Ian S. Adams
Ian S. Adams
The microphysics of ice clouds and falling snow plays a key role in weather and climate. For example, the density of snow affects the depth and mass of accumulations. The climatological impacts of clouds are directly connected to the types, sizes, and orientations of constituent ice and snow particles. Supercooled liquid water, i.e., water that exists in the liquid state below freezing, present in snow-generating clouds alters the balance of water available for evaporation/sublimation and condensation/deposition, thus affecting crystal growth. Additionally, the liquid present in mixed-phase clouds accretes on frozen hydrometeors through riming, adding to the density of cloud ice and falling snow. Thus, snow generation processes such as depositional growth, aggregation, and riming — along with local dynamical and thermodynamical conditions — affect snow particle shape, density, and degree of alignment.
Polarimetric active and passive millimeter wave observations of ice clouds and falling snow are dependent on particle microphysics such as ice and snow particle density and orientation. The intensity and polarization state of remote sensing observables (brightness temperature and polarization difference in passive observations; reflectivity, linear depolarization ratio, and differential reflectivity in radar observations) offer information on these microphysical parameters. Just as the presence of supercooled liquid affects the microphysics, it also modifies the millimeter-wave measurements, primarily through frequency-dependent attenuation (for active and passive sensors) and emission (for passive sensors).
The challenge, then, is to reconcile the radar and radiometer measurements and the properties of interest of falling snow and clouds. By applying scattering models to complex snow particle shapes, we are able to estimate the electromagnetic properties of snowflakes over a range of microwave and millimeter wave frequencies [link to Kuo database]. These electromagnetic scattering parameters are then ingested into a vector (i.e., polarized) radiative transfer model to understand the effects of particle microphysics on remote sensing observables. By performing radiative transfer calculations for various populations of snow particles and a range of appropriate atmospheric conditions, we are able to discern how particle microphysics impacts observations by matching simulations to measurements. When in situ data are available, estimates of microphysical properties can be verified [link to field campaigns] by comparing particle observations with populations properties inferred from remote sensing data.
Munchak, S. J., and I. Adams (2017) “Optimal estimation retrievals of precipitation with active and passive measurements (using ARTS as a forward model),” 3rd Open ARTS Community Workshop, Kristineberg, 6-8 September.
Adams, I. S., and M. H. Bettenhausen (2016) “Brightness Temperature Simulation of Observed Precipitation Using a Three-Dimensional Radiative Transfer Model,” Journal of Atmospheric and Oceanic Technology, 33 (10): 2053-2064 [10.1175/jtech-d-15-0241.1]
Munchak, S. J., I. Adams, and B. Johnson (2016) “An Investigation of Precipitation-Induced Polarization at 166 GHz Observed by GMI,” 8th IPWG and 5th IWSSM Joint Workshop, Bologna, 3-7 October.
Adams, I. S., and M. H. Bettenhausen (2012) “The scattering properties of horizontally aligned snow crystals and crystal approximations at millimeter wavelengths,” Radio Science, 47 (5): [10.1029/2012rs005015]
Adams, I. S., P. Gaiser, and W. L. Jones (2008) “Simulation of the Stokes vector in inhomogeneous precipitation,” Radio Science, 43 (5): [10.1029/2007rs003744]