So, I wrote this nifty Python script to help us download them.
import urllib month = 1 year = 2000 while year < 2013: while month < 13: urllib.urlretrieve("http://www.wunderground.com/history/airport/KNYC/" + str(year) + "/" + str(month) + "/1/MonthlyHistory.html?req_city=NA&req_state=NA&req_statename=NA&format=1", "csvfiles/" + str(year) + "-" + str(month) + ".csv") month = month + 1 month = 1 year = year + 1
That should download data back to 2000. I checked on their data for 1990 and they didn't seem to have it. Furthermore, I'm not entirely sure of the accuracy of this data.
This concludes Part 1. Yes, no analysis yet; but, half the fun of analyzing data is getting your hands on it in a format that you can process. Feel free to upload this data into a SQL database. I'll provide some data on the market's tendencies before and after snow, rain, temperature correlation, etc, in a few days.