Science impacts human society in many ways but of
particular importance is the application of scientific
results to the design of forecasting systems.
Forecasting systems are indispensable for making
informed decisions under risk. Informative and reliable
weather forecasts for instance help to better prepare
for or to reduce the exposure to adverse weather.
Therefore, there is a need for an objective and well
understood framework for ``forecast verification'',
i.e. qualitative and quantitative assessment of
forecast performance.

Statistical methods compare historical forecasts with
corresponding verifications, indicating whether the
forecasting system behaved significantly different (in
a statistical sense) from what was expected.

This short course will introduce the participants to
the fundamentals of statistical forecast verification.
Some necessary statistical theory will be discussed as well, and some hands-on numerical experiments will take place using freely available code. More specifically, the course will cover the following topics (more or less in that order)

* Forecast types and scoring rules
* Tests and p-values
* How to cope with dependent data
* How to cope with forecasts of spatial fields
* Code, literature, and further resources

Target audience are researchers (both from academic institutions and operational centres) who are either new to forecast verification or who have practical experience but want to know more about the theory. The course is NOT restricted to atmospheric forecasts, nor exclusively to the assessment of operational forecasting systems. The discussed methods are applicable in many other fields such as parameter estimation, data assimilation, model evaluation, and machine learning.