Tuesday, January 15, 2013

Spatial Variations of Supercell Environments 1980-2011

Over the past couple of weeks, I've thrown together a spatial environment climatology of supercell environments based on 0000 UTC North American Regional Reanalysis data.  A "supercell" environment is defined here as MLCAPE >= 1,500 J/kg, 0-6km BWD >= 35 kts., 0-3km SRH >= 150 J/kg, all in the presence of MLCIN >= -25 J/kg (a small capping inversion).  This equates to an old 'fixed' Supercell Composite Parameter (SCP) calculation of around two.  Remember that a majority of right-moving supercells are associated with SCP values greater than a value of one (Thompson et al. 2003).  Basically, if a storm is present in this environment, it has a statistically significant chance of being a supercell. 

The spatial anomaly plots shown in the link below are all plotting the frequency of the particular threshold discussed above.  Keep in mind that environments favorable for supercells can (and often do) arise outside of +/- 3 hours around 0000 UTC.  Thus, this environment climatology is biased towards diurnally driven convective environments.  Black 'freckles' on the anomaly maps indicate a statistically significant (α=.05) difference between that particular year and the 1980-2011 climatology.  Before you ask, yes, I have made similar images for individual months, but I have not made an online repository for them yet.  Please send any questions to my e-mail, or comment below.

Thursday, January 3, 2013

Discover Python!

A few weeks ago, I visited Northern Illinois University (NIU; my Alma mater) for Geography's annual career day.  It is a day focused on networking for students and alumni that helps build and maintain a strong foundation for all who graduate from the program.

When the chance came to give advice to meteorology students (no, not the advice to ignore Gilbert!), I promptly responded with Geographic Information Systems (GIS) and Python.  There is an ArcPY module for Python, and many tools in ArcMap run via Python scripts.  In my opinion, Python is *the* answer for budding scientists.  Its ease of use, open source, and large support community nature make it an obvious choice.  However, don't ask just me!  The latest issue of the Bulletin of the American Meteorological Society (BAMS) highlights the utility of Python for use in atmospheric science.  Python can easily deal with netCDF arrays (numPy module) or GRIB data (Nio module), both of which are commonplace data formats in meteorology.

Patrick Marsh (OU SoM) does a lot of amazing things in Python, and it is easy to see how useful it can be for research purposes.

For example, a Python script of mine was able to deal with all 1980-2011 NARR data and produce this map in about 3 hours!

It's also great for plotting any sort of GRIB message:

Download Python today and get started!  Some of my most used add-on modules are:

ArcPy (good BLOG here)