Estimating the surface layer refractive index structure constant over snow and sea ice using Monin-Obukhov similarity theory with a mesoscale atmospheric model

Since systematic direct measurements of refractive index structure constant (C-n(2)) for many climates and seasons are not available, an indirect approach is developed in which C-n(2) is estimated from the mesoscale atmospheric model outputs. In previous work, we have presented an approach that a state-of-the-art mesoscale atmospheric model called Weather Research and Forecasting (WRF) model coupled with Monin-Obukhov similarity (MOS) theory which can be used to estimate surface layer C-n(2) over the ocean. Here this paper is focused on surface layer C-n(2) over snow and sea ice, which is the extending of estimating surface layer C-n(2) utilizing WR model for ground-based optical application requirements. This powerful approach is validated against the corresponding 9-day C-n(2) data from a field campaign of the 30th Chinese National Antarctic Research Expedition (CHINARE). We employ several statistical operators to assess how this approach performs. Besides, we present an independent analysis of this approach performance using the contingency tables. Such a method permits us to provide supplementary key information with respect to statistical operators. These methods make our analysis more robust and permit us to confirm the excellent performances of this approach. The reasonably good agreement in trend and magnitude is found between estimated values and measurements overall, and the estimated C-n(2) values are even better than the ones obtained by this approach over the ocean surface layer. The encouraging performance of this approach has a concrete practical implementation of ground-based optical applications over snow and sea ice. (C) 2016 Optical Society of America