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WxChallenge

WxChallenge is a Python package used to scrape forecast results from the national weather challenge to use for grading purposes in classes.

Main features

  • Compatible with Python3
  • Saves scraped data in an SQL database
  • pandas DataFrames used for data manipulation

Installation

Whenever it's possible, please always use the latest version from the repository. To install it using pip:

pip install git+https://github.com/kwodzicki/WxChallenge

Grading Calculations

Grades are calculated on a per forecast city basis. Options can be changed in the data.py file, be default grading is discussed below:

  • Forecasters are allowed two (2) free days per city; i.e., must forecast for six (6) of the eight (8) days

  • Every day over two (2) missed days results in a penalty of 16.6% (i.e., 100% / required days). This is done so that if you miss the minimum of six (6) forecast days, your score is zero (0) for that city

  • For every day that one forecasts, they should beat climatology. For every day they do not beat climatology, 5% will be deducted from their score (i.e., 30% / required days), with a maximum penalty of 30% possible.

    Note: The cumulative normalized climatology error for the city must be greater than 3 standard deviations worse than national consensus (i.e., > 30.0) for this penalty to occur.

  • Bonus:

    Awarded for beating national and/or local consensus.

    • Local consensus:

      Points awarded as standard devitations better than local forecasters.

    • National consensus:

      For the national consensus, you must be within one standard deviation of national consensus on the last day of the forecast city to get a bonus. This means that the normalized cumulative city score must be less than 10.0. The following formula is then used to compute the bonus:

       -(norm_score / 10.0) + 1.0

      The is no upper limit to the national consensus bonus and is scaled as standard deviations from national consensus.

Excel Spreadsheet Files

The spreadsheet(s) created by the WxChall_Grades CLI contain a sheet for each of the forecast cities and a single sheet for the overall grades In each city spreadsheet, there are first/last name columns, with discussion of the other columns below

For city sheets

  • Forecasts: This is the number of forecasts submitted during the forecast period
  • Absence: This is the penalty (in percent) for the missed forecasts.
  • # Vaca Forecasts: If the --vacation flag was used at the command line, this column indicates how many days a given forecaster forecasted for over a break. By default this value will be zero
  • Climo: This is the penalty for not beating climatology
  • Consen. School: Scaled bonus points that could be applied based on beating school consensus. This value is NOT part of the total score in the last column.
  • Consen. Ntnl.: Scaled bonus points that could be applied based on beating national consensus. This value is NOT part of the total score in the last column.
  • Total: The final score for the city; 100 - absence - climo

For final grades sheet

  • Consen. School: Sum of all scaled bonus points that could be applied based on beating school consensus. This value is NOT part of the total score in the last column.
  • Consen. Ntnl.: Sum of all scaled bonus points that could be applied based on beating national consensus. This value is NOT part of the total score in the last column.
  • Total: Average of all the scores from each city

Code example

	# Scrape data for Fall 2017 semester for Texas A&M
	from WxChallenge import WxChallenge;            # Import package
	wx   = WxChallenge();                           # Initialize instance
	sem  = 'fall';                                  # Define semester
	year = 2017;                                    # Define year
	sch  = 'tam';                                   # Define school
	wx.update_Semester( sem, year, schools = sch ); # Get data & add to SQL
	fcsts = wx.get_forecasts( school = sch, semester = sem, year = year ):

License

WxChallenge is released under the terms of the GNU GPL v3 license.