Comparison of retrieved noctilucent cloud particle properties from Odin tomography scans and model simulations
- 1Department of Meteorology, Stockholm University, Stockholm, Sweden
- 2CRESS, York University, Toronto, Canada
Abstract. Mesospheric ice particles, known as noctilucent clouds or polar mesospheric clouds, have long been observed by rocket instruments, satellites and ground-based remote sensing, while models have been used to simulate ice particle growth and cloud properties. However, the fact that different measurement techniques are sensitive to different parts of the ice particle distribution makes it difficult to compare retrieved parameters such as ice particle radius or ice concentration from different experiments. In this work we investigate the accuracy of satellite retrieval based on scattered light and how this affects derived cloud properties. We apply the retrieval algorithm on spectral signals calculated from modelled cloud distributions and compare the results to the properties of the original distributions. We find that ice mass density is accurately retrieved whereas mean radius is often overestimated and high ice concentrations are generally underestimated. The reason is partly that measurements based on scattered light are insensitive to the smaller particles and partly that the retrieval algorithm assumes a Gaussian size distribution. Once we know the limits of the satellite retrieval we proceed to compare the properties retrieved from the modelled cloud distributions to those observed by the Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS) instrument on the Odin satellite. We find that a model with a stationary atmosphere, as given by average atmospheric conditions, does not yield cloud properties that are in agreement with the observations, whereas a model with realistic temperature and vertical wind variations does. This indicates that average atmospheric conditions are insufficient to understand the process of noctilucent cloud growth and that a realistic atmospheric variability is crucial for cloud formation and growth. Further, the agreement between results from the model, when set up with a realistically variable atmosphere, and the observations suggests that our understanding of the growth process itself is reasonable.