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Efficient Multiscale Grid Analysis for Space-Time Variational Data Assimilation System (STMAS)

STMAS is a 3DVAR system combining spatial and temporal observations to provide consistent grid analysis. It is evolving into 4DSTMAS, a new assimilation technique for accurate surface analysis. Utilizing recursive filters and multigrid methods, STMAS offers efficient multiscale analysis with wavelength control, ideal for data assimilation using different data sources and imposing balancing constraints.

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Efficient Multiscale Grid Analysis for Space-Time Variational Data Assimilation System (STMAS)

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  1. Space-Time Mesoscale Analysis SystemA sequential 3DVAR approach Yuanfu Xie, Steve Koch John McGinley and Steve Albers Global Systems Division Earth System Research Laboratory

  2. Space and time variational data assimilation system STMAS is a three-dimensional variational analysis, combining horizontal spatial and temporal observation to provide a temporally consistent surface grid analysis. It will be developed into a full three-dimensional spatial and temporal analysis (STMAS 4D) and will be a new data assimilation technique.

  3. Resolvable Information for a Given Observation Network

  4. Single solution of a 3DVAR • It builds the grid value correlation by statistical estimation; • It may not resolve the frontal or boundary correctly that that is critical for the automatic front detection algorithm.

  5. Surface Analysis: Ideal case Left: Mesonet surface stations; Right: An analysis function

  6. Analytic function:A multi-scale frontal propagation testing function

  7. Single 3DVAR approach • A recursive filter is usually used to approximate the error covariance; • After selecting the parameter of recursive filter , it solves one variational problem:

  8. A single 3DVAR with different RF  0.9 0.5 0.7 These analyses tend to approximate the truth:

  9. Recursive filter version of STMAS A sequential 3DVAR implemented through a recursive filter. • Solve the 3DVAR with large , e.g. 0.999; • Subtract the analysis from observation values used in previous 3DVAR analysis; • Reduce  by a fraction, say  in (0.5,1); • Return to step 1 if it is necessary; • Add the previous analyses together.

  10. Comparison: Single 3DVAR With =0.5 or 0.9 Truth STMAS-RF

  11. Multigrid application in STMAS Replacing the recursive filter in an early version of STMAS, a multigrid technique is implemented to obtain a multiscale analysis. The number of grid points over a given domain determines the shortest wavelength allowed. A multigrid uses the number of grid points to control the wavelength. STMAS solves its variational problem over the coarsest grid and obtains observation information for longest waves. By gradually increasing the number of grid points, STMAS multigrid gains shorter waves by each iterations.

  12. An efficient analysis system • Since the multigrid determines the wavelength, there is no correlation involved in STMAS variational analysis over a given grid. • Only computation for the cost function is simple interpolations. • A STMAS 5 km surface analysis of 6 state variables • over eastern US (two-third of CONUS) using recursive filter spends 15 minutes; A multigrid STMAS analysis could take about 40 seconds.

  13. Different Implementation of STMAS Recursive filter Wavelet Multigrid

  14. STMAS-MG • STMAS-MG (multigrid) is an efficient, multiscale grid analysis system; • It can use all possible data sources, including radar, satellite and so on; • It can also impose balances or constraints to its analysis directly; • STMAS 4D is under development.

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