since make is particularly good at sorting out dependencies, the order of these lines really doesn't make any difference. The FC = gfortran line ultimately defines the Fortran90 compiler to use, etc. The lines which are most likely to need site-specific changes start with FFLAGS and NETCDF, which indicate where to look for the netCDF F90 modules and the location of the netCDF library and modules.

If you have MPI installed on your system MPIFC, MPILD dictate which compiler will be used in that instance. If you do not have MPI, these variables are of no consequence.

NETCDF

Modifying the NETCDF value should be relatively straightforward.
Change the string to reflect the location of your netCDF installation containing netcdf.mod and typesizes.mod. The value of the NETCDF variable will be used by the FFLAGS, LIBS, and LDFLAGS variables.

FFLAGS

Each compiler has different compile flags, so there is really no way to exhaustively cover this other than to say the templates as we supply them should work -- depending on the location of your netCDF. The low-order models can be compiled without a -r8 switch, but the bgrid_solo model cannot.

Customizing the 'path_names_*' file

Several path_names_* files are provided in the work directory for each specific model, in this case: DART/models/lorenz_63/work. Since each model comes with its own set of files, the path_names_* files need no customization.

The Tutorial.

The DART/tutorial documents are an excellent way to kick the tires on DART and learn about ensemble data assimilation. If you have correctly configured your mkmf.template, you can run anything in the tutorial.


Building the Lorenz_63 DART project.

Currently, DART executables are constructed in a work subdirectory under the directory containing code for the given model. In the top-level DART directory, change to the L63 work directory and list the contents:

cd DART/models/lorenz_63/work
ls -1

With the result:

Posterior_Diag.nc
Prior_Diag.nc
True_State.nc
filter_ics
filter_restart
input.nml
mkmf_create_fixed_network_seq
mkmf_create_obs_sequence
mkmf_filter
mkmf_merge_obs_seq
mkmf_obs_diag
mkmf_perfect_model_obs
mkmf_preprocess
mkmf_wakeup_filter
obs_seq.final
obs_seq.in
obs_seq.out
obs_seq.out.average
obs_seq.out.x
obs_seq.out.xy
obs_seq.out.xyz
obs_seq.out.z
path_names_create_fixed_network_seq
path_names_create_obs_sequence
path_names_filter
path_names_merge_obs_seq
path_names_obs_diag
path_names_perfect_model_obs
path_names_preprocess
path_names_wakeup_filter
perfect_ics
perfect_restart
set_def.out
workshop_setup.csh

There are eight mkmf_xxxxxx files for the programs

  1. preprocess,
  2. create_obs_sequence,
  3. create_fixed_network_seq,
  4. perfect_model_obs,
  5. filter,
  6. wakeup_filter,
  7. merge_obs_seq, and
  8. obs_diag,

along with the corresponding path_names_xxxxxx files. There are also files that contain initial conditions, netCDF output, and several observation sequence files, all of which will be discussed later. You can examine the contents of one of the path_names_xxxxxx files, for instance path_names_filter, to see a list of the relative paths of all files that contain Fortran90 modules required for the program filter for the L63 model. All of these paths are relative to your DART directory. The first path is the main program (filter.f90) and is followed by all the Fortran90 modules used by this program (after preprocessing).

The mkmf_xxxxxx scripts are cryptic but should not need to be modified -- as long as you do not restructure the code tree (by moving directories, for example). The only function of the mkmf_xxxxxx script is to generate a Makefile and an input.nml.program_default file. It is not supposed to compile anything -- make does that:

csh mkmf_preprocess
make

The first command generates an appropriate Makefile and the input.nml.preprocess_default file. The second command results in the compilation of a series of Fortran90 modules which ultimately produces an executable file: preprocess. Should you need to make any changes to the DART/mkmf/mkmf.template, you will need to regenerate the Makefile.

The preprocess program actually builds source code to be used by all the remaining modules. It is imperative to actually run preprocess before building the remaining executables. This is how the same code can assimilate state vector 'observations' for the Lorenz_63 model and real radar reflectivities for WRF without needing to specify a set of radar operators for the Lorenz_63 model!

preprocess reads the &preprocess_nml namelist to determine what observations and operators to incorporate. For this exercise, we will use the values in input.nml. preprocess is designed to abort if the files it is supposed to build already exist. For this reason, it is necessary to remove a couple files (if they exist) before you run the preprocessor. It is just a good habit to develop.

\rm -f ../../../obs_def/obs_def_mod.f90
\rm -f ../../../obs_kind/obs_kind_mod.f90
./preprocess
ls -l ../../../obs_def/obs_def_mod.f90
ls -l ../../../obs_kind/obs_kind_mod.f90

This created ../../../obs_def/obs_def_mod.f90 from ../../../obs_kind/DEFAULT_obs_kind_mod.F90 and several other modules. ../../../obs_kind/obs_kind_mod.f90 was created similarly. Now we can build the rest of the project.

A series of object files for each module compiled will also be left in the work directory, as some of these are undoubtedly needed by the build of the other DART components. You can proceed to create the other programs needed to work with L63 in DART as follows:

csh mkmf_create_obs_sequence
make
csh mkmf_create_fixed_network_seq
make
csh mkmf_perfect_model_obs
make
csh mkmf_filter
make
csh mkmf_obs_diag
make

The result (hopefully) is that six executables now reside in your work directory. The most common problem is that the netCDF libraries and include files (particularly typesizes.mod) are not found. Edit the DART/mkmf/mkmf.template, recreate the Makefile, and try again.

programpurpose
preprocess creates custom source code for just the observations of interest
create_obs_sequence specify a (set) of observation characteristics taken by a particular (set of) instruments
create_fixed_network_seq specify the temporal attributes of the observation sets
perfect_model_obs spinup, generate "true state" for synthetic observation experiments, ...
filter perform experiments
obs_diag creates observation-space diagnostic files to be explored by the Matlab® scripts.
merge_obs_sequence manipulates observation sequence files. It is not generally needed (particularly for low-order models) but can be used to combine observation sequences or convert from ASCII to binary or vice-versa. Since this is a specialty routine - we will not cover its use in this document.
wakeup_filter is only needed for MPI applications. We're starting at the beginning here, so we're going to ignore this one, too.

Running Lorenz_63.

This initial sequence of exercises includes detailed instructions on how to work with the DART code and allows investigation of the basic features of one of the most famous dynamical systems, the 3-variable Lorenz-63 model. The remarkable complexity of this simple model will also be used as a case study to introduce a number of features of a simple ensemble filter data assimilation system. To perform a synthetic observation assimilation experiment for the L63 model, the following steps must be performed (an overview of the process is given first, followed by detailed procedures for each step):

Experiment Overview

  1. Integrate the L63 model for a long time
    starting from arbitrary initial conditions to generate a model state that lies on the attractor. The ergodic nature of the L63 system means a 'lengthy' integration always converges to some point on the computer's finite precision representation of the model's attractor.

  2. Generate a set of ensemble initial conditions
    from which to start an assimilation. Since L63 is ergodic, the ensemble members can be designed to look like random samples from the model's 'climatological distribution'. To generate an ensemble member, very small perturbations can be introduced to the state on the attractor generated by step 1. This perturbed state can then be integrated for a very long time until all memory of its initial condition can be viewed as forgotten. Any number of ensemble initial conditions can be generated by repeating this procedure.

  3. Simulate a particular observing system
    by first creating an 'observation set definition' and then creating an 'observation sequence'. The 'observation set definition' describes the instrumental characteristics of the observations and the 'observation sequence' defines the temporal sequence of the observations.

  4. Populate the 'observation sequence' with 'perfect' observations
    by integrating the model and using the information in the 'observation sequence' file to create simulated observations. This entails operating on the model state at the time of the observation with an appropriate forward operator (a function that operates on the model state vector to produce the expected value of the particular observation) and then adding a random sample from the observation error distribution specified in the observation set definition. At the same time, diagnostic output about the 'true' state trajectory can be created.

  5. Assimilate the synthetic observations
    by running the filter; diagnostic output is generated.

1. Integrate the L63 model for a 'long' time.

perfect_model_obs integrates the model for all the times specified in the 'observation sequence definition' file. To this end, begin by creating an 'observation sequence definition' file that spans a long time. Creating an 'observation sequence definition' file is a two-step procedure involving create_obs_sequence followed by create_fixed_network_seq. After they are both run, it is necessary to integrate the model with perfect_model_obs.

1.1 Create an observation set definition.

create_obs_sequence creates an observation set definition, the time-independent part of an observation sequence. An observation set definition file only contains the location, type, and observational error characteristics (normally just the diagonal observational error variance) for a related set of observations. There are no actual observations, nor are there any times associated with the definition. For spin-up, we are only interested in integrating the L63 model, not in generating any particular synthetic observations. Begin by creating a minimal observation set definition.

In general, for the low-order models, only a single observation set need be defined. Next, the number of individual scalar observations (like a single surface pressure observation) in the set is needed. To spin-up an initial condition for the L63 model, only a single observation is needed. Next, the error variance for this observation must be entered. Since we do not need (nor want) this observation to have any impact on an assimilation (it will only be used for spinning up the model and the ensemble), enter a very large value for the error variance. An observation with a very large error variance has essentially no impact on deterministic filter assimilations like the default variety implemented in DART. Finally, the location and type of the observation need to be defined. For all types of models, the most elementary form of synthetic observations are called 'identity' observations. These observations are generated simply by adding a random sample from a specified observational error distribution directly to the value of one of the state variables. This defines the observation as being an identity observation of the first state variable in the L63 model. The program will respond by terminating after generating a file (generally named set_def.out) that defines the single identity observation of the first state variable of the L63 model. The following is a screenshot (much of the verbose logging has been left off for clarity), the user input looks like this.

[unixprompt]$ ./create_obs_sequence
 Starting program create_obs_sequence
 Initializing the utilities module.
 Trying to log to unit   10
 Trying to open file dart_log.out
 
 Registering module :
 $url: http://squish/DART/trunk/utilities/utilities_mod.f90 $
 $revision: 2713 $
 $date: 2007-03-25 22:09:04 -0600 (Sun, 25 Mar 2007) $
 Registration complete.

 &UTILITIES_NML
 TERMLEVEL= 2,LOGFILENAME=dart_log.out                                          
                                                                            
 /
 
 Registering module :
 $url: http://squish/DART/trunk/obs_sequence/create_obs_sequence.f90 $
 $revision: 2713 $
 $date: 2007-03-25 22:09:04 -0600 (Sun, 25 Mar 2007) $
 Registration complete.

 { ... }

 Input upper bound on number of observations in sequence
10
 
 Input number of copies of data (0 for just a definition)
0

 Input number of quality control values per field (0 or greater)
0

 input a -1 if there are no more obs 
0

 Registering module :
 $url: http://squish/DART/trunk/obs_def/DEFAULT_obs_def_mod.F90 $
 $revision: 2820 $
 $date: 2007-04-09 10:37:47 -0600 (Mon, 09 Apr 2007) $
 Registration complete.
 
 
 Registering module :
 $url: http://squish/DART/trunk/obs_kind/DEFAULT_obs_kind_mod.F90 $
 $revision: 2822 $
 $date: 2007-04-09 10:39:08 -0600 (Mon, 09 Apr 2007) $
 Registration complete.
 
 ------------------------------------------------------
 
 initialize_module obs_kind_nml values are
 
 -------------- ASSIMILATE_THESE_OBS_TYPES --------------
 RAW_STATE_VARIABLE
 -------------- EVALUATE_THESE_OBS_TYPES --------------
 ------------------------------------------------------
 
      Input -1 * state variable index for identity observations
      OR input the name of the observation kind from table below:
      OR input the integer index, BUT see documentation...
        1 RAW_STATE_VARIABLE

-1

 input time in days and seconds
1 0

 Input error variance for this observation definition
1000000

 input a -1 if there are no more obs 
-1

 Input filename for sequence (  set_def.out   usually works well)
 set_def.out 
 write_obs_seq  opening formatted file set_def.out
 write_obs_seq  closed file set_def.out

1.2 Create an observation sequence definition.

create_fixed_network_seq creates an 'observation sequence definition' by extending the 'observation set definition' with the temporal attributes of the observations.

The first input is the name of the file created in the previous step, i.e. the name of the observation set definition that you've just created. It is possible to create sequences in which the observation sets are observed at regular intervals or irregularly in time. Here, all we need is a sequence that takes observations over a long period of time - indicated by entering a 1. Although the L63 system normally is defined as having a non-dimensional time step, the DART system arbitrarily defines the model timestep as being 3600 seconds. If we declare that we have one observation per day for 1000 days, we create an observation sequence definition spanning 24000 'model' timesteps; sufficient to spin-up the model onto the attractor. Finally, enter a name for the 'observation sequence definition' file. Note again: there are no observation values present in this file. Just an observation type, location, time and the error characteristics. We are going to populate the observation sequence with the perfect_model_obs program.

[unixprompt]$ ./create_fixed_network_seq

 ...

 Registering module :
 $url: http://squish/DART/trunk/obs_sequence/obs_sequence_mod.f90 $
 $revision: 2749 $
 $date: 2007-03-30 15:07:33 -0600 (Fri, 30 Mar 2007) $
 Registration complete.
 
 static_init_obs_sequence obs_sequence_nml values are
 &OBS_SEQUENCE_NML
 WRITE_BINARY_OBS_SEQUENCE =  F,
 /
 Input filename for network definition sequence (usually  set_def.out  )
set_def.out

 ...

 To input a regularly repeating time sequence enter 1
 To enter an irregular list of times enter 2
1
 Input number of observations in sequence
1000
 Input time of initial ob in sequence in days and seconds
1, 0
 Input period of obs in days and seconds
1, 0
           1
           2
           3
...
         997
         998
         999
        1000
What is output file name for sequence (  obs_seq.in   is recommended )
obs_seq.in
 write_obs_seq  opening formatted file obs_seq.in
 write_obs_seq closed file obs_seq.in

1.3 Initialize the model onto the attractor.

perfect_model_obs can now advance the arbitrary initial state for 24,000 timesteps to move it onto the attractor.
perfect_model_obs uses the Fortran90 namelist input mechanism instead of (admittedly gory, but temporary) interactive input. All of the DART software expects the namelists to found in a file called input.nml. When you built the executable, an example namelist was created input.nml.perfect_model_obs_default that contains all of the namelist input for the executable. If you followed the example, each namelist was saved to a unique name. We must now rename and edit the namelist file for perfect_model_obs. Copy input.nml.perfect_model_obs_default to input.nml and edit it to look like the following: (just worry about the highlighted stuff - and whitespace doesn't matter)

cp input.nml.perfect_model_obs_default input.nml
&perfect_model_obs_nml
   start_from_restart    = .false.,
   output_restart        = .true.,
   async                 = 0,
   init_time_days        = 0,
   init_time_seconds     = 0,
   first_obs_days        = -1,
   first_obs_seconds     = -1,
   last_obs_days         = -1,
   last_obs_seconds      = -1,
   output_interval       = 1,
   restart_in_file_name  = "perfect_ics",
   restart_out_file_name = "perfect_restart",
   obs_seq_in_file_name  = "obs_seq.in",
   obs_seq_out_file_name = "obs_seq.out",
   adv_ens_command       = "./advance_ens.csh"  /

&ensemble_manager_nml
   single_restart_file_in  = .true.,
   single_restart_file_out = .true.,
   perturbation_amplitude  = 0.2  /

&assim_tools_nml
   filter_kind                     = 1,
   cutoff                          = 0.2,
   sort_obs_inc                    = .false.,
   spread_restoration              = .false.,
   sampling_error_correction       = .false.,
   adaptive_localization_threshold = -1,
   print_every_nth_obs             = 0  /

&cov_cutoff_nml
   select_localization = 1  /

&reg_factor_nml
   select_regression    = 1,
   input_reg_file       = "time_mean_reg",
   save_reg_diagnostics = .false.,
   reg_diagnostics_file = "reg_diagnostics"  /

&obs_sequence_nml
   write_binary_obs_sequence = .false.  /

&obs_kind_nml
   assimilate_these_obs_types = 'RAW_STATE_VARIABLE'  /

&assim_model_nml
   write_binary_restart_files = .true. /

&model_nml
   sigma  = 10.0,
   r      = 28.0,
   b      = 2.6666666666667,
   deltat = 0.01,
   time_step_days = 0,
   time_step_seconds = 3600  /

&utilities_nml
   TERMLEVEL = 1,
   logfilename = 'dart_log.out'  /

For the moment, only two namelists warrant explanation. Each namelists is covered in detail in the html files accompanying the source code for the module.

perfect_model_obs_nml

namelist variabledescription
start_from_restart When set to 'false', perfect_model_obs generates an arbitrary initial condition (which cannot be guaranteed to be on the L63 attractor). When set to 'true', a restart file (specified by restart_in_file_name) is read.
output_restart When set to 'true', perfect_model_obs will record the model state at the end of this integration in the file named by restart_out_file_name.
async The lorenz_63 model is advanced through a subroutine call - indicated by async = 0. There is no other valid value for this model.
init_time_xxxx the start time of the integration.
first_obs_xxxx the time of the first observation of interest. While not needed in this example, you can skip observations if you want to. A value of -1 indicates to start at the beginning.
last_obs_xxxx the time of the last observation of interest. While not needed in this example, you do not have to assimilate all the way to the end of the observation sequence file. A value of -1 indicates to use all the observations.
output_interval interval at which to save the model state (in True_State.nc).
restart_in_file_name is ignored when 'start_from_restart' is 'false'.
restart_out_file_name if output_restart is 'true', this specifies the name of the file containing the model state at the end of the integration.
obs_seq_in_file_name specifies the file name that results from running create_fixed_network_seq, i.e. the 'observation sequence definition' file.
obs_seq_out_file_name specifies the output file name containing the 'observation sequence', finally populated with (perfect?) 'observations'.
advance_ens_command specifies the shell commands or script to execute when async /= 0.

utilities_nml

namelist variabledescription
TERMLEVEL When set to '1' the programs terminate when a 'warning' is generated. When set to '2' the programs terminate only with 'fatal' errors.
logfilename Run-time diagnostics are saved to this file. This namelist is used by all programs, so the file is opened in APPEND mode. Subsequent executions cause this file to grow.

Executing perfect_model_obs will integrate the model 24,000 steps and output the resulting state in the file perfect_restart. Interested parties can check the spinup in the True_State.nc file.

perfect_model_obs

2. Generate a set of ensemble initial conditions.

The set of initial conditions for a 'perfect model' experiment is created in several steps. 1) Starting from the spun-up state of the model (available in perfect_restart), run perfect_model_obs to generate the 'true state' of the experiment and a corresponding set of observations. 2) Feed the same initial spun-up state and resulting observations into filter.

The first step is achieved by changing a perfect_model_obs namelist parameter, copying perfect_restart to perfect_ics, and rerunning perfect_model_obs. This execution of perfect_model_obs will advance the model state from the end of the first 24,000 steps to the end of an additional 24,000 steps and place the final state in perfect_restart. The rest of the namelists in input.nml should remain unchanged.

&perfect_model_obs_nml
   start_from_restart    = .true.,
   output_restart        = .true.,
   async                 = 0,
   init_time_days        = 0,
   init_time_seconds     = 0,
   first_obs_days        = -1,
   first_obs_seconds     = -1,
   last_obs_days         = -1,
   last_obs_seconds      = -1,
   output_interval       = 1,
   restart_in_file_name  = "perfect_ics",
   restart_out_file_name = "perfect_restart",
   obs_seq_in_file_name  = "obs_seq.in",
   obs_seq_out_file_name = "obs_seq.out",
   adv_ens_command       = "./advance_ens.csh"  /


cp perfect_restart perfect_ics
perfect_model_obs

A True_State.nc file is also created. It contains the 'true' state of the integration.

Generating the ensemble

This step (#2 from above) is done with the program filter, which also uses the Fortran90 namelist mechanism for input. It is now necessary to copy the input.nml.filter_default namelist to input.nml.

cp input.nml.filter_default input.nml

You may also build one master namelist containting all the required namelists. Having unused namelists in the input.nml does not hurt anything, and it has been so useful to be reminded of what is possible that we made it an error to NOT have a required namelist. Take a peek at any of the other models for examples of a "fully qualified" input.nml.

HINT: if you used svn to get the project, try 'svn revert input.nml' to restore the namelist that was distributed with the project - which DOES have all the namelist blocks. Just be sure the values match the examples here.

&filter_nml
   async                    = 0,
   adv_ens_command          = "./advance_model.csh",
   ens_size                 = 100,
   start_from_restart       = .false.,
   output_restart           = .true.,
   obs_sequence_in_name     = "obs_seq.out",
   obs_sequence_out_name    = "obs_seq.final",
   restart_in_file_name     = "perfect_ics",
   restart_out_file_name    = "filter_restart",
   init_time_days           = 0,
   init_time_seconds        = 0,
   first_obs_days           = -1,
   first_obs_seconds        = -1,
   last_obs_days            = -1,
   last_obs_seconds         = -1,
   num_output_state_members = 20,
   num_output_obs_members   = 20,
   output_interval          = 1,
   num_groups               = 1,
   input_qc_threshold       =  4.0,
   outlier_threshold        = -1.0,
   output_forward_op_errors = .false.,
   output_timestamps        = .false.,
   output_inflation         = .true.,

   inf_flavor               = 0,                       0,
   inf_start_from_restart   = .false.,                 .false.,
   inf_output_restart       = .false.,                 .false.,
   inf_deterministic        = .true.,                  .true.,
   inf_in_file_name         = 'not_initialized',       'not_initialized',
   inf_out_file_name        = 'not_initialized',       'not_initialized',
   inf_diag_file_name       = 'not_initialized',       'not_initialized',
   inf_initial              = 1.0,                     1.0,
   inf_sd_initial           = 0.0,                     0.0,
   inf_lower_bound          = 1.0,                     1.0,
   inf_upper_bound          = 1000000.0,               1000000.0,
   inf_sd_lower_bound       = 0.0,                     0.0
/

&smoother_nml
   num_lags              = 0,
   start_from_restart    = .false.,
   output_restart        = .false.,
   restart_in_file_name  = 'smoother_ics',
   restart_out_file_name = 'smoother_restart'  /

&ensemble_manager_nml
   single_restart_file_in  = .true.,
   single_restart_file_out = .true.,
   perturbation_amplitude  = 0.2  /

&assim_tools_nml
   filter_kind                     = 1,
   cutoff                          = 0.2,
   sort_obs_inc                    = .false.,
   spread_restoration              = .false.,
   sampling_error_correction       = .false.,
   adaptive_localization_threshold = -1,
   print_every_nth_obs             = 0  /

&cov_cutoff_nml
   select_localization = 1  /

&reg_factor_nml
   select_regression    = 1,
   input_reg_file       = "time_mean_reg",
   save_reg_diagnostics = .false.,
   reg_diagnostics_file = "reg_diagnostics"  /

&obs_sequence_nml
   write_binary_obs_sequence = .false.  /

&obs_kind_nml
   assimilate_these_obs_types = 'RAW_STATE_VARIABLE'  /

&assim_model_nml
   write_binary_restart_files = .true. /

&model_nml
   sigma  = 10.0,
   r      = 28.0,
   b      = 2.6666666666667,
   deltat = 0.01,
   time_step_days = 0,
   time_step_seconds = 3600  /

&utilities_nml
   TERMLEVEL = 1,
   logfilename = 'dart_log.out'  /

Only the non-obvious(?) entries for filter_nml will be discussed.

namelist variabledescription
ens_size Number of ensemble members. 100 is sufficient for most of the L63 exercises.
start_from_restart when '.false.', filter will generate its own ensemble of initial conditions. It is important to note that the filter still makes use of the file named by restart_in_file_name (i.e. perfect_ics) by randomly perturbing these state variables.
num_output_state_members specifies the number of state vectors contained in the netCDF diagnostic files. May be a value from 0 to ens_size.
num_output_obs_members specifies the number of 'observations' (derived from applying the forward operator to the state vector) are contained in the obs_seq.final file. May be a value from 0 to ens_size
inf_flavor A value of 0 results in no inflation.(spin-up)

The filter is told to generate its own ensemble initial conditions since start_from_restart is '.false.'. However, it is important to note that the filter still makes use of perfect_ics which is set to be the restart_in_file_name. This is the model state generated from the first 24,000 step model integration by perfect_model_obs. Filter generates its ensemble initial conditions by randomly perturbing the state variables of this state.

num_output_state_members are '.true.' so the state vector is output at every time for which there are observations (once a day here). Posterior_Diag.nc and Prior_Diag.nc then contain values for 20 ensemble members once a day. Once the namelist is set, execute filter to integrate the ensemble forward for 24,000 steps with the final ensemble state written to the filter_restart. Copy the perfect_model_obs restart file perfect_restart (the `true state') to perfect_ics, and the filter restart file filter_restart to filter_ics so that future assimilation experiments can be initialized from these spun-up states.

filter
cp perfect_restart perfect_ics
cp filter_restart filter_ics

The spin-up of the ensemble can be viewed by examining the output in the netCDF files True_State.nc generated by perfect_model_obs and Posterior_Diag.nc and Prior_Diag.nc generated by filter. To do this, see the detailed discussion of matlab diagnostics in Appendix I.

3. Simulate a particular observing system.

Begin by using create_obs_sequence to generate an observation set in which each of the 3 state variables of L63 is observed with an observational error variance of 1.0 for each observation. To do this, use the following input sequence (the text including and after # is a comment and does not need to be entered):

4 # upper bound on num of observations in sequence
0 # number of copies of data (0 for just a definition)
0 # number of quality control values per field (0 or greater)
0 # -1 to exit/end observation definitions
-1 # observe state variable 1
0   0 # time -- days, seconds
1.0 # observational variance
0 # -1 to exit/end observation definitions
-2 # observe state variable 2
0   0 # time -- days, seconds
1.0 # observational variance
0 # -1 to exit/end observation definitions
-3 # observe state variable 3
0   0 # time -- days, seconds
1.0 # observational variance
-1 # -1 to exit/end observation definitions
set_def.out # Output file name

Now, generate an observation sequence definition by running create_fixed_network_seq with the following input sequence:

set_def.out # Input observation set definition file
1 # Regular spaced observation interval in time
1000 # 1000 observation times
0, 43200 # First observation after 12 hours (0 days, 12 * 3600 seconds)
0, 43200 # Observations every 12 hours
obs_seq.in # Output file for observation sequence definition

4. Generate a particular observing system and true state.

An observation sequence file is now generated by running perfect_model_obs with the namelist values (unchanged from step 2):

&perfect_model_obs_nml
   start_from_restart    = .true.,
   output_restart        = .true.,
   async                 = 0,
   init_time_days        = 0,
   init_time_seconds     = 0,
   first_obs_days        = -1,
   first_obs_seconds     = -1,
   last_obs_days         = -1,
   last_obs_seconds      = -1,
   output_interval       = 1,
   restart_in_file_name  = "perfect_ics",
   restart_out_file_name = "perfect_restart",
   obs_seq_in_file_name  = "obs_seq.in",
   obs_seq_out_file_name = "obs_seq.out",
   adv_ens_command       = "./advance_ens.csh"  /

This integrates the model starting from the state in perfect_ics for 1000 12-hour intervals outputting synthetic observations of the three state variables every 12 hours and producing a netCDF diagnostic file, True_State.nc.

5. Filtering.

Finally, filter can be run with its namelist set to:

&filter_nml
   async                    = 0,
   adv_ens_command          = "./advance_model.csh",
   ens_size                 = 100,
   start_from_restart       = .true.,
   output_restart           = .true.,
   obs_sequence_in_name     = "obs_seq.out",
   obs_sequence_out_name    = "obs_seq.final",
   restart_in_file_name     = "filter_ics",
   restart_out_file_name    = "filter_restart",
   init_time_days           = 0,
   init_time_seconds        = 0,
   first_obs_days           = -1,
   first_obs_seconds        = -1,
   last_obs_days            = -1,
   last_obs_seconds         = -1,
   num_output_state_members = 20,
   num_output_obs_members   = 20,
   output_interval          = 1,
   num_groups               = 1,
   input_qc_threshold       =  4.0,
   outlier_threshold        = -1.0,
   output_forward_op_errors = .false.,
   output_timestamps        = .false.,
   output_inflation         = .true.,

   inf_flavor               = 0,                       0,
   inf_start_from_restart   = .false.,                 .false.,
   inf_output_restart       = .false.,                 .false.,
   inf_deterministic        = .true.,                  .true.,
   inf_in_file_name         = 'not_initialized',       'not_initialized',
   inf_out_file_name        = 'not_initialized',       'not_initialized',
   inf_diag_file_name       = 'not_initialized',       'not_initialized',
   inf_initial              = 1.0,                     1.0,
   inf_sd_initial           = 0.0,                     0.0,
   inf_lower_bound          = 1.0,                     1.0,
   inf_upper_bound          = 1000000.0,               1000000.0,
   inf_sd_lower_bound       = 0.0,                     0.0
 /

filter produces two output diagnostic files, Prior_Diag.nc which contains values of the ensemble mean, ensemble spread, and ensemble members for 12- hour lead forecasts before assimilation is applied and Posterior_Diag.nc which contains similar data for after the assimilation is applied (sometimes referred to as analysis values).

Now try applying all of the matlab diagnostic functions described in the Matlab® Diagnostics section.

The Tutorial.

The DART/tutorial documents are an excellent way to kick the tires on DART and learn about ensemble data assimilation. If you have gotten this far, you can run anything in the tutorial.


Matlab® Diagnostics

The output files are netCDF files, and may be examined with many different software packages. We happen to use Matlab®, and provide our diagnostic scripts in the hopes that they are useful.

The diagnostic scripts and underlying functions reside in two places: DART/diagnostics/matlab and DART/matlab. They are reliant on the public-domain netcdf toolbox from http://woodshole.er.usgs.gov/staffpages/cdenham/public_html/MexCDF/nc4ml5.html as well as the public-domain CSIRO matlab/netCDF interface from http://www.marine.csiro.au/sw/matlab-netcdf.html. If you do not have them installed on your system and want to use Matlab to peruse netCDF, you must follow their installation instructions. The 'interested reader' may want to look at the DART/matlab/startup.m file I use on my system. If you put it in your $HOME/matlab directory, it is invoked every time you start up Matlab.

Once you can access the getnc function from within Matlab, you can use our diagnostic scripts. It is necessary to prepend the location of the DART/matlab scripts to the matlabpath. Keep in mind the location of the netcdf operators on your system WILL be different from ours ... and that's OK.

0[269]0 ghotiol:/<5>models/lorenz_63/work]$ matlab -nojvm

                                             < M A T L A B >
                                 Copyright 1984-2002 The MathWorks, Inc.
                                     Version 6.5.0.180913a Release 13
                                               Jun 18 2002

  Using Toolbox Path Cache.  Type "help toolbox_path_cache" for more info.
 
  To get started, type one of these: helpwin, helpdesk, or demo.
  For product information, visit www.mathworks.com.

>> which getnc
/contrib/matlab/matlab_netcdf_5_0/getnc.m
>>ls *.nc

ans =

Posterior_Diag.nc  Prior_Diag.nc  True_State.nc


>>path('../../../matlab',path)
>>path('../../../diagnostics/matlab',path)
>>which plot_ens_err_spread
../../../matlab/plot_ens_err_spread.m
>>help plot_ens_err_spread

  DART : Plots summary plots of the ensemble error and ensemble spread.
                         Interactively queries for the needed information.
                         Since different models potentially need different 
                         pieces of information ... the model types are 
                         determined and additional user input may be queried.
 
  Ultimately, plot_ens_err_spread will be replaced by a GUI.
  All the heavy lifting is done by PlotEnsErrSpread.
 
  Example 1 (for low-order models)
 
  truth_file = 'True_State.nc';
  diagn_file = 'Prior_Diag.nc';
  plot_ens_err_spread

>>plot_ens_err_spread

And the matlab graphics window will display the spread of the ensemble error for each state variable. The scripts are designed to do the "obvious" thing for the low-order models and will prompt for additional information if needed. The philosophy of these is that anything that starts with a lower-case plot_some_specific_task is intended to be user-callable and should handle any of the models. All the other routines in DART/matlab are called BY the high-level routines.

Matlab scriptdescription
plot_bins plots ensemble rank histograms
plot_correl Plots space-time series of correlation between a given variable at a given time and other variables at all times in a n ensemble time sequence.
plot_ens_err_spread Plots summary plots of the ensemble error and ensemble spread. Interactively queries for the needed information. Since different models potentially need different pieces of information ... the model types are determined and additional user input may be queried.
plot_ens_mean_time_series Queries for the state variables to plot.
plot_ens_time_series Queries for the state variables to plot.
plot_phase_space Plots a 3D trajectory of (3 state variables of) a single ensemble member. Additional trajectories may be superimposed.
plot_total_err Summary plots of global error and spread.
plot_var_var_correl Plots time series of correlation between a given variable at a given time and another variable at all times in an ensemble time sequence.

Bias, filter divergence and covariance inflation (with the L63 model)

One of the common problems with ensemble filters is filter divergence, which can also be an issue with a variety of other flavors of filters including the classical Kalman filter. In filter divergence, the prior estimate of the model state becomes too confident, either by chance or because of errors in the forecast model, the observational error characteristics, or approximations in the filter itself. If the filter is inappropriately confident that its prior estimate is correct, it will then tend to give less weight to observations than they should be given. The result can be enhanced overconfidence in the model's state estimate. In severe cases, this can spiral out of control and the ensemble can wander entirely away from the truth, confident that it is correct in its estimate. In less severe cases, the ensemble estimates may not diverge entirely from the truth but may still be too confident in their estimate. The result is that the truth ends up being farther away from the filter estimates than the spread of the filter ensemble would estimate. This type of behavior is commonly detected using rank histograms (also known as Talagrand diagrams). You can see the rank histograms for the L63 initial assimilation by using the matlab script plot_bins.

A simple, but surprisingly effective way of dealing with filter divergence is known as covariance inflation. In this method, the prior ensemble estimate of the state is expanded around its mean by a constant factor, effectively increasing the prior estimate of uncertainty while leaving the prior mean estimate unchanged. The program filter has a namelist parameter that controls the application of covariance inflation, cov_inflate. Up to this point, cov_inflate has been set to 1.0 indicating that the prior ensemble is left unchanged. Increasing cov_inflate to values greater than 1.0 inflates the ensemble before assimilating observations at each time they are available. Values smaller than 1.0 contract (reduce the spread) of prior ensembles before assimilating.

You can do this by modifying the value of cov_inflate in the namelist, (try 1.05 and 1.10 and other values at your discretion) and run the filter as above. In each case, use the diagnostic matlab tools to examine the resulting changes to the error, the ensemble spread (via rank histogram bins, too), etc. What kind of relation between spread and error is seen in this model?


Synthetic Observations

Synthetic observations are generated from a `perfect' model integration, which is often referred to as the `truth' or a `nature run'. A model is integrated forward from some set of initial conditions and observations are generated as y = H(x) + e where H is an operator on the model state vector, x, that gives the expected value of a set of observations, y, and e is a random variable with a distribution describing the error characteristics of the observing instrument(s) being simulated. Using synthetic observations in this way allows students to learn about assimilation algorithms while being isolated from the additional (extreme) complexity associated with model error and unknown observational error characteristics. In other words, for the real-world assimilation problem, the model has (often substantial) differences from what happens in the real system and the observational error distribution may be very complicated and is certainly not well known. Be careful to keep these issues in mind while exploring the capabilities of the ensemble filters with synthetic observations.


Terms of Use

DART software - Copyright 2004 - 2013 UCAR.
This open source software is provided by UCAR, "as is",
without charge, subject to all terms of use at
http://www.image.ucar.edu/DAReS/DART/DART_download

Contact: DART core group
Revision: $Revision: 6671 $
Source: $URL: https://svn-dares-dart.cgd.ucar.edu/DART/releases/Lanai/doc/html/history/Jamaica_release.html $
Change Date: $Date: 2013-12-11 13:41:41 -0700 (Wed, 11 Dec 2013) $
Change history:  try "svn log" or "svn diff"