Error Covariance Estimation and Representation for Mesoscale Data Assimilation
Abstract
The goal of this project is to explore and develop new methods of error covariance estimation and representation that can improve mesoscale data assimilation and numerical weather prediction. To this end, three research objectives were fulfilled: (i) A spline-spectral covariance model was developed to enhanced the capability of the innovation method for error covariance estimation. (ii) Non-isotropic error correlation functions were derived for radar radial-wind analysis and used to reformulate the innovation method. The reformulated method provided the first objective way to statistically estimate not only radar observation error variance but also observation error correlation between neighboring gates or beams of radar scans at very fine scales. (iii) By using the advanced functional approach and generalized Fourier transformation, the inverse of a covariance function was shown to be representable by a vector differential operator, called D-operator. With D-operator representations, the inverses error covariance matrices can be formulated directly and efficiently in the cost-functions of variational data assimilation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 12, 2005
- Accession Number
- ADA441445
Entities
People
- Qin Xu
Organizations
- University of Oklahoma