Data assimilation research

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.

CO assimilation
Concentration of carbon monoxide (CO) in six-hour forecasts produced with the DART/WRF-Chem ensemble data assimilation system for a period during the FRAPPE field experiment. An experiment that assimilates only observations of meteorological variables (left) and one that also assimilates observations of CO from MOPITT (middle) are shown. The differences in concentrations (right) reflect an improved analysis of CO when the MOPITT observations are included and validate the performance of the system.

CISL’s data assimilation research advances CISL’s strategic imperative to produce scientific excellence. Specifically, this work leads the mathematics and geophysical communities in ways that accentuate the contributions of mathematical methods and models to scientific progress in the geosciences. Further, DAReS research advances CISL’s science frontier for understanding large and heterogeneous data sets by assimilating strategic, heterogeneous, and nonlinear observations into Earth System models.

There is increasing interest in applying data assimilation for the middle and upper atmosphere for space weather applications. DAReS scientists have been collaborating with several groups on data assimilation using the Whole Atmosphere Community Climate Model (WACCM) variant of the Community Atmosphere Model. Tuning of vertical localization of observation impact and adaptive inflation algorithms has been explored with DART. DAReS scientists have also recommended appropriate numerical damping in the WACCM model itself. WACCM forecasts of upper-air observations have improved as a result.

A new observation operator for space weather applications has been implemented in collaboration with Johns Hopkins Applied Physics Laboratory. Slant path observations of total electron content can now be assimilated in the TIEGCM and other space weather models like WACCM-X.

Prediction of aerosols in the troposphere is an important area of research for both climate science and air quality predictions. In collaboration with scientists at the Naval Research Laboratory and the University of California at Berkeley, DAReS scientists have been working to improve DART assimilation for aerosols. Improved localization for use with the Navy Aerosol Analysis and Prediction System (NAAPS) and CAM/Chem has helped improve aerosol predictions generated with both systems.

Work continues on improving atmospheric chemistry assimilations in both CAM-Chem and WRF-Chem. DAReS scientists have collaborated with ACOM scientists to improve localization, inflation, and the design of forward operators to assimilate satellite observations of atmospheric trace constituents. The improved systems have been used to generate extended reanalyses with CAM-Chem and are being used for reanalyses of the FRAPPE field experiment period with WRF-Chem.