The Data Assimilation Research Testbed (DART) is an open-source, freely available community facility for ensemble data assimilation (DA).1 DART is developed and maintained by the Data Assimilation Research Section (DAReS) at the NSF National Center for Atmospheric Research (NSF NCAR).
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Ensemble DA is a technique for combining observations with numerical models to estimate the state of a physical system.
It enables modelers, observational scientists, and geophysicists to:
- Generate initial conditions for forecasts.
- Create a retrospective estimate of the state of a system, a practice known as producing a reanalysis.
- Assess the relative value of specific observations on forecast skill, a practice known as conducting an observing system experiment (OSE).
- Estimate the value of hypothetical observations in order to inform the design of an observing system, a practice known as conducting an observing system simulation experiment (OSSE).
- Determine a model's systematic bias in estimating the state of a system, a practice known as diagnosing model error.
The DART software environment makes it easy to explore a variety of data assimilation methods and observations with different numerical models. It provides powerful, flexible DA tools that are easy to use and customize to support efficient and reliable DA applications. While DART is primarily oriented for DA research, it has also been used in operational settings.
DART includes:
- A comprehensive tutorial introducing the concepts of ensemble DA.
- Extensive documentation of its source code.
- Interfaces to a variety of models and observation sets that can be used to introduce new users or graduate students to ensemble DA.
- Nonlinear and Non-Gaussian DA Capabilities
DART is also designed to facilitate the combination of assimilation algorithms, models, and real or synthetic observations to allow increased understanding of all three. It provides a framework for developing, testing, and distributing advances in ensemble DA to a broad community of users by removing the implementation-specific peculiarities of one-off DA systems.
These tools are intended for use by the full range of geosciencies community: beginners and experts; students and teachers; national centers and university research labs.
One of the historical drawbacks of ensemble data assimilation techniques is the assumption that the quantities being assimilated obey a normal distribution. While this is often a safe assumption -- distributions of temperature and pressure can be approximated using a normal distribution -- many quantities such as precipitation, snow depth and tracer concentration, as well as many model parameters aren't normally distributed.
Applying traditional ensemble data assimilation techniques in situations where assumptions of gaussianity are invalid can lead to poor forecast skill and inconclusive results.
To overcome these problems, DART now implements a novel data assimilation technique that no longer requires quantities to be normally distributed. The Quantile-Conserving Ensemble Filtering Framework (QCEFF) <QCEFF> provides a general method of computing increments for the prior ensemble of an observed quantity by allowing the use of arbitrary distributions for the prior and the observation error. For a detailed description of the QCEFF, see Anderson (2022).2
While the QCEFF for computing observation increments can lead to significant improvements in analysis estimates for observed variables, those improvements can be lost when using standard linear regression of observation increments to update state variables. The QCEFF also implements a capability to do regression in a probit probability integral transformed space. Doing the regression of observation quantile increments in the transformed space guarantees that the posterior ensembles for state variables also retain the advantages of the observation space quantile conserving posteriors. For example, if state variables are bounded, then posterior ensembles will respect the bounds. The posterior ensembles also respect other aspects of the continuous prior distributions. For a detailed description of this process, see Anderson (2023)3 and Anderson et al. (2023).4
Inflation and localization, methods that improve the quality of ensemble DA, can also negate the advantages of the QCEFF methods. For this reason, both localization and inflation can be done in the probit-transformed quantile space as well. Combining these new methods can significantly improve data assimilation for non-Gaussian quantities in Earth system models by extending the capabilities of ensemble DA to general non-Gaussian and nonlinear distributions. Transformative improvements in DA can result for many applications. The largest improvements are found for bounded variables like tracer concentrations, snow and ice depth, soil moisture, and similar quantities in other parts of the Earth system. Model parameters can also be estimated with DA and large improvements can occur for bounded parameters. Variables that have distinctly non-Gaussian prior distributions can also see large improvements. Examples can include atmospheric quantities like moisture and cloud amount in the presence of convection, and many land surface variables.
For instructions on how to use these tools, see QCEFF.
For step-by-step examples of the QCEFF tools, you can work through examples with the Lorenz 96 tracer model <quantile tracer>
Because of DART's extensive scope, this documentation is detailed and carefully organized, enabling you to easily find the information you need. If you have any questions or suggestions for improvements, please contact DAReS staff by emailing dart@ucar.edu.
The documentation is partitioned into three parts:
- a user guide that explains how to install DART and perform data assimilation
- source code documentation that provides a detailed description of the programs and modules in the repository
- a comprehensive description of data assimilation theory
The current version of DART is the Manhattan release. Email dart@ucar.edu for advice if you are interested in a model which has not been converted from the previous Lanai release.
DART is available through GitHub. To download the latest version of DART, use:
git clone https://github.com/NCAR/DART.gitGo into the build_templates directory and copy over the closest mkmf.template._compiler.system_ file into mkmf.template.
Edit it to set the NETCDF directory location if not in /usr/local or comment it out and set $NETCDF in your environment. This NetCDF library must have been compiled with the same compiler that you use to compile DART and must include the F90 interfaces.
Go into models/lorenz_63/work and run quickbuild.sh nompi.
$ cd models/lorenz_63/work
$ ./quickbuild.sh nompiIf it compiles, run this series of commands to do a very basic test:
$ ./perfect_model_obs
$ ./filterIf that runs and you have Matlab installed on your system add DART/diagnostics/matlab to your matlab search path and run the plot_total_err diagnostic script while in the models/lorenz_63/work directory. If the output plots and looks reasonable (error level stays around 2 and doesn’t grow unbounded) you have successfully installed DART and completed your first assimilation with it.
If you are planning to run one of the larger models and want to use the Lorenz 63 model as a test, run ./quickbuild.sh. It will build filter and any other MPI-capable executables with MPI.
./quickbuild.sh help will print out the quickbuild.sh usage.
Important
The mpif90 command you use must have been built with the same version of the compiler as you are using.
If any of these steps fail or you don’t know how to do them, go to the DART project web page listed above for very detailed instructions that should get you over any bumps in the process.
To create a fork of DART for your own development you will need a GitHub account.
- fork the NCAR/DART repo on GitHub
- clone your (new) fork to your machine - this will set up a remote named ‘origin’.
git clone https://github.com/USERNAME/DART.gitwhere USERNAME is your GitHub username.
- create a remote to point back to the NCAR/DART repo. Convention dictates that this remote should be called ‘upstream’
git remote add upstream https://github.com/NCAR/DART.gitUse ‘upstream’ to keep your fork up to date with NCAR/DART. GitHub has documentation on working with forks.
- Download one of the tar files (listed below) of ‘large’ files so you can test your DART installation.
- If you want to contribute your work back to the DART community, create a feature branch with your work, then issue a pull request to propose changes to NCAR/DART.
There are several large files that are needed to run some of the tests and examples but are not included in order to keep the repository as small as possible. If you are interested in running bgrid_solo, cam-fv, or testing the NCEP/prep_bufr observation converter, you will need these files. These files are available at:
| Release | Size | Filename |
|---|---|---|
| “Manhattan” | 189M | Manhattan_large_files.tar.gz |
| “wrf-chem.r13172” | 141M | wrf-chem.r13172_large_files.tar.gz |
| “Lanai” | 158M | Lanai_large_files.tar.gz |
| “Kodiak” | 158M | Kodiak_large_files.tar.gz |
| “Jamaica” | 32M | Jamaica_large_files.tar.gz |
| “Hawaii” | 32M | Hawaii_large_files.tar.gz |
Download the appropriate tar file and untar it into your DART repository. Ignore any warnings about tar: Ignoring unknown extended header keyword.
Cite DART using the following text:
The Data Assimilation Research Testbed (Version X.Y.Z) [Software]. (2019). Boulder, Colorado: UCAR/NCAR/CISL/DAReS. http://doi.org/10.5065/D6WQ0202
Update the DART version and year as appropriate.
guide/system-requirements guide/fortran-compiler guide/locating-netcdf-library guide/downloading-dart guide/compiling-dart guide/verifying-installation
guide/introduction-ensemble-da guide/lorenz-63-model guide/da-in-dart-with-lorenz-63
guide/what-is-dart guide/benefits-of-using-dart guide/brief-history-of-dart guide/high-level-da-workflows guide/dart-design-philosophy guide/important-capabilities-dart guide/qceff_probit
guide/advice-for-new-collaborators guide/instructions-for-porting-a-new-model-to-dart DART build system <guide/quickbuild.rst> guide/assimilation-complex-model guide/mpi_intro guide/filters guide/inflation guide/required-model-mod-routines guide/suggestions-for-a-simple-model guide/suggestions-for-a-complex-model guide/how-to-test-your-model-mod-routines guide/controlling-files-output guide/vertical-conversion guide/data-management-issues assimilation_code/programs/readme
guide/adding-your-observations-to-dart guide/preprocess-program guide/introduction-obs-seq-file guide/detailed-structure-obs-seq guide/creating-obs-seq-synthetic guide/creating-obs-seq-real guide/available-observation-converters guide/manipulating-with-obs-sequence-tool guide/difference-between-type-and-quantity guide/adding-support-new-type Radiances <guide/Radiance_support>
observations/obs_converters/README
observations/obs_converters/AIRS/README observations/obs_converters/AIRS/convert_airs_L2 observations/obs_converters/AIRS/convert_amsu_L1 observations/obs_converters/AVISO/AVISO observations/obs_converters/Ameriflux/fluxnetfull_to_obs observations/obs_converters/Ameriflux/level4_to_obs observations/obs_converters/CHAMP/work/README observations/obs_converters/cice/cice_to_obs observations/obs_converters/CONAGUA/README observations/obs_converters/COSMOS/COSMOS_to_obs observations/obs_converters/COSMOS/COSMOS_development observations/obs_converters/DWL/dwl_to_obs observations/obs_converters/GMI/README observations/obs_converters/GOES/README observations/obs_converters/GPSPW/README observations/obs_converters/GSI2DART/readme observations/obs_converters/GTSPP/GTSPP observations/obs_converters/MADIS/MADIS observations/obs_converters/MIDAS/MIDAS_to_obs observations/obs_converters/MODIS/readme observations/obs_converters/MODIS/MOD15A2_to_obs observations/obs_converters/MPD/README observations/obs_converters/NASA_Earthdata/README observations/obs_converters/NCEP/prep_bufr/prep_bufr observations/obs_converters/NCEP/ascii_to_obs/create_real_obs observations/obs_converters/NSIDC/SMAP_L2_to_obs observations/obs_converters/ROMS/ROMS observations/obs_converters/SIF/SIF_to_obs_netcdf observations/obs_converters/SSEC/SSEC observations/obs_converters/SST/SST observations/obs_converters/SSUSI/convert_f16_edr_dsk observations/obs_converters/WOD/WOD observations/obs_converters/gnd_gps_vtec/README observations/obs_converters/gps/gps observations/obs_converters/ok_mesonet/ok_mesonet observations/obs_converters/ocean_color/README observations/obs_converters/quikscat/QuikSCAT observations/obs_converters/even_sphere/README observations/obs_converters/obs_error/README observations/obs_converters/radar/README observations/obs_converters/snow/snow_to_obs observations/obs_converters/text/text_to_obs observations/obs_converters/tpw/tpw observations/obs_converters/tropical_cyclone/tc_to_obs observations/obs_converters/var/littler_tf_dart observations/obs_converters/var/rad_3dvar_to_dart observations/obs_converters/var/var
guide/checking-your-assimilation guide/computing-filter-increments guide/how-does-output-differ-from-input-increments guide/dart-missing-data-value guide/dart-quality-control guide/examining-obs-seq-final guide/matlab-observation-space
theory/readme theory/conditional-probability-bayes-theorem guide/DART_LAB/DART_LAB CLM-DART Tutorial <models/clm/tutorial/README> WRF-DART Tutorial <models/wrf/tutorial/README> guide/qceff-examples.rst
models/README
models/9var/readme models/aether_lat-lon/readme models/am2/readme models/bgrid_solo/readme models/cam-fv/readme models/cam-fv/cam_dart_obs_preprocessor models/CESM/readme models/cice/readme models/clm/readme models/clm/clm_to_dart models/clm/dart_to_clm models/cm1/readme models/coamps_nest/readme models/coamps/readme models/ECHAM/readme models/FESOM/readme models/gitm/readme models/gitm/netcdf_to_gitm_blocks models/gitm/gitm_blocks_to_netcdf models/ikeda/readme models/LMDZ/readme models/lorenz_04/readme models/lorenz_63/readme models/lorenz_84/readme models/lorenz_96/readme models/lorenz_96_2scale/readme models/lorenz_96_tracer_advection/readme models/forced_lorenz_96/readme models/MITgcm_ocean/readme models/MOM6/readme models/mpas_atm/readme models/mpas_atm/mpas_dart_obs_preprocess models/mpas_ocn/readme models/mpas_ocn/model_to_dart models/NCOMMAS/readme models/noah/readme models/null_model/readme models/PBL_1d/readme models/pe2lyr/readme models/POP/readme models/POP/dart_pop_mod models/ROMS/readme models/rose/readme models/seir/readme models/simple_advection/readme models/sqg/readme models/template/new_model models/tiegcm/readme models/wrf_hydro/readme models/wrf/readme models/wrf/WRF_DART_utilities/replace_wrf_fields models/wrf/WRF_DART_utilities/wrf_dart_obs_preprocess models/template/readme models/utilities/default_model_mod
guide/contributors-guide guide/requesting-features-reporting-bugs guide/mailing-list
guide/Manhattan_diffs_from_Lanai guide/forward_operator guide/netcdf_inflation_files guide/state_structure guide/filter_async_modes guide/distributed_state
assimilation_code/location/channel/location_mod assimilation_code/location/location_mod assimilation_code/location/oned/location_mod assimilation_code/location/threed_cartesian/location_mod assimilation_code/location/threed_sphere/location_mod assimilation_code/programs/obs_seq_verify/obs_seq_verify assimilation_code/programs/wakeup_filter/wakeup_filter assimilation_code/programs/compare_states/compare_states assimilation_code/programs/gen_sampling_err_table/gen_sampling_err_table assimilation_code/programs/perturb_single_instance/perturb_single_instance assimilation_code/programs/system_simulation/system_simulation assimilation_code/programs/compute_error/compute_error assimilation_code/programs/preprocess/preprocess assimilation_code/programs/obs_impact_tool/obs_impact_tool assimilation_code/programs/create_fixed_network_seq/create_fixed_network_seq assimilation_code/programs/obs_loop/obs_loop assimilation_code/programs/perfect_model_obs/perfect_model_obs assimilation_code/programs/obs_selection/obs_selection assimilation_code/programs/obs_sequence_tool/obs_sequence_tool assimilation_code/programs/integrate_model/integrate_model assimilation_code/programs/obs_diag/oned/obs_diag assimilation_code/programs/obs_diag/threed_cartesian/obs_diag assimilation_code/programs/obs_diag/threed_sphere/obs_diag assimilation_code/programs/fill_inflation_restart/fill_inflation_restart assimilation_code/programs/obs_seq_coverage/obs_seq_coverage assimilation_code/programs/advance_time/advance_time assimilation_code/programs/model_mod_check/model_mod_check assimilation_code/programs/closest_member_tool/closest_member_tool assimilation_code/programs/filter/filter assimilation_code/programs/obs_keep_a_few/obs_keep_a_few assimilation_code/programs/create_obs_sequence/create_obs_sequence assimilation_code/programs/obs_seq_to_netcdf/obs_seq_to_netcdf assimilation_code/programs/obs_common_subset/obs_common_subset assimilation_code/modules/utilities/ensemble_manager_mod assimilation_code/modules/utilities/random_seq_mod assimilation_code/modules/utilities/mpi_utilities_mod assimilation_code/modules/utilities/time_manager_mod assimilation_code/modules/utilities/utilities_mod assimilation_code/modules/utilities/types_mod assimilation_code/modules/utilities/schedule_mod assimilation_code/modules/observations/obs_kind_mod assimilation_code/modules/observations/DEFAULT_obs_kind_mod assimilation_code/modules/observations/obs_sequence_mod assimilation_code/modules/assimilation/assim_model_mod assimilation_code/modules/assimilation/assim_tools_mod assimilation_code/modules/assimilation/cov_cutoff_mod assimilation_code/modules/assimilation/obs_model_mod assimilation_code/modules/assimilation/reg_factor_mod assimilation_code/modules/assimilation/adaptive_inflate_mod assimilation_code/modules/assimilation/quality_control_mod assimilation_code/modules/assimilation/filter_mod
developer_tests/location/location_mod developer_tests/forward_operators/readme developer_tests/utilities/PrecisionCheck
observations/forward_operators/obs_def_gps_mod observations/forward_operators/obs_def_dew_point_mod observations/forward_operators/obs_def_ocean_mod observations/forward_operators/obs_def_1d_state_mod observations/forward_operators/obs_def_radar_mod observations/forward_operators/DEFAULT_obs_def_mod observations/forward_operators/obs_def_mod observations/forward_operators/obs_def_rttov_mod
models/CESM/doc/setup_guidelines
models/MITgcm_ocean/create_ocean_obs models/NCOMMAS/dart_to_ncommas models/NCOMMAS/ncommas_to_dart
copyright Changelog <CHANGELOG>
guide/404
Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn and A. Arellano, 2009 The Data Assimilation Research Testbed: A Community Facility. Bulletin of the American Meteorological Society, 90, 1283-1296, doi:10.1175/2009BAMS2618.1↩
Anderson, J. L., 2022: A Quantile-Conserving Ensemble Filter Framework. Part I: Updating an Observed Variable. Monthly Weather Review, 150, 1061–1074, doi:10.1175/MWR-D-21-0229.1↩
Anderson, J. L., 2023: A Quantile-Conserving Ensemble Filter Framework. Part II: Regression of Observation Increments in a Probit and Probability Integral Transformed Space. Monthly Weather Review, 151, 2759–2777, doi:10.1175/MWR-D-23-0065.1↩
Anderson, J. L., Riedel, C., Wieringa, M., Ishraque, F., Smith, M., Kershaw, H. 2023: A Quantile-Conserving Ensemble Filter Framework. Part III: Data Assimilation for Mixed Distributions with Application to a Low-Order Tracer Advection Model. Monthly Weather Review [Manuscript submitted for publication]↩