My research program utilizes several data and computing intensive tools to explore the interaction between clouds and the Earth’s climate system. These include: simulation of atmospheric processes over 1000’s of km length scales at sub-1 km horizontal grid spacing, multivariate and multisensor remote sensing measurements, and nonlinear ensemble-based data assimilation methods. Each of these requires the effective use of large-capacity computational resources. Examination of the interaction between small scale cloud processes (on the order of 10 km) and the larger scale climate system dynamics (on the order of 1000s of km) requires model domains that can simultaneously resolve planetary and cloud scale processes. Assimilation of observations for highly nonlinear processes require not just a single model integration, but large ensembles of simulations. The effective exploration of the information content of observations in systems with large numbers of degrees of freedom (e.g., retrieval of three dimensional volumes of cloud and precipitation microphysical properties), involves exploration of a multidimensional probability space. Such an exercise typically requires a very large (on the order of 10^7) number of integrations of forward algorithms that map sets of atmospheric properties to simulated satellite or ground-based radiance measurements.
Data science applications: Interaction between precipitating cloud systems and a changing climate, optimal observation of cloud properties from space, model uncertainty quantification