Lanai¶
DART Lanai release documentation¶
Attention
Lanai is a prior release of DART. Its source code is available via the DART repository on Github. This documentation is preserved merely for reference. See the DART homepage for information on the latest release.
Dart overview¶
The Data Assimilation Research Testbed (DART) is designed to facilitate the combination of assimilation algorithms, models, and real (or synthetic) observations to allow increased understanding of all three. The DART programs are highly portable, having been compiled with many Fortran 90 compilers and run on linux compute-servers, linux clusters, OSX laptops/desktops, SGI Altix clusters, supercomputers running AIX, and more. Read the Customizations section for help in building on new platforms.
DART employs a modular programming approach to apply an Ensemble Kalman Filter which adjusts model values 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 assimilated 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. See the DARTLAB demos or read more in the tutorials included with the DART distribution. They are described below.
DART diagnostic output includes two 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 with other
analysis programs such as NCL, R, etc.
In this document links are available which point to Web-based documentation files and also to the same information in
html files distributed with DART. If you have used subversion to check out a local copy of the DART files you can open
this file in a browser by loading DART/docs/html/Lanai_release.html and then use the local file links to see
other documentation pages without requiring a connection to the internet. If you are looking at this documentation from
the www.image.ucar.edu web server or you are connected to the internet you can use the Website links to view
other documentation pages.
Getting started¶
What’s required¶
a Fortran 90 compiler
a netCDF library including the F90 interfaces
the C shell
(optional, to run in parallel) an MPI library
DART has been tested on many Fortran compilers and platforms. We don’t have any platform-dependent code sections and we use only the parts of the language that are portable across all the compilers we have access to. We explicitly set the Fortran ‘kind’ for all real values and do not rely on autopromotion or other compile-time flags to set the default byte size for numbers. It is possible that some model-specific interface code from outside sources may have specific compiler flag requirements; see the documentation for each model. The low-order models and all common portions of the DART code compile cleanly.
.nc and can be read by a number of standard data analysis tools.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 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. 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.
Types of input¶
DART programs can require three different types of input. First, some of the DART programs, like 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 Fortran90 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.
Installation¶
This document outlines the installation of the DART software and the system requirements. The entire installation process is summarized in the following steps:
Determine which F90 compiler is available.
Determine the location of the
netCDFlibrary.Download the DART software into the expected source tree.
Modify certain DART files to reflect the available F90 compiler and location of the appropriate libraries.
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.
Running the Lorenz_63 Model.
Using the Matlab® diagnostic scripts.
A short discussion on bias, filter divergence and covariance inflation.
And another one on synthetic observations.
You should absolutely run the DARTLAB interactive tutorial (if you have Matlab available) and look at the DARTLAB
presentation slides Website or
DART_LAB Tutorial in the DART_LAB directory, and then take the tutorial in the DART/tutorial
directory.
Requirements: an F90 compiler¶
The DART software has been successfully built on many Linux, OS/X, and supercomputer platforms with compilers that include GNU Fortran Compiler (“gfortran”) (free), Intel Fortran Compiler for Linux and Mac OS/X, Portland Group Fortran Compiler, Lahey Fortran Compiler, Pathscale Fortran Compiler, and the Cray native compiler. Since recompiling the code is a necessity to experiment with different models, there are no binaries to distribute.
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/
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. Depending on the netCDF build options, the
Fortran 90 interfaces may be built in a separate library named netcdff.a and you may need to add -lnetcdff to
the library flags.
Downloading the distribution¶
This release of the DART source code can be downloaded as a
compressed zip or tar.gz file. When extracted, the source tree will begin with a directory named DART and will be
approximately 175.3 Mb. Compiling the code in this tree (as is usually the case) will necessitate much more space.
$ gunzip DART-8.0.0.tar.gz
$ tar -xvf DART-8.0.0.tar
You should wind up with a directory named DART.
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¶
NETCDF value should be relatively straightforward.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.
Libs¶
The Fortran 90 interfaces may be part of the default netcdf.a library and -lnetcdf is all you need. However it
is also common for the Fortran 90 interfaces to be built in a separate library named netcdff.a. In that case you
will need -lnetcdf and also -lnetcdff on the LIBS line. This is a build-time option when the netCDF
libraries are compiled so it varies from site to site.
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.
Building the Lorenz_63 DART project¶
DART executables are constructed in a work subdirectory under the directory containing code for the given model.
From 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_obs_diag
mkmf_obs_sequence_tool
mkmf_perfect_model_obs
mkmf_preprocess
mkmf_restart_file_tool
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_obs_diag
path_names_obs_sequence_tool
path_names_perfect_model_obs
path_names_preprocess
path_names_restart_file_tool
path_names_wakeup_filter
perfect_ics
perfect_restart
quickbuild.csh
set_def.out
workshop_setup.csh
In all the work directories there will be a quickbuild.csh script that builds or rebuilds the executables. The
following instructions do this work by hand to introduce you to the individual steps, but in practice running quickbuild
will be the normal way to do the compiles.
There are nine mkmf_xxxxxx files for the programs
preprocess,create_obs_sequence,create_fixed_network_seq,perfect_model_obs,filter,wakeup_filter,obs_sequence_tool, andrestart_file_tool, andobs_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 function of the mkmf_xxxxxx script is to generate a
Makefile and an input.nml.program_default file. It does not do the compile; 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. (The quickbuild.csh script will do this for you automatically.)
$ \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.
program |
purpose |
|---|---|
|
creates custom source code for just the observation types of interest |
|
specify a (set) of observation characteristics taken by a particular (set of) instruments |
|
repeat a set of observations through time to simulate observing networks where observations are taken in the same location at regular (or irregular) intervals |
|
generate “true state” for synthetic observation experiments. Can also be used to ‘spin up’ a model by running it for a long time. |
|
does the assimilation |
|
creates observation-space diagnostic files to be explored by the Matlab® scripts. |
|
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. We will not cover its use in this document. |
|
manipulates the initial condition and restart files. We’re going to ignore this one here. |
|
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¶
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.
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.
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.
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.
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 /
®_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 variable |
description |
|---|---|
|
When set to ‘false’, |
|
When set to ‘true’, |
|
The lorenz_63 model is advanced through a subroutine call - indicated by async = 0. There is no other valid value for this model. |
|
the start time of the integration. |
|
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. |
|
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. |
|
interval at which to save the model state (in True_State.nc). |
|
is ignored when ‘start_from_restart’ is ‘false’. |
|
if |
|
specifies the file name that results from running |
|
specifies the output file name containing the ‘observation sequence’, finally populated with (perfect?) ‘observations’. |
|
specifies the shell commands or script to execute when async /= 0. |
utilities_nml¶
namelist variable |
description |
|---|---|
|
When set to ‘1’ the programs terminate when a ‘warning’ is generated. When set to ‘2’ the programs terminate only with ‘fatal’ errors. |
|
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 /
®_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 variable |
description |
|---|---|
|
Number of ensemble members. 100 is sufficient for most of the L63 exercises. |
|
when ‘.false.’, |
|
specifies the number of state vectors contained in the netCDF diagnostic files. May
be a value from 0 to |
|
specifies the number of ‘observations’ (derived from applying the forward operator
to the state vector) are contained in the |
|
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 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 MEXNC/SNCTOOLS netCDF interface from http://mexcdf.sourceforge.net. 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 $HOME/matlab directory it is invoked every time you start up Matlab.
nc_varget function from within Matlab you can use our diagnostic scripts. It is necessary
to prepend the location of the DART/matlab scripts to the matlabpath. Keep in mind the location of the netcdf
operators on your system WILL be different from ours … and that’s OK.[models/lorenz_63/work]$ matlab -nodesktop
< 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 nc_varget
/contrib/matlab/snctools/4024/nc_varget.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 script |
description |
|---|---|
|
plots ensemble rank histograms |
|
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. |
|
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. |
|
Queries for the state variables to plot. |
|
Queries for the state variables to plot. |
|
Plots a 3D trajectory of (3 state variables of) a single ensemble member. Additional trajectories may be superimposed. |
|
Summary plots of global error and spread. |
|
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 group of namelist parameters that controls the application of covariance inflation. For a simple set of inflation
values, you will set inf_flavor, and inf_initial. These values come in pairs; the first value controls inflation
of the prior ensemble values, while the second controls inflation of the posterior values. Up to this point
inf_flavor has been set to 0 indicating that the prior ensemble is left unchanged. Setting the first value of
inf_flavor to 3 enables one variety of inflation. Set inf_initial to different values (try 1.05 and 1.10 and
other values). 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?
There are many more options for inflation, including spatially and temporarily varying values, with and without damping. See the discussion of all inflation-related namelist items Website or local file.
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.
Notes for current users¶
If you have been updating from the development branch of the DART subversion repository you will not notice much difference between that and the Lanai release. If you are still running the Kodiak release there are many new models, new observation types, capabilities in the assimilation tools, new diagnostics, and new utilities. There is a short list of non-backwards compatible changes (see below), and then a long list of new options and functions.
In the near future we will be making substantial changes to the internal structure of DART to accomodate both larger models and machines with thousands of processors. We will continue to maintain the Lanai release with bug fixes, but we will be updating the subversion trunk with new and non-backwards-compatible code. Checking out the Lanai release branch and running ‘svn update’ from time to time is the recommended way to update your DART tree.
Non-backwards compatible changes¶
Changes in the Lanai release (13 Dec 2013) which are not backwards compatible with the Kodiak release (30 June 2011):
The DART system uses a new random number generator based on the Mersenne Twister algorithm from the GNU scientific library. It is believed to have better behavior in general, and in particular when it is frequently reseeded, as may be the case in some perfect_model_obs experiments. The seed in perfect_model_obs is now based on the time-stamp associated with the data, so running single advances as separate invocations of the executable will still result in a good random distribution of the observation errors. The seeds in several other places in the code have been changed so they are more consistent in the face of different numbers of MPI tasks when executing. The random values should reproduce if an identical run is repeated, but there are still a few places in the code where changing the number of MPI tasks results in different seeds being created for the random number generator, and so the non-deterministic values will differ.
The WRF model_mod now interpolates in the vertical in log(pressure) space instead of linear pressure space. This is the new default. There is a module global variable that can be set at compile time to restore the previous behavior.
The POP model_mod used to interpolate sensible temperature observations using a potential temperature field in the state vector. The code now correctly does the conversion from potential temperature to sensible (in-situ) temperature during the forward operator process.
If your
model_mod.f90provides a customizedget_close_obs()routine that makes use of the types/kinds arguments for either the base location or the close location list, there is an important change in this release. The fifth argument to theget_close_obs()call is now a list of generic kinds corresponding to the location list. The fourth argument to theget_dist()routine is now also a generic kind and not a specific type. In previous versions of the system the list of close locations was sometimes a list of specific types and other times a list of generic kinds. The system now always passes generic kinds for the close locations list for consistency. The base location and specific type remains the same as before. If you have aget_close_obs()routine in yourmodel_mod.f90file and have questions about usage, contact the DART development team.The
obs_common_subsetprogram namelist has changed. The program comparesobs_seq.finalfiles that were produced by different runs of filter using the same input obs_sequence file. The old version supported comparing only 2 files at a time; this version supports up to 50. It also enforces the implicit assumption that the incoming obs_seq.final files are identical except for the DART QC and the obs values.The simple_advection model was incorrectly calling the random number generator initialization routines after generating some random numbers. It now correctly initializes the generator before getting any random values.
The gts_to_dart converter now creates separate obs types for surface dewpoint vs obs aloft because they have different vertical coordinates. The obs_diag program (and other diagnostic routines) do not cope with the same obs type having different vertical coordinates because it is trying to bin observations in the vertical (it is unable to convert pressure to height after the fact, for example, or bin surface obs with a height with pressure obs).
Shell scripts which used to contain MSS (mass store) commands for long-term archiving have been converted to call HSI (HPSS) commands.
The ‘wrf_dart_obs_preprocess’ program will now refuse to superob observations which are too close to the poles. If the superob radius includes either pole, the computation of an average obs location becomes more complicated than the existing code is prepared to deal with. (If this case is of interest to you, contact the DART development team. We have ideas on how to implement this.)
The default namelist values for the ‘obs_seq_to_netcdf’ program has changed so the default is a single large time bin, which means you don’t have to know the exact time extents when converting an obs_seq.final file into a netCDF file. You can still set specific bins and get multiple netCDF files as output if you prefer.
The tutorial files are now directly in the DART/tutorial directory and no longer in separate subdirectories.
The default flags in the mkmf_template.XXX files have been updated to be more consistent with current compiler versions.
The default work/input.nml namelists for Lorenz 63 and Lorenz 96 have been changed to give good assimilation results by default. Originally these were set to work with a workshop tutorial in which the settings did not work and as part of the tutorial they were changed to good values. Now the workshop versions of the namelists are separate and copied into place by a workshop_setup script.
filter now calls the end_model() subroutine in the model_mod for the first time. It should have been called all along, but was not.
The ‘rat_cri’ namelist item has been removed from the &obs_diag namelist.
The preprocess program has a new namelist item ‘overwrite_output’ and it is .true. by default. The program will no longer fail if the target obs_kind_mod.f90 or obs_def_mod.f90 files exist but will silently overwrite them. Set this namelist item to .false. to recover the previous behavior.
New features¶
Customizable Outlier-Threshold Handling
Filter contains code to compute whether an observation should not be assimilated because the forward operator mean is too different from the observation value. This is done uniformly for all observation values and types. To customize this computation (e.g. to allow all obs of a particular type to be assimilated without having to pass the outlier threshold test), there is a new namelist item
enable_special_outlier_codein the &filter_nml namelist that enables a call to a subroutine at the end of the filter.f90 source file. That subroutine can be customized by the user to do any computation required. See the filter namelist documentation Website or local file for more details.
Fill inflation restart files
There is a new utility that will write inflation restart files based on values read from the console. This enables multi-step runs to start with the ‘read inflation values from a file’ option set to .true. for all steps instead of having to change the namelist after the first cycle. See the documentation Website or PROGRAM fill_inflation_restart for more details.
New location module options
There are additional options for the model and observation coordinate systems. Note that only a single location option can be chosen and all observations and all model locations must use that coordinate system. New options include:
Channel coordinate system
[0-1] periodic 3D coordinate system
X,Y,Z 3D Cartesian coordinate system
2D annulus coordinate system
See the documentation Website or MODULE location_mod for more details.
Missing values in state
In some models there are values which are not valid in all ensemble members. With this release there is limited support for this in DART. There are still serious questions about what the correct results should be if the ensemble count for some state vector item is smaller than the total ensemble size. Nevertheless, with this release we have implemented support for missing state vector values in the CLM Land model. There is a new namelist item
allow_missing_in_clmin the &assim_tools_nml namelist. Setting this to .true. will allow DART to avoid updating any state vector items in which one or more of the ensemble members in CLM have a missing value. Inflation will be disabled for any state vector items where one or more ensemble members have missing values. All CLM forward operators must test for and be prepared to return with a failed forward operator code if any of the interpolation items it requires are missing. See the documentation Website or local file for more details.
Different task layout options
The ensemble manager has a new option to distribute MPI tasks round robin across the available nodes instead of assigning them sequentially. The first N tasks, where N is the ensemble size, require more memory than other tasks. Distributing them round-robin may allow assigning more tasks per node with a more uniform memory usage. This may result in a small decrease in performance at runtime, but it might allow using fewer nodes for the job and thus reduce the job cost. See the documentation for the
layoutandtasks_per_nodein the &ensemble_manager_nml namelist Website or local file for more details.
Different MPI communication options
The ensemble manager has 3 new options for the order in which the communication is done when transposing the ensemble of state vectors. There is a new namelist option in the &ensemble_manager_nml called
communication_configurationwhich can have the values 1-4. If DART is running slower than expected, try the various options and see which is fastest on your hardware. The fastest value depends on the MPI library implementation, the type and speed of interconnect, the processor speed, and node memory size and so it is almost impossible to recommend a value without doing timing tests on the target system. See the documentation in the &ensemble_manager_nml namelist Website or local file for more details.
Several more places where large arrays were put on the stack have been removed, decreasing the total amount of stack required by DART.
New models¶
CESM framework components
DART now supports running CESM components CAM, POP, and CLM under the CESM framework. Setup scripts are provided to configure a single or multiple component assimilation. See:
Documentation for the model:
the user’s guide for CESM version 1.1.1: http://www.cesm.ucar.edu/models/cesm1.1/cesm/doc/usersguide/book1.html
the page that explains how to download the release code: http://www.cesm.ucar.edu/models/cesm1.1/cesm/doc/usersguide/x388.html
the web page that shows the names of the ‘compsets’ which are the configurations of the various models: http://www.cesm.ucar.edu/models/cesm1.1/cesm/doc/modelnl/compsets.html
list of recent CESM versions: http://www2.cesm.ucar.edu/models/current
MPAS Atmosphere and Ocean Models
NOAH Land Model
Dart interface documentation Website or NOAH, NOAH-MP.
Documentation for the model: The Community NOAH Land Surface Model (LSM).
NAAPS Aerosol Model
Dart interface documentation Website
Documentation for the model: NRL/Monterery Aerosol Model.
GITM Global Ionosphere Thermosphere Model
Documentation for the model: GITM Web Pages.
NOGAPS Global Atmosphere Model
SQG Surface Quasi-Geostrophic Model
Documentation for the model: Paper on SQG model.
The DART/models/template directory contains sample files for adding a new model. See this
section of the DART web pages for more
help on adding a new model.
Changed models¶
WRF
Allow advanced microphysics schemes (needed interpolation for 7 new kinds)
Interpolation in the vertical is now done in log(p) instead of linear pressure space. log(p) is the default, but a compile-time variable can restore the linear interpolation.
Added support in the namelist to avoid writing updated fields back into the wrf netcdf files. The fields are still updated during the assimilation but the updated data is not written back to the wrfinput file during the dart_to_wrf step.
Fixed an obscure bug in the vertical convert routine of the wrf model_mod that would occasionally fail to convert an obs. This would make tiny differences in the output as the number of mpi tasks change. No quantitative differences in the results but they were not bitwise compatible before and they are again now.
CAM
DART/CAM now runs under the CESM framework, so all options available with the framework can be used.
Support for the SE core (HOMME) has been developed but is NOT part of this release. Please contact the DART Development Group if you have an interest in this configuration of CAM.
Simple Advection Model
Fixed a bug where the random number generator was being used before being called with an initial seed.
New observation types/forward operators¶
Many new observation types related to land and atmospheric chemistry have been added. See the
obs_kind/obs_kind_mod.f90for a list of the generic kinds now available.New forward operator for total precipitable water. It loops over model levels to compute the accumulated value. See Website or Total Precipitable Water Observations.
New forward operator for COSMOS ground moisture observations. See Website or PROGRAM COSMOS_to_obs.
New forward operator for MIDAS total electron count observations. See Website or PROGRAM MIDAS_to_obs.
Added example of how to set additional metadata in an observation to the obs_def_1d_state_mod.f90 file. Website or MODULE obs_def_1d_state_mod.
New observation types/sources¶
MADIS Added a converter for wind profiler data to the set of existing MADIS converters. More scripting support in the MADIS obs converters; more error checks added to the rawin converter. Documentation Website or MADIS Data Ingest System.
Ameriflux Added an obs_sequence converter for Ameriflux land observations of latent heat flux, sensible heat flux, net ecosystem production). Documentation Website or PROGRAM level4_to_obs.
MODIS Added an obs_sequence converter for MODIS snow coverage measurements. Documentation Website or PROGRAM snow_to_obs.
COSMOS Added an obs_sequence converter for COSMOS ground moisture observations. Documentation Website or PROGRAM COSMOS_to_obs.
MIDAS Added an obs_sequence converter for MIDAS observations of Total Electron Count. Documentation Website or PROGRAM MIDAS_to_obs.
GPS Updated scripts for the GPS converter; added options to convert data from multiple satellites. Documentation Website or GPS Observations.
wrf_dart_obs_preprocess Added processing for wind profiler observation to the wrf_dart_obs_preprocess program. Documentation Website or PROGRAM wrf_dart_obs_preprocess.
AIRS Fix BUG in AIRS converter - the humidity obs are accumulated across the layers and so the best location for them is the layer midpoint and not on the edges (levels) as the temperature obs are. Also fixed off-by-one error where the converter would make one more obs above the requested top level. Documentation Website or AIRS Observations.
GTS Made gts_to_dart converter create separate obs types for surface dewpoint vs obs aloft because they have different vertical coordinates. Documentation Website or GTSPP Observations.
Tape Archive scripts Converted mss commands to hpss commands for a couple observation converter shell scripts (inc AIRS).
Evenly distributed obs New matlab code to generate evenly spaced observations on the surface of a sphere (e.g. the globe). Documentation Website or local file.
observation utilities Added obs_loop.f90 example file in obs_sequence directory; example template for how to construct special purpose obs_sequence tools. Documentation (source) Website or local file.
PREPBUFR Change the default in the script for the prepbufr converter so it will swap bytes, since all machines except ibms will need this now. Documentation Website or PROGRAM prepbufr.
New diagnostics and documentation¶
Other new stuff:
Handle empty epochs in the obs_seq_to_netcdf converter.
Added a matlab utility to show the output of a ‘hop’ test (running a model for a continuous period vs. stopping and restarting a run).
Improved the routine that computes axes tick values in plots with multiple values plotted on the same plot.
The obs_common_subset program can select common observations from up to 4 observation sequence files at a time.
Add code in obs_seq_verify to ensure that the ensemble members are in the same order in all netcdf files.
Added support for the unstructured grids of mpas to our matlab diagnostics.
Fix to writing of ReportTime in obs_seq_coverage.
Fixed logic in obs_seq_verify when determining the forecast lat.
Fixed loops inside obs_seq_coverage which were using the wrong limits on the loops. Fixed writing of ‘ntimes’ in output netcdf variable.
Rewrote the algorithm in the obs_selection tool so it had better scaling with large numbers of obs.
Several improvements to the ‘obs_diag’ program:
Added preliminary support for a list of ‘trusted obs’ in the obs_diag program.
Can disable the rank histogram generation with a namelist item.
Can define height_edges or heights in the namelist, but not both.
The ‘rat_cri’ namelist item (critical ratio) has been deprecated.
Extend obs_seq_verify so it can be used for forecasts from a single member. minor changes to obs_selection, obs_seq_coverage and obs_seq_verify to support a single member.
Added Matlab script to read/print timestamps from binary dart restart/ic files.
Default for obs_seq_to_netcdf in all the namelists is now ‘one big time bin’ so you don’t have to know the exact timespan of an obs_seq.final file before converting to netCDF.
New utilities¶
This section describes updates and changes to the tutorial materials, scripting, setup, and build information since the Kodiak release.
The mkmf-generated Makefiles now take care of calling ‘fixsystem’ if needed so the mpi utilities code compiles without further user intervention for any compiler.
Make the default input.nml for the Lorenz 96 and Lorenz 63 model gives good assimilation results. Rename the original input.nml to input.workshop.nml. The workshop_setup script renames it back before doing anything else so this won’t break the workshop instructions. Simplify all the workshop_setup.csh scripts to do the minimal work needed by the DART tutorial.
Updates to the models/template directory with the start of a full 3d geophysical model template. Still under construction.
Move the pdf files in the tutorial directory up a level. Removed framemaker source files because we no longer have access to a working version of the Framemaker software. Moved routines that generate figures and diagrams to a non-distributed directory of the subversion repository.
Enable netCDF large file support in the work/input.nml for models which are likely to have large state vectors.
Minor updates to the doc.css file, make pages look identical in the safari and firefox browsers.
Added a utility that sorts and reformats namelists, culls all comments to the bottom of the file. Useful for doing diffs and finding duplicated namelists in a file.
Cleaned up mkmf files - removed files for obsolete platforms and compilers, updated suggested default flags for intel.
Update the mkmf template for gfortran to allow fortran source lines longer than 132 characters.