Towards a multivariate tropical data assimilation based on equatorial waves
Nedjeljka Zagar, NCAR/ASP
Atmospheric analysis fields, used to initialize Numerical Weather Prediction (NWP) models, are obtained by combining available observations and a priori (background) information. Analysis procedures are dominated by the background information coming from NWP models. Thus a reliable estimate of the background-field errors is an important input to data assimilation. This issue is especially challenging in the tropics due to the lack of wind observations and complex dynamics.
Most NWP assimilation schemes are effectively univariate near the equator. In this seminar I would present a multivariate formulation of the variational data assimilation in the tropics. The modelling framework is idealized with respect to NWP; the model is based on nonlinear shallow-water equations solved in spectral space. The proposed background-error model supports the mass-wind coupling based on eigenmodes derived from linear equatorial wave theory. Background-error covariances are derived from the tropical forecast errors of the ECMWF model. The resulting assimilation model produces "balanced" analysis increments and hereby should increase the efficiency of all types of observations. A comparison of the error statistics in two phases of the quasi-biennial oscillation (QBO) shows the impact of QBO on the background-error covariances in the tropical stratosphere.
Examples of tropical assimilation experiments will be presented from a study of the potential impact of space-borne line-of-sight wind measurements to be provided by the Atmospheric Dynamic Mission (ADM-Aeolus).
SECURITY: If you are coming from outside the NOAA campus, please be advised that you will need an on-site sponsor. Please contact that person in advance of the seminar to be put on the list and allow 10 minutes extra on the day of the seminar. Please contact Joe Barsugli (303-497-6042) or Lucia Harrop (303-497-6188) at least a day before the seminar if you have any questions.