Self-optimization of Model Parameters with the LETKF: a Real-world Application.

JUAN RUIZ; TAKEMASA MIYOSHI; MASARU KUNII

Kobe

Congreso; RIKEN-AICS Data assimilation workshop; 2013

AICS-RIKEN

The state augmentation approach to optimizing model parameters is explored using the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting (WRF) model. The parameter variables are augmented to the state vector; the LETKF accounts for ensemble-based correlations between the parameter variables and observed variables, and estimates optimal parameters that give better fit to observations. Usually parameters are not directly observed, so that the ensemble-based correlations play an essential role. We tested the parameter-estimation method in the real-world case of Typhoon Sinlaku (2008). The LETKF successfully found optimal parameter values of the sea-surface heat and moisture exchange coefficients and improved the forecast of atmospheric variables near the surface. The optimization of the surface fluxes of heat and moisture produce a significant reduction of the moisture and temperature bias near the surface.