# DART "Iceland release" Documentation

 Jump to DART Documentation Main Index version information for this file: $Id: Iceland_release.html 11996 2017-10-17 22:19:25Z nancy@ucar.edu$

# Overview of DART

The Data Assimilation Research Testbed (DART) is designed to facilitate the combination of assimilation algorithms, models, and observation sets to allow increased understanding of all three. The DART programs have been compiled with several Fortran 90 compilers and run on a linux compute-server and linux clusters. 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. Sort of. There's 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.

The Iceland release provides several new models and has a greatly expanded capability for real observations which required a fundamentally different implementation of the low-level routines. It is now required to run a preprocessor on several of the program units to construct the source code files which will be compiled by the remaining units. Due to the potentially large number of observations types possible and for portability reasons, the preprocessor is actually a F90 program that uses the namelist mechanism for specifying the observation types to be included. This also prevents having a gory set of compile flags that is different for every compiler. One very clever colleague also 'built a better mousetrap' and figured out how to effectively and robustly read namelists, detect errors, and generate meaningful error messages. HURRAY!

The Iceland release has also been tested with more compilers in an attempt to determine non-portable code elements. It is my experience that the largest impediment to portable code is the reliance on the compiler to autopromote real variables to one flavor or another. Different compilers perform this autopromotion in different ways. Using the F90 "kind" allows for much more flexible code, in that the use of interface procedures is possible only when two routines do not have identical sets of input arguments -- something that happens when the compiler autopromotes 32bit reals to 64bit reals, for example.

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.

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. A set of Matlab scripts, designed to produce graphical diagnostics from DART netCDF output files are available. DART users have also used ncview to create rudimentary graphical displays of output data fields. The NCO tools, produced by UCAR's Unidata group, are available to do operations like concatenating, slicing, and dicing of netCDF files.

## 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. For convenience, some of the original colloquium exercises are repeated here, mostly just to check the installation. A few of the exercises from the ASP summer 2003 Colloquium are repeated here, primarily to serve as the verification of the installation. The entire installation process is summarized in the following steps:

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.

## 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, 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.

## Unpacking the distribution.

The DART source code is distributed as a compressed tar file from our download site. When gunzip'ed and untarred, the source tree will begin with a directory named DART and will be approximately 203 Mb. Compiling the code in this tree (as is usually the case) will necessitate much more space.

gunzip DART_iceland.tar.gz
tar -xvf DART_iceland.tar

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 relatively 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 either 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.pgf90.ghotiol) is needed: (note that only the LAST lines are shown here, the head of the file is just a big comment)

# Makefile template for PGI f90
FC = pgf90
LD = pgf90
CPPFLAGS =
LIST = -Mlist
NETCDF = /contrib/netcdf-3.5.1-cc-c++-pgif90.5.2-4
FFLAGS = -O0 -Ktrap=fp -pc 64 -I$(NETCDF)/include LIBS = -L$(NETCDF)/lib -lnetcdf
LDFLAGS = $(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 = pgf90 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. #### 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. 1. path_names_preprocess 2. path_names_create_obs_sequence 3. path_names_create_fixed_network_seq 4. path_names_perfect_model_obs 5. path_names_filter 6. path_names_obs_diag Since each model comes with its own set of files, no further customization is needed. ## 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: filter_ics filter_restart input.nml mkmf_create_fixed_network_seq mkmf_create_obs_sequence mkmf_filter mkmf_obs_diag mkmf_perfect_model_obs mkmf_preprocess 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_perfect_model_obs path_names_preprocess perfect_ics perfect_restart Posterior_Diag.nc Prior_Diag.nc set_def.out True_State.nc workshop_setup.csh There are six mkmf_xxxxxx files for the programs preprocess, create_obs_sequence, create_fixed_network_seq, perfect_model_obs, filter, and obs_diag along with the corresponding path_names_xxxxxx files. 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: 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 five 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. ## 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
Initializing the utilities module.
Trying to read from unit           10
Trying to open file dart_log.out

Registering module :
$source: /home/dart/CVS.REPOS/DART/utilities/utilities_mod.f90,v$
$revision: 1.18$
$date: 2004/06/29 15:16:40$
Registration complete.

&UTILITIES_NML
TERMLEVEL= 2,LOGFILENAME=dart_log.out

/

Registering module :
$source: /home/dart/CVS.REPOS/DART/obs_sequence/create_obs_sequence.f90,v$
$revision: 1.18$
$date: 2004/05/24 15:41:46$
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 :
$Source$
$Revision: 11996$
$Date: 2017-10-17 16:19:25 -0600 (Tue, 17 Oct 2017)$
Registration complete.

Registering module :
$Source$
$Revision: 11996$
$Date: 2017-10-17 16:19:25 -0600 (Tue, 17 Oct 2017)$
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]$./create_fixed_network_seq ... Registering module :$source: /home/dart/CVS.REPOS/DART/obs_sequence/obs_sequence_mod.f90,v revision: 1.31 date: 2004/06/29 15:04:37 $Registration complete. 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 [blah blah blah]/work/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) &perfect_model_obs_nml async = 0, adv_ens_command = "./advance_ens.csh", obs_seq_in_file_name = "obs_seq.in", obs_seq_out_file_name = "obs_seq.out", start_from_restart = .false., output_restart = .true., restart_in_file_name = "perfect_ics", restart_out_file_name = "perfect_restart", init_time_days = 0, init_time_seconds = 0, output_interval = 1 / &ensemble_manager_nml in_core = .true., single_restart_file_in = .true., single_restart_file_out = .true. / &assim_tools_nml filter_kind = 1, cutoff = 0.2, sort_obs_inc = .false., cov_inflate = -1.0, cov_inflate_sd = 0.05, sd_lower_bound = 0.05, deterministic_cov_inflate = .true., start_from_assim_restart = .false., assim_restart_in_file_name = 'assim_tools_ics', assim_restart_out_file_name = 'assim_tools_restart', do_parallel = 0, num_domains = 1 parallel_command = "./assim_filter.csh", spread_restoration = .false., cov_inflate_upper_bound = 10000000.0, internal_outlier_threshold = -1.0 / &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 async For the lorenz_63, simply ignore this. Leave it set to '0' advance_ens_command specifies the shell commands or script to execute when async /= 0 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'. 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). 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. 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. init_time_xxxx the start time of the integration. output_interval interval at which to save the model state. ### 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 async = 0, adv_ens_command = "./advance_ens.csh", obs_seq_in_file_name = "obs_seq.in", obs_seq_out_file_name = "obs_seq.out", start_from_restart = .true., output_restart = .true., restart_in_file_name = "perfect_ics", restart_out_file_name = "perfect_restart", init_time_days = 0, init_time_seconds = 0, output_interval = 1 / 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 or you may simply insert the filter_nml namelist block into the existing input.nml. Having the perfect_model_obs namelist in the input.nml does not hurt anything. In fact, I generally create a single input.nml that has all the namelist blocks in it. I simply copied the filter namelist block from input.nml.filter_default and inserted it into our input.nml for the following example. &perfect_model_obs_nml async = 0, adv_ens_command = "./advance_ens.csh", obs_seq_in_file_name = "obs_seq.in", obs_seq_out_file_name = "obs_seq.out", start_from_restart = .true., output_restart = .true., restart_in_file_name = "perfect_ics", restart_out_file_name = "perfect_restart", init_time_days = 0, init_time_seconds = 0, output_interval = 1 / &filter_nml async = 0, adv_ens_command = "./advance_ens.csh", ens_size = 100, cov_inflate = 1.0, 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, output_state_ens_mean = .true., output_state_ens_spread = .true., output_obs_ens_mean = .true., output_obs_ens_spread = .true., num_output_state_members = 20, num_output_obs_members = 20, output_interval = 1, num_groups = 1, outlier_threshold = -1.0 / &ensemble_manager_nml in_core = .true., single_restart_file_in = .true., single_restart_file_out = .true. / &assim_tools_nml filter_kind = 1, cutoff = 0.2, sort_obs_inc = .false., cov_inflate = -1.0, cov_inflate_sd = 0.05, sd_lower_bound = 0.05, deterministic_cov_inflate = .true., start_from_assim_restart = .false., assim_restart_in_file_name = 'assim_tools_ics', assim_restart_out_file_name = 'assim_tools_restart', do_parallel = 0, num_domains = 1 parallel_command = "./assim_filter.csh", spread_restoration = .false., cov_inflate_upper_bound = 10000000.0, internal_outlier_threshold = -1.0 / &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. cutoff to limit the impact of an observation, set to 0.0 (i.e. spin-up) cov_inflate A value of 1.0 results in no inflation.(spin-up) 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 perfect_ics by randomly perturbing these state variables. output_state_ens_mean when '.true.' the mean of all ensemble members is output. output_state_ens_spread when '.true.' the spread of all ensemble members is output. num_output_state_members may be a value from 0 to ens_size output_obs_ens_mean when '.true.' Output ensemble mean in observation output file. output_obs_ens_spread when '.true.' Output ensemble spread in observation output file. num_output_obs_members may be a value from 0 to ens_size output_interval The frequency with which output state diagnostics are written. Units are in assimilation times. Default value is 1 meaning output is written at every observation time 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. The arguments output_state_ens_mean and output_state_ens_spread are '.true.' so that these quantities are output at every time for which there are observations (once a day here) and num_output_ens_members means that the same diagnostic files, Posterior_Diag.nc and Prior_Diag.nc also 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 async = 0, adv_ens_command = "./advance_ens.csh", obs_seq_in_file_name = "obs_seq.in", obs_seq_out_file_name = "obs_seq.out", start_from_restart = .true., output_restart = .true., restart_in_file_name = "perfect_ics", restart_out_file_name = "perfect_restart", init_time_days = 0, init_time_seconds = 0, output_interval = 1 / 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_ens.csh", ens_size = 100, cov_inflate = 1.0, 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, output_state_ens_mean = .true., output_state_ens_spread = .true., output_obs_ens_mean = .true., output_obs_ens_spread = .true., num_output_state_members = 20, num_output_obs_members = 20, output_interval = 1, num_groups = 1, outlier_threshold = -1.0 / The large value for the cutoff allows each observation to impact all other state variables (see Appendix V for localization). 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. ## 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.