Characterizing the Effects of Ice Microphysical Assumptions on Retrieval Algorithm Uncertainties

One of the principal objectives of NASA satellite missions has been to characterize the physical properties of clouds and precipitation. This objective has been in response to well-known biases that predictive models of the Earth's climate system continue to demonstrate in key hydrological processes such as precipitation and radiative forcing. Deriving cloud and precipitation properties from remote sensing measurements in ice-phase regions is an inherently ill-posed problem. Assumptions are unavoidable that result in substantial and difficult-to-quantify uncertainties in retrieval algorithm results.

With these issues in mind, we are analyzing in situ and remote sensing data sets that have been collected in recent years by NASA and NSF suborbital campaigns. A unique aspect of the campaigns we are focusing on is that they included accurate observations of the ice crystal particle size distribution (PSD) and independent measurements of bulk quantities such as condensed ice mass and radar reflectivity. Often these measurements were collected in concert with other co-located remote sensing observations. Our objectives are to use these in situ measurements to characterize the uncertainties in single scattering properties of ice crystal populations due to the assumptions that are typically necessary in developing and implementing cloud and precipitation property retrieval algorithms. In particular, we will demonstrate the degree to which these assumptions influence forward model errors in radar reflectivity and demonstrate how these errors influence retrieved quantities.