Lesson 2. Data, Data Everywhere

This is a sample lesson page from the course METEO 101 which is part of the online Certificate of Achievement in Weather Forecasting offered by the Penn State Department of Meteorology. Any questions about this program can be directed to: David Babb

Motivate...

Looking down the path in a corn maze.
Getting lost in a corn maze is an annual fall tradition in many parts of the country.  Meteorologists often feel like they to are lost in a maze of observational data.Credit: Kruger's Farm Corn Maze / Art Institute of Portland / CC BY 2.0
After reading through the last lesson, you probably are getting the idea that the first step to forecasting is knowing the current state of the atmosphere.  Meteorologists are able to come to this understanding through observational data -- truck-loads of data!  Think about it.  We are observing as many as 10 variables, at hundreds of sites, every hour!  (and that's not even including other types of observations like satellite images, radar images, etc.) The problem with so much information is that it's hard to make sense out of it all. 

Consider the following example. Have you ever been in a corn maze?  If not, it's exactly what is sounds like... a maze cut into a cornfield.  The problem of course, is that while navigating the maze, all you see are corn stalks (see right).  What you can't see however, is the pattern created by the whole maze.  Now imagine that you had some friends floating above you in a hot air balloon.  Their view of the corn maze from above would be very different (and much more informative) than yours.  In fact, from their unique perspective, they can see the pattern of the maze and ultimately direct you to the finish line.

Our data problem is much like being in a maze.  There is so much raw data that it hinders us from seeing the "big picture".  What we need are some strategies that can present the data we've collected in a better way -- a way that allows patterns to emerge (an "overhead view" if you will).  That's what this lesson's all about.  The primary means of visualizing data in a spatial context is contouring.  We will examine how contour maps are drawn and how to interpret them.  We'll also take a look at how data is observed over the oceans and how map projections play a role in our analysis approach.  Finally, we are going to take a look at meteograms, which are time-plots for the data at a single station.

So, if you're ready let's dive in!