Data Assimilation for Clouds and Precipitation Using Non-Linear Ensemble Algorithms

Water content in clouds is always positive definite, may be near zero, and is commonly nonlinearly related to the cloud environment and cloud microphysical processes. This nonlinearity extends to the amount and intensity of precipitation. As such, data assimilation algorithms that are based on linearity and/or Gaussian probability distributions may have trouble producing an analysis that realistically represents the actual distribution of clouds and precipitation. In this presentation, we show results from data assimilation experiments using a Markov chain Monte Carlo (MCMC) algorithm. We demonstrate the various types of nonlinearity present in cloud microphysical parameterizations, in particular. We then show results from various ensemble filter algorithms, including the Ensemble Transform Kalman Filter (ETKF), a newly developed filter based on Gamma and Inverse Gamma distributions (the Gamma - Inverse Gamma (GIG) filter), and a particle filter (PF).