Can I run my model with DART?
The DART team often collaborates with other groups to help write the interface code to a new model. The most efficient way to get started is to meet with DAReS staff either virtually or in person, to discuss what is involved in supporting a different model.
If part of your team isn’t familiar with data assimilation yet, you should review the introductory material in this documentation and and also look at work through the concepts in the DART Tutorial.
Goals of using DART
DART is the Data Assimilation Research Testbed. It is a collection of tools, routines, and scripts that allow users to build custom solutions and explore a variety of DA related efforts. It is not a turnkey system; it must be built before use and is often customized based on needs and goals.
DART is often used for the following types of projects:
Learning about Data Assimilation (DA)
Using DART with an existing model and supported observations
Using DART with a new model: Instructions for porting a new model to DART
Using new observations with DART in an existing model
Using both a new model and new observations with DART
Using DART to teach DA
You can view a list of models that are already supported at Supported Models and a list of supported observations at Converter programs.
Everything on this “possible goals” list except adding support for a new model can generally be done by a single user with minimal help from the DART team. Therefore this discussion focuses only on adding a new model to DART.
Should I consider using DART with my model?
DART is an ensemble-based DA system. It makes multiple runs of a model with slightly different inputs and uses the statistical distribution of the results to decide how to adjust the model state to be more consistent with the observations.
The advantage of ensemble systems is that no changes to the model itself are required. The disadvantage is that multiple runs of the model are needed and this can be computationally expensive.
Simple models can be added to DART with a single person effort, but larger, more complex models can require multiple person-months with support from the DART team to add the interfaces and scripts needed to perform a large-scale DA experiment.
The DART code is in Fortran. The supporting scripts and tools are a mix of shell scripts and python. The model can be written in any language; it will only be run and the input and output files will be used by DART.
Things to discuss before beginning
Is your model appropriate for any kind of DA?
If your model isn’t chaotic, you don’t need data assimilation. In non-chaotic models, you can improve your predictions by running the model, examining the difference between the prediction and the observations, inverting the equations inside the model to compute how different inputs would have produced outputs closer to the observations.
Chaotic models do not have a simple relationship between inputs and outputs. There are internal feedbacks and non-linear behaviors that make it difficult to adjust the inputs to make the outputs better match the observations.
What is your model state?
“Model state” has a very specific definition that can be the source of much confusion if someone running a model has not thought about DA before. Formally it is the minimal set of variables that must be saved when a model stops so it can be restarted again exactly.
At first glance this means all the variables on the right side of the equals sign for the governing equations of the system. However many models which have not been designed with DA in mind may have no clear time when all parts of the model are at a consistent time. e.g. some variables may be 1/2 timestep ahead or behind others. Some derived variables may be expensive to compute and so are precomputed and stored and not recomputed. If the DA process changes the state variables all derived variables must be recomputed before proceeding.
Restart files often store many more variables than the minimal set needed to restart the model. Often other variables are used in diagnostic routines or are of interest on their own. Generally these are not considered part of the model state.
How is your model execution controlled?
Generally larger and more complex models have an environment they are expecting to run in/with. e.g. scripts to control the execution parameters, or input parameter files; how many processors are used in a parallel system, how the tasks are distributed over the hardware; how long does the execution run, in model time, and what variables are written to the output files.
For DA, at a minimum there must be a way to control how long the model runs before it writes out the results and exits.
For large models, the DA filter process is a large parallel program generally requiring a multi-processor supercomputer or cluster. Many models themselves are large parallel programs, so there can be issues with how the switch between model and DA process is done.
New or adjusted scripting is generally required to include the DA process in the overall execution flow.
Cycling with a DA system
The DA process is generally a cycle of running the model for a certain amount of model time, then running the DA filter to adjust the model state before continuing.
These two steps happen over and over as observations are available to guide the adjustments to the model state.
Models may be written with the assumption that startup costs are only done once and then the model runs for a long period of time. When used with DA models are generally started and stopped after running a relatively short amount of model time. If model startup time is long this can result in unacceptably slow performance.
A small amount of round-off error is often introduced when a model writes restart files before stopping. So running a model N timesteps forward vs. running N/2, stopping, writing restart files, starting, reading restart files, and finishing the last N/2 timesteps will may not result in identical values. Large changes suggest that the model is not a good candidate for a cycling DA system.
The goal is to minimize the differences. This can require small or large changes to make the model behave as expected with repeated starting and stopping.
Some models include external forcing, for example boundary conditions from a separate model. If cycling the forcing files may need to be updated periodically outside of the DA system.
What coordinate system is used by your model?
Coordinate systems use a series of numbers to describe the relationship in space between parts of the model state and where observations are located. In Earth-system models, often a latitude-longitude-vertical coordinate system is used. X,Y,Z Cartesian coordinates are also used to describe 3D space. Other options include cyclindrical or spherical coordinates, and unit-line, -square or -cube coordinates with cyclical boundaries.
Only a single coordinate system can be selected and it applies to both the model state locations as well as the observations.
If the model coordinate system is based on some other space it may be necessary to transform it into physical coordinates before running DA. For example, some models compute in spectral space and the output must be translated into a physical space before DA can be done.
What file format is used for model restart files?
DART reads and writes NetCDF file format. Many earth-system models already use this format. If the model does not, converter programs from the native format to NetCDF and back are needed. NetCDF is a self-describing format with metadata that allows DART to read and process model data without additional configuration files.
What quantities are in the model state?
DART defines a “Quantity” as the fundamental physical object a value is measuring. Examples are Temperature, Pressure, Salinity, etc. Each value in a model state must be associated with a defined quantity.
What observations are you intending to assimilate?
Any observation you intend to assimilate requires a method to compute an “expected value” based on the model state. Often the observation is of the same quantity as exists in the model state, so computing the expected value is a direct process.
Other times the expected value is a function of quantities in the model state, and code called a “forward operator” uses one or more quantities from the model state and computes the expected value.
If the model state does not contain quantities that are needed to compute an expected value, auxiliary data values can be read and used to compute the expected value. But if the expected value cannot be computed or is not in some way a function of the model state, the observations cannot be assimilated.
How are you going to generate your initial ensemble?
Most models don’t have an existing ensemble of states ready for ingestion into an ensemble DA system. Options for generating the initial ensemble include adding random perturbations to a single variable in a single state, perturbing forcing variables differently for each ensemble member, or perturbing the entire state.
For models which have a lot of error growth it may be enough to add a very small amount of noise to a single variable in the state to generate an ensemble of states and then run them forward in time with the model to generate states which have sufficient differences.
For models with slower error growth, larger perturbations may be needed, a longer model advance time before starting assimilation, or perturbations of forcing or boundary files may be needed.
The goal is to generate a set of model states which are different but contain internally-consistent values.
An ensemble of states without sufficient differences (spread) will reject assimilating observations.
What code is required to interface a model with DART?
There is a single FORTRAN module that hides the model details from the rest of the DART system. Generally the routines which require the most work are the interpolation routine, followed by the metadata routine and the “get close” localization routines.
Given an observation quantity and location, the model interface routines must return an array of values, one for each ensemble member. The values must be the best estimate of what a real instrument would return if the real state of the system were each of the ensemble values.
For a regular grid this can be computed fairly simply with routines already provided in the DART system. It involves locating the grid values that enclose the observation location, and doing bi- or tri-linear interpolation to the actual location.
However, many models have non-regular grid, especially in the vertical coordinates for an earth-system-based model. Or the grid can be an irregular mesh or deformed mesh. It may take searching or transforms to identify the closest values in the model state to use for interpolation.
Given an offset into the model state, the model interface routines must return the location in the selected coordinate system, and the quantity at that offset.
There are routines provided which simplify this for regular or deformed grids, so this generally is not too complex but may require additional arrays for irregular grids or unstructured grids.
DART bases the impact of observations on the model state on the correlation between the array of predicted observation values, the actual observation value and error, and the array of model state values.
In practice observations are only correlated with model state values “close” to the observation. Spurrious correlations can occur which degrade the results after assimilation. Also there are efficiency gains if only parts of the model state which are “close” to the observation are processed.
DART includes routines which can compute what part of the state are close to a given observation. However some models have special considerations for whether they want to control the impact of observations on parts of the model state and this can be adjusted based on code added to the model-specific parts of getting close observations and model state.
Most Earth System models use Latitude and Longitude for horizontal coordinates or can generate them if needed (e.g. spectral models can transform their state into Lat/Lon coords). But often vertical coordinates pose additional complications.
If the model and the observations both use the same coordinates for vertical, e.g. pressure or height, then there are no need for conversion routines. But some models use terrain-following coordinates, or a mix of pressure and terrain coordinates. Observation vertical locations can be reported in height or in pressure.
Additionally, if vertical localization is to be done in a different coordinate than the model or observations (e.g. scale height), then conversion routines are needed.
The interface code may need to read in additional arrays from the model in order to convert the vertical coordinates accurately.
During the run of filter there are two options for when vertical conversion is done: all at the start, or on demand. If the observations to be assimilated are expected to impact all or almost all of the state, doing all vertical conversion at the start is more efficient. If the observations are expected to impact only a small percentage of the state variables then doing it on demand is more efficient. The options here are namelist selectable at runtime and the impact on total runtime can be easily measured and compared.