This is an excerpt from Applying the science and technology of data assimilation by Brian Bevirt 07/11/2017 as part of a CISL News series describing the many ways CISL improves modeling beyond providing supercomputing systems and facilities.These plots show measured and modeled zonal mean temperatures between 70N and 90N during the January 2009 sudden warming of the stratosphere. The bottom plot shows the observed temperatures (in degrees Kelvin, see legend at right), the center plot shows how this state of the atmosphere was simulated by the specified-dynamics version of the WACCM model, and the top plot shows WACCM's improved result after using DART to assimilate middle atmosphere observations.
The key point in this figure is that WACCM+DART captures both the stratosphere warming and mesosphere cooling that are seen in the observations. Also seen in the specified-dynamics version of WACCM, the elevated stratopause that forms at high altitudes around day 30 descends too fast compared to the observations. The elevated stratopause is maintained at a high altitude in the WACCM+DART simulation. This has implications for the descent of species from the mesosphere into the stratosphere. Accurate representation of the mesosphere dynamics is important for the ionosphere variability during sudden stratosphere warming events. (Figure courtesy of Nick Pedatella, HAO)
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Nick Pedatella, email@example.com Hanli Liu, firstname.lastname@example.org Jing Liu, email@example.com
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.