On the Choice of Momentum Control Variables and Covariance Modeling for Mesoscale Data Assimilation
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

On the Choice of Momentum Control Variables and Covariance Modeling for Mesoscale Data Assimilation

  • 2019

  • Source: Journal of the Atmospheric Sciences, 76(1), 89-111
Filetype[PDF-784.32 KB]



Details:

  • Journal Title:
    Journal of the Atmospheric Sciences
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    For mesoscale variational data assimilation with high-resolution observations, there has been an issue concerning the choice of momentum control variables and related covariance modeling. This paper addresses the theoretical aspect of this issue. First, relationships between background error covariance functions for differently chosen momentum control variables are derived, and different choices of momentum control variables are proven to be theoretically equivalent in the sense that they lead to the same optimally analyzed incremental wind field in the limit of infinitely high spatial resolution provided their error covariance functions satisfy the derived relationships. It is then shown that when the velocity potential χ and streamfunction ψ are used as momentum control variables with their background error autocovariance functions modeled by single-Gaussian functions, the derived velocity autocovariance functions contain significant negative sidelobes. These negative sidelobes can represent background wind error structures associated with baroclinic waves on the synoptic scale but become unrepresentative on the mesoscale. To reduce or remove these negative sidelobes for mesoscale variational data assimilation, Gaussian functions are used with two types of modifications to model the velocity covariance functions in consistency with the assumed homogeneity and isotropy in variational data assimilation. In this case, the random (χ, ψ) background error fields have no classically valid homogeneous and isotropic covariance functions, but generalized (χ, ψ) covariance functions can be derived from the modified velocity covariance functions for choosing (χ, ψ) as momentum control variables. Mathematical properties of generalized covariance functions are explored with physical interpretations. Their important implications are discussed for mesoscale data assimilation.
  • Keywords:
  • Source:
    Journal of the Atmospheric Sciences, 76(1), 89-111
  • DOI:
  • Document Type:
  • Rights Information:
    Other
  • Compliance:
    Submitted
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.27.1