Size – and Shape – Does Matter – June 19, 2019
Today we’re hosting Dr. Carl Schmitt, a scientist from the National Center for Atmospheric Research (NCAR) in Boulder, CO, who specializes in cloud physics.
“Size does matter, and so does shape when it comes to particles in clouds. Being surface dwellers, people often experience rain and fog, both of which are made of liquid water drops. With few exceptions, these water drops are spherical. By contrast, ice particles are not spherical. Anyone who has looked at a snowflake knows this and also knows that there is an extreme variety in shapes of snow particles.
Every weather forecasting and climate model needs to predict cloud properties in order to have a chance at being accurate. The most important property of cloud particles is the speed at which they fall. Air resistance affects different particles differently. For water drop clouds, this is easy because the droplets are spherical. We can calculate the weight and the area, the two main ingredients for calculating fall speed. With that information we determine a relationship between the fall speed and the size of the droplet.
The variability in ice particle shape causes problems when trying to do the same for ice particles though. A lot of research effort has gone into measuring these properties though and typically, average values are used for sizes. So, for a specific particle size, the average properties of observed particles are used.
I wanted to know how much variability there was though and if this could significantly affect model results. In the first part of this study (published yesterday), my collaborators and I used methods of fractal geometry to estimate the variability in fall speed for particles with different shapes but that were the same size. We analyzed measurement data from ice clouds from Alaska to southern Australia. Consistently, we found that the variability in fall speed was around 18% around the mean value. The second part of this study, how model results change, is in its early stages. Preliminary results suggest that incorporating this variability in the models could result in substantial redistribution of snowfall. Some areas received up to 20% more or less snow when compared to model runs that did not incorporate the variability.
**The work is still in the preliminary phases, but snowfall forecasts could substantially improve in the future due to incorporating these ideas into forecast models.**”
For further technical reading: https://journals.ametsoc.org/d…/pdf/10.1175/JAMC-D-18-0291.1