Advance data assimilation science

DART science
Insufficient ensemble spread, a key problem for ensemble filters, can be corrected by inflation that increases the spread of an ensemble forecast before assimilation. The adaptive inflation values from an improved DART algorithm are shown for the surface pressure field from a CAM reanalysis (top) and differences from the inflation values from the old algorithm are also shown (bottom). The new algorithm leads to improved ensemble mean reanalyses with better-calibrated ensemble spread.

Data assimilation is providing rapid advances in geophysical studies. The Data Assimilation Research Section (DAReS) of IMAGe performs fundamental research on ensemble data assimilation methodologies for application across a wide range of geophysical problems. DAReS develops and maintains a software facility for ensemble data assimilation called the Data Assimilation Research Testbed (DART). DAReS also provides support to a growing community of NCAR, university, and government laboratory partners who are applying ensemble data assimilation methods.

DART provides ensemble data assimilation (DA) tools that use state-of-the-art statistical methods for combining model forecasts with observations to produce initial conditions for forecasts along with estimates of uncertainty. DART tools can also diagnose and improve both models and observing systems. The use of ensembles of forecasts means that DART applications are among the largest and most computing-intensive in the geosciences, so effective use of supercomputing facilities using advanced scalable algorithms is essential. All of these aspects of DART are key to meeting CISL’s strategic goal to “Enhance the effective use of current and future computational systems by improving mathematical and computational methods for Earth System models and related observations” and in particular the imperative to “advance data-centric research.”

The Manhattan release of DART was completed and released in March 2017. Manhattan includes parallel ensemble Kalman filter implementations that are memory scalable, allowing DART to be used with the largest configurations of NCAR community models, for instance 1/10-degree POP. Manhattan also provides a simpler and more powerful capability to communicate between DART and models that uses direct access to model NetCDF files. Interfaces between all NCAR community models that use DART have been updated to use the Manhattan capabilities. A number of new algorithms for data assimilation and associated diagnostics have been added to DART. These include an enhanced adaptive inflation algorithm that leads to improved ensemble mean model state estimates with more accurate estimates of uncertainty. A new ensemble filter algorithm, the Gamma/Inverse gamma filter has been added to DART in collaboration with developers at the Naval Research Labs. This is part of an ongoing program to facilitate careful comparison between a wide range of ensemble-based assimilation algorithms.

Data assimilation research in CISL is supported by NSF Core funding plus Grant 16-013 from the University of New Hampshire’s Open Geospace General Circulation Model program, Grant N0014-15-1-2300 (UCSC subaward A15-0093-S001-P0567931) from the DOD Office of Naval Research’s National Oceanographic Partnership Program, Grants OCE1419559 and OCE1243015 from the National Science Foundation program Decadal and Regional Climate Prediction using Earth System Models, and Grant NNX16AP33G (U of Utah subaward 10042008) from NASA.