Refractive index confidence explorer (RICE): A tool for propagating uncertainties through complex refractive index retrievals from aerosol particles

Accurate and precise retrievals of aerosol complex refractive indices (m) are essential to constraining the direct radiative effect of atmospheric aerosols. Despite this, there is no generally accepted method for constraining the uncertainty in full-distribution aerosol complex refractive index retrievals. This is in part due to condition-dependent and solution-dependent uncertainties which propagate through retrievals. Here, the Refractive Index Confidence Explorer (RICE), a program written in WaveMetrics Igor Pro, is presented. RICE applies a Monte Carlo-like method to propagate uncertainties through a full size distribution inverse Mie method (FD-IMM) for m retrievals. The m retrieval and RICE uncertainty analysis use absorption coefficients, scattering coefficients, aerosol size distributions, and measurement uncertainties as inputs. RICE iteratively tests a series of m values for their ability to produce the retrieved m under perturbed conditions. Perturbations account for uncertainties in optical, particle size, and particle number concentration measurements. RICE then uses these data to calculate semi-empirical probability distributions which are used to provide confidence intervals for the real (n) and imaginary (k) components of m. RICE provides measurement by measurement uncertainty estimations enabling estimation of uncertainty even when conditions are highly dynamic, like those associated with field measurements. When RICE is applied to idealized test cases and external data, uncertainty is shown to be dynamic in relation to the value of the retrieved m (solution) and the nature of the particle size distribution (measurement condition). Within these cases, m uncertainties were shown to be large for the upper end of n and k values explored here (i.e., n = 1.8 and k = 0.5, at 375 nm) under uncertainty conditions typical of modern particle and optical measurement technologies, suggesting FD-IMM’s usefulness may be limited by instrumental uncertainties under some measurement conditions. However, FD-IMM retrievals may still provide reasonable estimates of m when n <∼1.6 and k < 0.1.