Global Validation of EOS-AQUA Land Surface Dynamics using Data Assimilation

PI: Paul Houser
Institution: NASA-Goddard Space Flight Center
Mail Code 974
Greenbelt, MD 20771
Phone: (301) 614-5772
FAX: (301) 614-5808
Email: Paul.Houser@gsfc.nasa.gov

Co-investigators:
- Jared K. Entin, NASA-Goddard Space Flight Center, Mail Code 974
- James Foster, NASA-Goddard Space Flight Center, Mail Code 974
- Peggy E. O'Neill, NASA-Goddard Space Flight Center, Mail Code 974
- Jeffrey Walker, USRA/NASA-Goddard Space Flight Center, Mail Code 974
- Xiwu Zhan, UMBC-GEST/NASA-Goddard Space Flight Center, Mail Code 974

EOS Team: AMSR-E

NASA EOS-PSO funding through FY02: $100,000

ABSTRACT

Provision of high quality remotely-sensed global land surface measurements is a key element of NASA's Earth Science Enterprise Program. However, it is recognized that these data fields will contain uncertainties due to imperfect instrument calibration and inversion algorithms, geophysical noise, representativeness error, communication breakdowns, and other sources. It is therefore essential that the accuracy and credibility of these remotely-sensed fields be evaluated for their use in critical research and applications.

Data assimilation systems have been used extensively in meteorology to expose significant defects in satellite data processing schemes, technology limits, bias, and noise. Modern data assimilation techniques use relevant prior data and a state-of-the-art computer model to estimate the state of the land surface. For each observation, a background value is derived from the model forecast for comparison. Systematic differences between observations and model predictions can identify systematic error, or identify uncharacteristically large differences in observations. Thus the consistency of the model provides guidance to identify observation problems in a data assimilation context.

We propose to determine the nature and variability of uncertainties in selected global soil moisture and snow products, measured by the EOS-AQUA AMSR-E sensor on a variety of time scales, and to analyze the effects of these uncertainties on the predictability of the global surface water and energy balance using land surface data assimilation techniques in near real-time. Specifically, we will use innovative land surface data assimilation techniques to check the quality control, physical consistency, and systematic realism of global EOS-AQUA land observations, in the context of the collaborative, real-time Land Data Assimilation System (LDAS) project. The goal will be to develop a nearly real-time operational land data assimilation system that will monitor the spatial-temporal AMSR soil moisture and snow observation quality, so as to provide feedback to mission operators of observational problems. This system will also extend AMSR-E products in time and space to produce consistent data assimilation land surface fields that will be valuable for use in subsequent analysis and application. Finally, selected in situ and airborne land surface observations will be used as a secondary test of data integrity. The proposed innovative validation strategies will feed back to improve the calibration and accuracy of EOS land-surface observations. These improvements will lead to enhanced characterization of the spatial and temporal dynamics of uncertainty in these critical land surface quantities and will benefit climate and weather prediction efforts.