[17] Observational issues 2

[17-2] March 2, 11:20-11:40

Incorporating Correlated Observation Errors in Variational Data Assimilation

N. K. Nichols (Universtiy of Reading), J.M. Tabeart (Universtiy of Reading), S.L. Dance (Universtiy of Reading), A.S. Lawless (Universtiy of Reading), J.A Waller (Universtiy of Reading)


With the development of convection-permitting numerical weather prediction, the efficient use of high resolution observations in data assimilation is becoming increasingly important. Although the diagnosis of observation error statistics is difficult, idealized and operational studies have shown that a better treatment of observation error correlations gives improved forecast skill. Here we investigate the incorporation of correlated observation errors in a variational system and establish that the computational work needed to solve the assimilation problem increases as: the observations become more accurate; the observation spacing decreases; the prior (background) becomes less accurate; the prior error correlation length scales increase; and the observation error covariance matrix becomes ill-conditioned. In particular we show that the rate of convergence of the assimilation scheme depends on the minimum eigenvalue of the observation error correlation matrix. To reduce operational costs of the assimilation, we recondition the error correlation matrix by altering its eigenstructure. We implement the observation error correlations in a 1D-Var variational system used operationally at the Met Office for satellite retrievals. Experiments with IASI data demonstrate that incorporating the reconditioned observation error correlation matrices in the assimilation improves convergence and has an impact on humidity retrievals but has minimal effect on temperature retrievals.

  Presentation file: 17_2_N.K.Nichols.pdf