U21C-0620 High Resolution Recovery of Amazon Basin Water Storage Change Using Line-Of-Sight (LOS) Gravity Difference Data from GRACE Yiqun Chen1, Doug.

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Presentation transcript:

U21C-0620 High Resolution Recovery of Amazon Basin Water Storage Change Using Line-Of-Sight (LOS) Gravity Difference Data from GRACE Yiqun Chen1, Doug Alsdorf1, R.E. Beighley2, C.K. Shum1, Burkhard Schaffrin1 1School of Earth Sciences, The Ohio State University, Columbus, OH, USA 2Civil and Environmental Engineering, San Diego State University, CA, USA GRACE Data Processing Introduction 1. from in situ LOS gravity differences 2. from in situ geopotential differences GRACE Level 1B data have been analyzed and processed to recover continental water storage in a regional solution, by first estimating in situ Line-Of-Sight (LOS) gravity differences simultaneously with the relative position and velocity vectors of the twin GRACE satellites. This new approach has been validated using a simulation study over the Amazon basin (with three different regularization methods to stabilize the downward continuation solutions) and it is demonstrated that the method achieves an improved spatial resolution as compared to some of the other GRACE processing techniques, including global spherical harmonic solutions, and regional solution using in situ geopotential differences. 1a 2a c - an optimal regularization factor via formulas for the repro-BIQUUE of variance components d - iterative least-squares estimation with simultaneous updating of the a-priori covariance (using a difference covariance model) 1 - from in situ LOS gravity differences 2 - from in situ geopotential differences 1b 2b a - iterative least-squares estimation with simultaneous updating of the a-priori covariance b - Bayesian inference with variance components GRACE L2 global solutions) 1c 2c Simulation global solutions from in situ LOS gravity differences 1d 2d global solutions from in situ geopotential differences 1. from in situ LOS gravity difference d. applying iterative least-squares estimation with simultaneous updating of the a-priori covariance (using a different cov. model) from in situ geopotential difference 2a 1a c. applying an optimal regularization factor via formulas for the repro-BIQUUE of variance components TRUTH 2. from in situ geopotential difference from in situ LOS gravity difference degree variance a. applying iterative least-squares estimation with simultaneous updating of the a-priori covariance truth 1b 2b Conclusions and future work 1. Based on the global solutions from the closed-loop simulation, the LOS gravity approach (jumps at degree 40) performs better than the energy approach (jumps at degree 30). The regional solutions from the closed-loop simulation are close to each other, but the LOS gravity approach still performs a bit better than the energy approach at high degrees. 2. In the simulation, both (a) and (c) perform better than (b). 3. Conclusion 2 holds true for the real GRACE data processing and (a) is the most reliable approach. 4. There are actually three variance components involved in (a), from which two ratios of the variance components can be combined. With the two ratios, this approach is more flexible and more likely to approximate the reality. 5. Compare with mascon approach (Yuan and Watkins, 2006), or the modified Fredholm integral equation approach [Mayer-Gürr et al., 2006], or a reasonable regional hydrological model. 1c 2c The research is supported by grants from NSF's Collaboration in Mathematical Geociences Program (EAR0327633), NASA Earth Science programs (NNG04GN19G, NNG05GL26G, JPL 1265252).