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In this section we discuss the capabilities of DART that may be of interest to
the user. This is a partial list of all of the functionality that is available
in DART, and additional capabilities and improvements are continually being
added.
As mentioned above, DART allows for both OSSE and OSE systems of models large
and small. This allows users to test both theoretical limits of DA, models, and
observations with idealized experiments as well as to improve actual real-world
forecasts of chaotic systems with real observations.
The models listed as “Prior to Lanai” will take some additional work to
integrate with a supported version of DART; please contact the dart @ ucar.edu
team for more information. The versions listed as “Lanai” will be ported to the
Manhattan version of DART depending on the needs of the user community as well
as the availablity of resources on the DART team.
Observation converters provided by DART
Given a way to compute the expected observation value from the model state, in
theory any and all observations can be assimilated by DART through the
obs_seq.out file. In practice this means a user-defined observation
converter is required. DART provides many observation converters to make this
process easier for the user. Under the directory
DART/observations/obs_converters there are multiple subdirectories, each
of which has at least one observation converter. The list of these directories
is as follows:
DART allows users to test the impact of using multiple different types of
algorithms for filtering, inflation/deflation, and covariance localization.
DART offers numerous filter algorithms. These determine how the posterior
distribution is updated based on the observations and the prior ensemble. The
following table lists the filters supported in DART along with their type (set
by filter_kind in input.nml under the “assim_tools_nml” section):
Filter #
Filter Name
References
1
EAKF (Ensemble Adjustment
Kalman Filter)
Anderson, J. L., 2001. [1]Anderson, J. L., 2003. [2]Anderson, J., Collins, N., 2007. [3]
DART also has several inflation algorithms available for both prior (the
first value in the namelist) and posterior (the second value in the namelist).
The following table lists the inflation “flavors” supported in DART along with
their type number (set by inf_flavor in input.nml under the “filter_nml”
section):
DART has the ability to correct for sampling errors in the regression
caused by finite ensemble sizes. DART’s sampling error correction algorithm
(and localization algorithm) is described in Anderson, J.L., 2012 [11]
Sampling error correction can be turned on or off via the sampling_error_correction
variable in the input.nml under the “assim_tools_nml” section.
The following covariance localization options are available
(set by select_localization in input.nml under the “cov_cutoff_nml” section):
Anderson, J., Collins, N., 2007:
Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation.
Journal of Atmospheric and Oceanic Technology, 24, 1452-1463.
doi:10.1175/JTECH2049.1
Anderson, J. L., 2009:
Spatially and temporally varying adaptive covariance inflation for ensemble filters.
Tellus A, 61, 72-83,
doi:10.1111/j.1600-0870.2008.00361.x
Anderson, J. L., 2007:
An adaptive covariance inflation error correction algorithm for ensemble filters.
Tellus A, 59, 210-224,
doi:10.1111/j.1600-0870.2006.00216.x
Whitaker, J.S. and T.M. Hamill, 2012:
Evaluating Methods to Account for System Errors in Ensemble Data Assimilation.
Monthly Weather Review, 140, 3078–3089,
doi:10.1175/MWR-D-11-00276.1
Gaspari, G. and Cohn, S. E., 1999:
Construction of correlation functions in two and three dimensions.
Quarterly Journal of the Royal Meteorological Society, 125, 723-757.
doi:10.1002/qj.49712555417
