There are two broad classes of models supported by DART. Some are 'low-order' models, generally single-threaded, subroutine-callable, and idealized: there are no real observations of these systems. The other class of models are 'high-order' models. There are real observations of these systems. Or at least, we like to think so ...
The Ikeda model is a 2D chaotic map useful for visualization data assimilation updating directly in state space. There are three parameters: a, b, and mu. The state is 2D, x = [X Y]. The equations are:
X(i+1) = 1 + mu * ( X(i) * cos( t ) - Y(i) * sin( t ) ) Y(i+1) = mu * ( X(i) * sin( t ) + Y(i) * cos( t ) ),
where
t = a - b / ( X(i)**2 + Y(i)**2 + 1 )
Note the system is time-discrete already, meaning there is no delta_t. The system stems from nonlinear optics (Ikeda 1979, Optics Communications). Interface written by Greg Lawson. Thanks Greg!
This is the 3-variable model as described in:
Lorenz, E. N. 1963. Deterministic nonperiodic flow.
J. Atmos. Sci. 20, 130-141.
The system of equations is:
X' = -sigma*X + sigma*Y Y' = -XZ + rX - Y Z' = XY -bZ
This model is based on:
Lorenz E. N., 1984: Irregularity: A fundamental property of the atmosphere.
Tellus, 36A, 98-110.
The system of equations is:
X' = -Y^2 - Z^2 - aX + aF Y' = XY - bXZ - Y + G Z' = bXY + XZ - Z
Where a, b, F, and G are the model parameters.
This model provides interesting off-attractor transients that behave something like gravity waves.
This is the model we use to become familiar with new architectures, i.e.,
it is the one we use 'first'. It can be called as a subroutine or as a separate
executable. We can test this model both single-threaded and mpi-enabled.
Quoting from the Lorenz 1998 paper:
... the authors introduce a model consisting of 40 ordinary differential equations, with the dependent variables representing values of some atmospheric quantity at 40 sites spaced equally about a latitude circle. The equations contain quadratic, linear, and constant terms representing advection, dissipation, and external forcing. Numerical integration indicates that small errors (differences between solutions) tend to double in about 2 days. Localized errors tend to spread eastward as they grow, encircling the globe after about 14 days.
...
We have chosen a model with J variables, denoted by X1, ..., XJ; in most of our experiments we have let J = 40. The governing equations are:
dXj/dt = (Xj+1 - Xj-2)Xj-1 - Xj + F (1)for j = 1, ..., J. To make Eq. (1) meaningful for all values of j we define X-1 = XJ-1, X0 = XJ, and XJ+1 = X1, so that the variables form a cyclic chain, and may be looked at as values of some unspecified scalar meteorological quantity, perhaps vorticity or temperature, at J equally spaced sites extending around a latitude circle. Nothing will simulate the atmosphere's latitudinal or vertical extent.
The forced_lorenz_96 model implements the standard L96 equations except that the forcing term, F, is added to the state vector and is assigned an independent value at each gridpoint. The result is a model that is twice as big as the standard L96 model. The forcing can be allowed to vary in time or can be held fixed so that the model looks like the standard L96 but with a state vector that includes the constant forcing term. An option is also included to add random noise to the forcing terms as part of the time tendency computation which can help in assimilation performance. If the random noise option is turned off (see namelist) the time tendency of the forcing terms is 0.
This is the Lorenz 96 2-scale model, documented in Lorenz (1995). It also has the option of the variant on the model from Smith (2001), which is invoked by setting local_y = .true. in the namelist. The time step, coupling, forcing, number of X variables, and the number of Ys per X are all specified in the namelist. Defaults are chosen depending on whether the Lorenz or Smith option is specified in the namelist. Lorenz is the default model. Interface written by Josh Hacker. Thanks Josh!
The reference for these models is Lorenz, E.N., 2005: Designing
chaotic models. J. Atmos. Sci., 62, 1574-1587.
Model II is a single-scale model, similar to Lorenz 96, but with
spatial continuity in the waves. Model III is a two-scale
model. It is fudamentally different from the Lorenz 96 two-scale
model because of the spatial continuity and the fact that both scales
are projected onto a single variable of integration. The scale
separation is achived by a spatial filter and is therefore not perfect
(i.e. there is leakage). The slow scale in model III is model II,
and thus model II is a deficient form of model III. The basic
equations are documented in Lorenz (2005) and also in the model_mod.f90
code. The user is free to choose model II or III with a Namelist
variable.
This model is on a periodic one-dimensional domain. A wind field is modeled using Burger's Equation with an upstream semi-lagrangian differencing. This diffusive numerical scheme is stable and forcing is provided by adding in random gaussian noise to each wind grid variable independently at each timestep. An Eulerian option with centered-in-space differencing is also provided. The Eulerian differencing is both numerically unstable and subject to shock formation. However, it can sometimes be made stable in assimilation mode (see recent work by Majda and collaborators).
In roughly the order they were supported by DART.
This is a dynamical core for B-grid dynamics using the Held-Suarez forcing. The resolution is configurable, and the entire model can be run as a subroutine. Status: supported.
This model is a 2-layer, isentropic, primitive equation model on a sphere. Status: orphaned.
The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. More people are using DART with WRF than any other model. Note: The actual WRF code is not distributed with DART. Status: supported.
There are many DART-supported versions of CAM. The frozen version of the Community Climate System Model (CCSM4.0) uses the Community Atmosphere Model (CAM4). The Community Earth System Model (CESM 1.0) uses the Community Atmosphere Model (CAM5); the latest in a series of global atmosphere models developed at NCAR for the weather and climate research communities. Status: both are supported (as are some earlier releases).
The PBL model is a single column version of the WRF model. In this instance, the necessary portions of the WRF code are distributed with DART. Status: supported - but looking to be adopted.
The MITgcm annulus model as configured for this application within DART is a non-hydrostatic, rigid lid, C-grid, primitive equation model utilizing a cylindrical coordinate system. For detailed information about the MITgcm, see http://mitgcm.org Status: orphaned - and looking to be adopted.
The rose model is for the stratosphere-mesosphere and was used by Tomoko Matsuo (now at CU-Boulder and NOAA) for research in the assimilation of observations of the Mesosphere Lower-Thermosphere (MLT). Note: the model code is not distributed with DART. Status: orphaned
The MIT ocean GCM version 'checkpoint59a' is the foundation of this implementation. It was modified by Ibrahim Hoteit (then of Scripps) to accomodate the interfaces needed by DART. Status: supported - but looking to be adopted.
The FMS AM2 model is GFDL's atmosphere-only code using observed sea surface temperatures, time-varying radiative forcings (including volcanos) and time-varying land cover type. This version of AM2 (also called AM2.1) uses the finite-volume dynamical core (Lin 2004). Robert Pincus (CIRES/NOAA ESRL PSD1) and Patrick Hoffman (NOAA) wrote the DART interface and are currently using the system for research. Note: the model code is not distributed with DART. Status: supported
The DART interface was originally written and supported by Tim Whitcomb. The following model description is taken from the COAMPS overview web page:
The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) has been developed by the Marine Meteorology Division (MMD) of the Naval Research Laboratory (NRL). The atmospheric components of COAMPS, described below, are used operationally by the U.S. Navy for short-term numerical weather prediction for various regions around the world.
Note: the model code is not distributed with DART. Status: supported
The Parallel Ocean Program (POP) comes in two variants. Los Alamos National Laboratory provides POP Version 2.0 which has been modified to run in the NCAR Community Climate System Model (CCSM) framework. As of November 2009, the CCSM-POP version is being run. The LANL-POP version is nearly supported - and some extensions useful for data assimilation in general have been proposed to LANL, who have agreed in principle to implement the changes. Fundamentally, the change is an additional restart option in which the first timestep after an assimilation is a Eulerian timestep (similar to a cold start). Note: the souce code for POP is not distributed with DART. Status: actively being developed
The code distribution was getting 'cluttered' with datasets,
boundary conditions, intial conditions, ... large files that were not
necessarily interesting to all people who downloaded the DART code.
Worse, subversion makes a local hidden copy of the original repository
contents, so the penalty for being large is doubled.
It just made sense to make all the large files available on
as 'as-needed' basis.
To keep the size of the DART distribution down we have a separate
www-site to provide some observation sequences, initial conditions,
and general datasets.
It is our intent to populate this site with some 'verification' results,
i.e. assimilations that were known to be 'good' and that should be fairly
reproducible - appropriate to test the DART installation.
Please be patient as I make time to populate this directory.
(yes, 'make', all my 'found' time is taken ...)
Observation sequences can be found at
http://www.image.ucar.edu/pub/DART/Obs_sets.
Verification experiments will be posted to
http://www.image.ucar.edu/pub/DART/VerificationData as soon as
I can get to it. These experiments will consist of initial conditions files
for testing different high-order models like CAM, WRF, POP ...
The low-order models are already distributed with verification data in
their work directories.
Useful bits for CAM can be found at
http://www.image.ucar.edu/pub/DART/CAM.
Useful bits for WRF can be found at
http://www.image.ucar.edu/pub/DART/WRF.
Useful bits for MPAS_ocn can be found at
http://www.image.ucar.edu/pub/DART/MPAS_OCN.