Important capabilities of DART
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 realworld forecasts of chaotic systems with real observations.
Models supported by DART
A full list of models can be found here, but in brief the models supported by DART include:
Model 
Latest version 
Model 
Latest version 

lorenz_63 
Manhattan 
lorenz_84 
Manhattan 
lorenz_96 
Manhattan 
lorenz_96_2scale 
Manhattan 
lorenz_04 
Manhattan 
simple_advection 
Manhattan 
bgrid_solo 
Manhattan 
WRF 
Manhattan 
MPAS 
Manhattan 
ATM 
Manhattan 
ROMS 
Manhattan 
CESM 
Manhattan 
CAMFV 
Manhattan 
CAMCHEM 
Manhattan 
WACCM 
Manhattan 
WACCMX 
Manhattan 
CICE 
Manhattan 
CM1 
Manhattan 
FESOM 
Manhattan 
NOAHMP 
Manhattan 
WRFHydro 
Manhattan 
GCCOM 
Lanai 
LMDZ 
Lanai 
MITgcm_ocean 
Lanai 
NAAPS 
Lanai 
AM2 
Lanai 
CAMSE 
Manhattan 
CLM 
Manhattan 
COAMPS 
Lanai 
COSMO 
Lanai 
Dynamo 
Lanai 
GITM 
Lanai 
Ikeda 
Lanai 
JULES 
Lanai 
MPAS_ocean 
Lanai 
null_model 
Lanai 
openggcm 
Lanai 
PARFLOW 
Lanai 
sqg 
Lanai 
TIEGCM 
Lanai 
WRFCHEM 
Lanai 
ECHAM 
Prior to Lanai 
PBL_1d 
Prior to Lanai 
MITgcm_annulus 
Prior to Lanai 
forced_barot 
Prior to Lanai 
pe2lyr 
Prior to Lanai 
ROSE 
Prior to Lanai 
CABLE 
Prior to Lanai 
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 userdefined 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:
Observation 
Directory 
Format 

Atmospheric Infrared Sounder satellite retrievals 
AIRS 
HDFEOS 
Advanced Microwave Sounding Unit brightness temperatures 
AIRS 
netCDF 
Aviso: satellite derived sea surface height 
Aviso 
netCDF 
Level 4 Flux Tower data from AmeriFlux 
Ameriflux 
Commaseparated text 
Level 2 soil moisture from COSMOS 
COSMOS 
Fixedwidth text 
Doppler wind lidar 
DWL 
ASCII text 
GPS retrievals of precipitable water 
GPSPW 
netCDF 
GSI observation file 
GSI2DART 
Fortran binary 
Global TemperatureSalinity Profile Program (GTSPP) 
GTSPP 
netCDF 
Meteorological Assimilation Data Ingest System (MADIS) 
MADIS 
netCDF 
MIDAS ionospheric obs 
MIDAS 
netCDF 
MODIS satellite retrievals 
MODIS 
Commaseparated text 
NCEP/prep_bufr 
PREPBUFR 

NCEP ASCII observations 
NCEP/ascii_to_obs 
NCEP text files 
ROMS verification observations 
ROMS 
netCDF 
Satellite winds from SSEC 
SSEC 
ASCII text 
Sea surface temperature 
SST 
netCDF 
Special Sensor Ultraviolet Spectrographic Imager (SSUSI) retrievals 
SSUSI 
netCDF 
World Ocean Database (WOD) 
WOD 
World Ocean Database packed ASCII 
National Snow and Ice Data Center sea ice obs 
cice 
Binary sea ice 
VTEC Madrigal upper atmospheric obs 
gnd_gps_vtec 
ASCII text 
GPS obs from COSMIC 
gps 
netCDF 
Oklahoma Mesonet MDF obs 
ok_mesonet 
Oklahoma Mesonet MDF files 
QuikSCAT scatterometer winds 
quikscat 
HDF 4 
Radar reflectivity/radial velocity obs 
Radar 
WSR88D (NEXRAD) 
MODIS Snowcover Fraction obs 
snow 
General text 
Text file (e.g. spreadsheet) obs 
Text 
General text 
Total precipitable water from AQUA 
tpw 
HDFEOS 
Automated Tropical Cyclone Forecast (ATCF) obs 
Tropical Cyclones 
Fixed width text 
LITTLE_R obs 
var 
littler 
MM5 3DVAR radar obs 
var 
MM5 3DVAR 2.0 Radar data files 
Data assimilation algorithms available in DART
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 
2 
ENKF (Ensemble Kalman Filter) 
Evensen, G., 2003. 4 
3 
Kernel filter 

4 
Observation Space Particle filter 

5 
Random draw from posterior 
None. IMPORTANT: (contact dart @ ucar.edu before using) 
6 
Deterministic draw from posterior with fixed kurtosis 
None. IMPORTANT: (contact dart @ ucar.edu before using) 
7 
Boxcar kernel filter 

8 
Rank Histogram filter 
Anderson, J. L., 2010. 5 
9 
Particle filter 
Poterjoy, J., 2016. 6 
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):
Flavor # 
Inflation flavor name 
References 

0 
No inflation 
n/a 
1 
(Not Supported) 
n/a 
2 
Spatiallyvarying statespace (Gaussian) 
Anderson, J. L., 2009. 7 
3 
Spatiallyfixed statespace (Gaussian) 
Anderson, J. L., 2007. 8 
4 
Relaxation to prior spread (posterior inflation only) 
Whitaker, J.S. and T.M. Hamill, 2012. 9 
5 
Enhanced spatiallyvarying statespace (inverse gamma) 
El Gharamti M., 2018. 10 
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):
Loc # 
Localization type 
References 

1 
GaspariCohn eq. 4.10 
Gaspari, G. and Cohn, S. E., 1999. 12 
2 
Boxcar 
None 
3 
Ramped boxcar 
None 
The following image depicts all three of these options:
References
 1
Anderson, J. L., 2001: An Ensemble Adjustment Kalman Filter for Data Assimilation. Monthly Weather Review, 129, 28842903. doi:10.1175/15200493(2001)129<2884:AEAKFF>2.0.CO;2
 2
Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Monthly Weather Review, 131, 634642. doi:10.1175/15200493(2003)131<0634:ALLSFF>2.0.CO;2
 3
Anderson, J., Collins, N., 2007: Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation. Journal of Atmospheric and Oceanic Technology, 24, 14521463. doi:10.1175/JTECH2049.1
 4
Evensen, G., 2003: The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation. Ocean Dynamics. 53(4), 343–367. doi:10.1007%2Fs1023600300369
 5
Anderson, J. L., 2010: A NonGaussian Ensemble Filter Update for Data Assimilation. Monthly Weather Review, 139, 41864198. doi:10.1175/2010MWR3253.1
 6
Poterjoy, J., 2016: A localized particle filter for highdimensional nonlinear systems. Monthly Weather Review, 144 5976. doi:10.1175/MWRD150163.1
 7
Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus A, 61, 7283, doi:10.1111/j.16000870.2008.00361.x
 8
Anderson, J. L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus A, 59, 210224, doi:10.1111/j.16000870.2006.00216.x
 9
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/MWRD1100276.1
 10
El Gharamti M., 2018: Enhanced Adaptive Inflation Algorithm for Ensemble Filters. Monthly Weather Review, 2, 623640, doi:10.1175/MWRD170187.1
 11
Anderson, J.L., 2012: Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation. Monthly Weather Review, 140, 2359–2371. doi:10.1175/MWRD1100013.1
 12
Gaspari, G. and Cohn, S. E., 1999: Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125, 723757. doi:10.1002/qj.49712555417