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Space and Time Multiscale Analysis System A sequential data assimilation approach

This study presents STMAS, a two-step multiscale analysis system designed for efficient data assimilation in observational networks. The first step retrieves resolvable observational information, while the second step reduces the data using standard statistical variational analysis. STMAS utilizes a localized error covariance approach through a banded matrix, which is optimized via a multigrid technique that controls base functions based on the number of grid points. The application of STMAS includes frontal boundary detection, showcasing its effectiveness against existing methods.

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Space and Time Multiscale Analysis System A sequential data assimilation approach

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  1. Space and Time Multiscale Analysis SystemA sequential data assimilation approach Yuanfu Xie, Steven Koch, Steve Albers Huiling Yuan and Brad Beechler Global Systems Division Earth System Research Laboratory

  2. Resolvable Information for a Given Observation Network Obse r vation Longer wave Obse r vation Longer wave B ac k g r ound B ac k g r ound Difference on longer wave Difference on shorter wave

  3. STMAS STMAS is implemented in two steps. • It retrieves the resolvable observation information. • After the resolvable information retrieved, STMAS is reduced to a standard statistical variational analysis With long waves retrieved, STMAS deals with a localized error covariance, a banded matrix.

  4. Multigrid Technique Using the number of gridpoints to control the base functions.

  5. An idealized multiscale case Left: Mesonet surface stations; Right: An analysis function

  6. An idealized multiscale case (cont.)

  7. A single 3DVAR with different α 0.9 0.7 α=0.5 These analyses tend to approximate the truth:

  8. Different Implementations of STMAS Recursive filter Wavelet Multigrid

  9. FAA/MIT Boundary Detection Application

  10. STMAS for frontal boundary detection

  11. STMAS vs. HPC

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