Albedo-Ice regression method for determining ice water content of Polar Mesospheric Clouds using ultraviolet observations from space
Abstract. High spatial resolution images of polar mesospheric clouds (PMCs)
from a camera array on board the Aeronomy of Ice in the Mesosphere (AIM) satellite
have been obtained since 2007. The Cloud Imaging and Particle Size
Experiment (CIPS) detects scattered ultraviolet (UV) radiance at a variety
of scattering angles, allowing the scattering phase function to be measured
for every image pixel. With well-established scattering theory, the mean
particle size and ice water content (IWC) are derived. In the nominal mode
of operation, approximately seven scattering angles are measured per cloud
pixel. However, because of a change in the orbital geometry in 2016, a new
mode of operation was implemented such that one scattering
angle, or at most two, per pixel are now available. Thus particle size and IWC can no longer
be derived from the standard CIPS algorithm. The Albedo-Ice Regression (AIR)
method was devised to overcome this obstacle. Using data from both a
microphysical model and from CIPS in its normal mode, we show that the AIR
method provides sufficiently accurate average IWC so that PMC IWC can be
retrieved from CIPS data into the future, even when albedo is not measured
at multiple scattering angles. We also show from the model that 265  nm UV
scattering is sensitive only to ice particle sizes greater than about 20–25  nm 
in (effective) radius and that the operational CIPS algorithm has an
average error in retrieving IWC of - 13 ± 17  %.