Dependencies are included in requirements.txt. Install with pip install -r requirements.txt.

Additionally, one of the dependencies is not yet included in the PyPI for Python 3. Install it through:

pip install git+https://github.com/vanife/pyfscache

The program and ensemble can be configured by modifying config.ini.

• Rank histograms
• Skill scores
• CRPS
• QQ plot

Assumptions

• Temperatures can be modeled by Normal distributions
• Ensemble members are equally likely to represent the observation a priori
• The ensemble set behaves as a randomly selected sample from the expected distribution of observations.

Pseudo code

function main() {
Input:
    Forecast hours F
    ensemble set S
Output:
    PDF for each forecast hour

For each forecast hour f in F:
    For each ensemble member m in set s:
        m.variance <- getVariance()
    f.pdf <- getPdf()

return F
}

Options for variance

• Get control member variance
• Derive member variance from ensemble mean variance

Options for model combination

• Uniform distribution as prior

The following data columns

• Model name + Element name
• Element observation value
• Element value
• Station id
• Forecast hour
• Issue date
• Valid date

Do something with covariance between model hours

• Maybe Gaussian process fit on the ensemble members.
• Or fit all hours together respecting their covariance
• Maybe have a look into time series modeling to see if we can use autocorrelation

Relate variance between multiple issues for the same forecast hour