Because observations and model predictions both have errors, it is
important to quantify this uncertainty when making forecasts.
DART's ensemble data assimilation methods provide modelers with sets of
equally likely initial conditions for producing an ensemble of forecasts.
Differences between these forecasts give information that can help scientists
identify the most important shortcomings in individual models. DART
ensemble assimilation tools can also help modelers `tune' parameters in
their models so model forecasts better fit observations.
A recent example of how DART benefits model development appears in the
figure. Nick Pedatella (HAO) applied DART to the Whole Atmosphere Community
Climate Model (WACCM) and used DART to quickly improve the model's ability
to produce short-term forecasts. DART helped him analyze the model's
outputs to identify the types of issues that could be involved. The
model developers then addressed those issues in the model's code and
significantly improved its predictions. This work is valuable to three
NCAR labs: CGD developed the WACCM dynamics, HAO provided code development
for WACCM to simulate space weather, and ACOM uses WACCM for
high-altitude atmospheric chemistry. The figure compares model short-term
forecasts generated with DART (top) to another method of making the model
close to observations (specified dynamics WACCM, middle) and the
available observations (bottom).
Data assimilation - with its comparison of model outputs to observations -
is a key technology for modeling because it is the best method we have
to evaluate and validate prediction models. We have only recently developed
the capability to perform data assimilation on climate models.
Historically, it has been too difficult and expensive for modeling
groups to do that, but as computers have become more capable and software
such as DART has been developed, validation of model predictions is now
practical. The ongoing development and improvement of models requires
scientific assurance that a model is producing accurate forecasts for
the right reasons. Data assimilation helps developers progress from a
model that produces something that isn't right, to understanding why it
isn't right, to code changes that produce consistently accurate results.
Finally, data assimilation adds meaning to those results by quantifying
the uncertainty in model predictions.
People involved in this project are: N. Pedatella, H. Liu, J. Liu, and K. Raeder.