Developing a Comprehensive Land Data Assimilation System Using NCAR's Community Land Model (CLM) and Data Assimilation Research Testbed

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 4 February 2014: 9:30 AM
Room C209 (The Georgia World Congress Center )
Zong-Liang Yang, University of Texas at Austin, Austin, TX; and Y. Zhang, T. Hoar, M. Rodell, and J. Anderson

Land plays an important role in shaping regional and global climate and the water cycle. However, many of these processes are not well understood, which is largely due to the lacking of high quality datasets. Recently, my group has been collaborating with Jeff Anderson and Tim Hoar of NCAR and Matt Rodell of NASA by developing a global-scale multi-sensor snow data assimilation system based on NCAR's Data Assimilation Research Testbed (DART) coupled to the Community Land Model version 4 (CLM4); CLM4 can be replaced by CLM4.5 as the latter was released in 2013. This data assimilation system can be applied to land areas to take advantage of high-resolution regional-specific observations. The DART data assimilation system has an unprecedented large ensemble (80-member) atmospheric forcing (temperature, precipitation, winds, humidity, radiation) with a quality of typical reanalysis products, which not only facilitates ensemble land data assimilation, but also allows a comprehensive study of many feedback processes (e.g. the snow albedo feedback). DART has long been linked with ocean and atmospheric models as well as the WRF model, but the CLM4 and DART have just been linked (mostly by my student Yongfei Zhang), and initial results were reported in the past AGU and AMS meetings. Besides our prototype snow data assimilation, the coupled CLM4/DART framework should also be useful for data assimilation involving other variables, such as soil moisture, skin temperature, and leaf area index from various satellite sources and ground observations. Such a truly multi-mission, multi-platform, multi-sensor, and multi-scale data assimilation system with DART will, ultimately, help constrain earth system models using all kinds of observations to improve their prediction skills from intraseasonal to interannual. For an offline application, however, we are interested in using this framework to produce mutually consistent state and flux variables for energy, carbon, and water balances as well as to use these products to study extreme events such as drought and flooding.