Cloud-resolving satellite data assimilation: Information content of IR window observations and uncertainties in estimation

This study addresses the problem of four-dimensional (4D) estimation of a cloudy atmosphere on cloud-resolving scales using satellite remote sensing measurements. The motivation is to develop a methodology for accurate estimation of cloud properties and the associated atmospheric environment on small spatial scales but over large regions to aid in better understanding of the clouds and their role in the atmospheric system. The problem is initially approached by the study of the assimilation of the Geostationary Operational Environmental Satellite (GOES) imager observations into a cloud-resolving model with explicit bulk cloud microphysical parameterization. A new 4D variational data assimilation (4DVAR) research system with the cloud-resolving capability is applied to a case of a multilayered cloud evolution without convection. In the experiments the information content of the IR window channels is addressed as well as the sensitivity of estimation to lateral boundary condition errors, model first guess, decorrelation length in the background statistical. error model, and the use of a generic linear model error. The assimilation results are compared with independent observations from the Atmospheric Radiation Measurement (ARM) central facility archive. The modeled 3D spatial distribution and short-term evolution of the ice cloud mass is significantly improved by the assimilation of IR window channels when the model already contains conditions for the ice cloud formation. The assimilated ice cloud in this case is in good agreement with the independent cloud radar measurements. The simulation of liquid clouds below thick ice clouds is not influenced by the IR window observations. The assimilation results clearly demonstrate that increasing the observational constraint from individual to combined channel measurements and from less to more frequent observation times systematically improves the assimilation results. The experiments with the model error indicate that the current specification of this error in the form of a generic linear forcing, which was adopted from other data assimilation studies, is not suitable for the cloud-resolving data assimilation. Instead, a parameter estimation approach may need to be explored in the future. The experiments with varying decorrelation lengths Suggest the need to use the model horizontal grid spacing that is several times smaller than the GOES imager native resolution to achieve equivalent spatial variability in the assimilation.