Nonlinear data assimilation for clouds and precipitation using a gamma inverse-gamma ensemble filter

Abstract

Where clouds occur, their water content is always positive definite, and may be near zero. In addition, it is common for errors in remote-sensing observations of clouds and rainfall to be represented as a fraction of the measurement. Furthermore, there is nonlinearity in the relationships among cloud environment, cloud microphysical processes, and the amount and distribution of cloud and precipitation. For these reasons, data assimilation algorithms that rely on linearity and assumptions of Gaussian probability distributions may have difficulty in assimilating observations in cloudy regions, as well as producing an analysis that realistically represents the actual distribution of clouds and ..