Advance the application of ensemble data assimilation

Data assimilation
Total electron content at 75W at 1800 local time as a function of day of year in 2009 for a forecast initialized from a DART/WACCMX ensemble reanalysis for 15 January (top), the DART/WACCMX reanalysis (middle panel), and independent observations that were not used in the reanalysis (lower panel). This period includes a sudden stratospheric warming (indicated by the dashed line) and the results demonstrate that the DART/WACCMX forecast and reanalysis capture important aspects of the observed total electron content as the warming progresses.

Data assimilation is providing rapid advances in geophysical studies. The Data Assimilation Research Section (DAReS) of IMAGe develops and maintains the Data Assimilation Research Testbed (DART), a software facility for ensemble data assimilation. DAReS also provides support to a growing community of NCAR, university, and government laboratory partners who apply ensemble data assimilation methods with DART. With DART, modelers and experimenters can use state-of-the-art DA algorithms to build forecasting systems and diagnose both models and observation systems with a minimum investment in software and algorithm development. DA experts can use DART as a framework for building and testing new algorithms with both simple and sophisticated models. Finally, DART fosters a community of users, supported by the DART development team, who collaborate scientifically and contribute important new capabilities to the facility.

Data assimilation is a key tool for Earth System science that allows models to be confronted with observations. DA is essential for making forecasts for all components of the Earth System at all space and time scales. DA also provides a tool for identifying deficiencies in models and observing systems. The ensemble DA tools provided by DART allow uncertainty quantification which is essential to many prediction and scientific goals. Facilitating the use of DART by scientists helps to achieve CISL’s strategic goal to “Advance Earth System science by expanding the productivity of researchers through high-performance computing and data services.”

Work continues on improving interfaces between DART and models including further development for WACCM-X in collaboration with HAO and a merging of two independent interfaces to the ROMS ocean model into a single more powerful interface. An enhanced interface to the Navy COAMPS atmosphere model is also nearly completed. Applying ensemble data assimilation simultaneously for both the troposphere and also the upper atmosphere in the WACCM-X model revealed a number of challenges. The physical time scales are very different in these regions, and the frequency of observations and appropriate assimilation intervals may be inconsistent. Improvements to the model made by HAO scientists coupled with better tuning of the ensemble assimilation is leading to significantly improved analyses. In collaboration with UC Berkeley, a capability to estimate surface sources of CO2 using assimilation of meteorological and remotely sensed column CO2 is being refined. In collaboration with CGD, appropriate localization for ensemble DA in coupled ocean atmosphere models has been studied and should lead to improved analyses for coupled CESM predictions.

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.