A Comparison of Measured and Predicted Cloud Condensation Nuclei (CCN) Concentrations

I presented my research on Tuesday at the University of North Dakota 2010 Scholarly Forum. Although a lot has changed from the original abstract I still provide it here for the curious. (Follow the links to access the PDF slides and the code and data.) Once I achieve the closure on the lab data I should be able to easily extend the similar methodology onto our field data.


Abstract:

    Research on CCN helps us to understand cloud and precipitation formation in a local scale and resolving the indirect aerosol effects on a global perspective. Following the conclusions of similar studies which emphasize the particle size distribution's being the biggest impactor on cloud nucleation ability of aerosols, and accounting for the chemical composition with the hygroscopicity parameter in κ-Köhler theory, a CCN closure study was performed. Measurements of the aerosol size distributions in the range of 0.1 to 3 μm is made by using a Passive Cavity Aerosol Spectrometer Probe (PCASP-100X), and the missing 0.01 to 0.1 μm range data is extrapolated with a spline interpolation technique. A two-parameter log-normal distribution is also estimated by fitting on the aerosol measurements including total aerosol particle concentrations measured by a Condensation Particle Counter (CPC - TSI-3771). Determination of the critical activation diameters is done by utilizing the κ-Köhler theory as proposed by Petters and Kreidenweis (2007). The closure resulted with the best R²=0.33 between the predicted and measured CCN concentrations at the 0.2% supersaturation (SS). Additional to this poor agreement, the concentrations obtained from the calculations under-predict the CCN concentrations over the full range of supersaturation measurements. The ground and airborne data from the spring 2009 Saudi Arabia field campaign are currently being analyzed.

PCASP aerosol number-size distribution

Following the similar approach given in Chapter 8 "Properties of the Atmospheric Aerosol" [Seinfeld, J. H., and S. N. Pandis, 2006: Properties of Atmospheric Aerosol, in: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. 2nd ed. Wiley-Interscience] one can construct PCASP aerosol number-size distributions as shown in the figure.

 

The code used for this demonstration is given at pcasp_aerosol.py

Record number of equations

Chapter 7 "Collection Growth" of "Cloud and Precipitation Microphysics - Principles and Parameterizations" (Jerry M. Straka, 2009, Cambridge University Press) has 278 numbered equations. Other chapters follow:

238 in Chapter 2 "Foundations of microphysical parameterizations"

206 in Chapter 5 "Vapor diffusion growth of liquid-water drops"

These are the highest number of equations I have seen so far listed in a textbook.

Bubbles in a bottle

 

If Mayavi can do it, so does nature...

Weird clouds on Wired magazine

Some interesting looking clouds are shown on Wired with brief explanations about their formations.

I spotted Mammatus clouds and Kelvin-Helmholz waves after I have started looking up into the sky with an increased curiosity. I posted a few of them on my Picasa...

Py4Science: a Starter Kit

Py4Science: a Starter Kit

This is a great collection of resources from Fernando Pérez (research scientist at the Helen Wills Neuroscience Institute at U.C. Berkeley) for those who want to try Python for Scientific Computing.

Random particles in a cube

 

Gamma-size distributed randomly placed particles using MayaVi.