PBL state estimation with surface observations, a column model, and an ensemble Kalman filter

Temporal propagation of skillful analyses is also assessed, separating the effects of good prior state estimates from the impact of assimilation at night when covariance is weak. Results show that accurate profiles of temperature, mixing ratio, and winds are estimated with the column model and ensemble filter assimilating only surface observations. Results are largely insensitive to choice of parameterization scheme and specified observation error variance.

The effects of using different parameterization schemes within the column model depend on whether assimilation is included, showing the importance of evaluating models within assimilation systems. At night, skillful estimates are possible because the influence of the observations from the previous day is temporally propagated, and atmospheric dynamics in the residual layer operate on slow time scales. It is expected that these profiles will have applications for nowcasting and secondary models (e.g., plume dispersion models) that rely on accurate specification of PBL structure.