Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: Application to a subgrid-scale orography parametrization

TANDEO, P; PULIDO, M; LOTT, F

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

JOHN WILEY & SONS LTD

Lugar: Reading, UK; Año: 2015 vol. 141 p. 383 - 395

0035-9009

Recent work has shown that the parameters controlling parametrizations of the physicalprocesses in climate models can be estimated from observations using filtering techniques.In this article, we propose an offline parameter estimation approach, without estimatingthe state of the climate model. It is based on the Ensemble Kalman Filter (EnKF) and aniterative estimation of the error covariance matrices and of the background state using amaximum likelihood algorithm. The technique is implemented in a subgrid-scale orography(SSO) parametrization scheme which works in a single vertical column. First, the parameterestimation technique is evaluated using twin experiments. Then, the technique is usedwith synthetic observations to estimate how the parameters of the SSO scheme shouldchange when the resolution of the input orography dataset of a general circulation model isincreased. Our analysis reveals that, when the resolution of the orography dataset increases,the scheme should take into account the dynamical sheltering that can occur at low levelsbetween mountain peaks located within the same gridbox area.