The potential benefits of handling mixture statistics via a bi-Gaussian
EnKF: tests with all-sky satellite infrared radiances
Abstract
The meteorological characteristics of cloudy atmospheric columns can be
very different from their clear counterparts. Thus, when a forecast
ensemble is uncertain about the presence/absence of clouds at a specific
atmospheric column (i.e., some members are clear while others are
cloudy), that column’s ensemble statistics will contain a mixture of
clear and cloudy statistics. Such mixtures are inconsistent with the
ensemble data assimilation algorithms currently used in numerical
weather prediction. Hence, ensemble data assimilation algorithms that
can handle such mixtures can potentially outperform currently used
algorithms.
In this study, we demonstrate the potential
benefits of addressing such mixtures through a bi-Gaussian extension of
the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the
commonly used ensemble Kalman filter (EnKF) using perfect model
observing system simulated experiments (OSSEs) with a realistic weather
model (the Weather Research and Forecast model). Synthetic all-sky
infrared radiance observations are assimilated in this study. In these
OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind
components, temperature, specific humidity, and simulated upper
tropospheric water vapor channel infrared brightness
temperatures.
This study is one of the first to
demonstrate the potential of a Gaussian mixture model EnKF with a
realistic weather model. Our results thus motivate future research
towards improving numerical Earth system predictions though explicitly
handling mixture statistics.