disc_score_dt

Calculate the Generalized discrimination score from a data.table with data belonging to a single group (as defined by the
by variable in the DISS function), for example a single location and month.
Formula (5a) from Mason&amp;2018 is used in the calculation. Mostly auxiliary function for the DISS function.

Provides tools for processing and evaluating seasonal weather forecasts,
with an emphasis on tercile forecasts. We follow the World Meteorological Organization's
"Guidance on Verification of Operational Seasonal Climate Forecasts",
S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: <https://library.wmo.int/idurl/4/56227>).
The development was supported by the European Union’s Horizon 2020 research and innovation
programme under grant agreement no. 869730 (CONFER).
A comprehensive online tutorial is available at <http://files.nr.no/samba/CONFER/SeaVal/>.

Claudio Heinrich-Mertsching

SeaVal

Validation of Seasonal Weather Forecasts

Celine Cunen

Michael Scheuerer

disc_score_dt function

<dl><dt>year</dt>
<dd>a vector of pool variables, typically year.</dd>
<dt>obs</dt>
<dd>a vector of observations the observation column, needs to contain -1 if it falls into the first category,
0 for the second and 1 for the third category.</dd>
<dt>pB</dt>
<dd>a vector of probabilities for the first category.</dd>
<dt>pN</dt>
<dd>a vector of probabilities for the second category.</dd>
<dt>pA</dt>
<dd>a vector of probabilities for the third category.</dd></dl>

Arguments

Generalized Discrimination score — disc_score_dt

<dl>

<dt>year</dt>
<dd>a vector of pool variables, typically year.</dd>


<dt>obs</dt>
<dd>a vector of observations the observation column, needs to contain -1 if it falls into the first category,
0 for the second and 1 for the third category.</dd>


<dt>pB</dt>
<dd>a vector of probabilities for the first category.</dd>


<dt>pN</dt>
<dd>a vector of probabilities for the second category.</dd>


<dt>pA</dt>
<dd>a vector of probabilities for the third category.</dd>

</dl>

disc_score_dt: Generalized Discrimination score

Description

Usage

Value

Arguments

Examples