ens_spread_and_skill: Compute the skill (RMSE) and spread of an ensemble forecast

In andrew-MET/harpPoint: Point verifition for NWP forecasts

Compute the skill (RMSE) and spread of an ensemble forecast

Description

The ensemble mean and spread are computed as columns in a harp_fcst object. Typically the scores are aggregated over lead time by other grouping variables cam be chosen. The mean bias is also computed.

Usage

ens_spread_and_skill(
  .fcst,
  parameter,
  groupings = "leadtime",
  spread_drop_member = NULL,
  jitter_fcst = NULL,
  ...
)

Arguments

.fcst	A harp_fcst object with tables that have a column for observations, or a single forecast table.
parameter	The name of the column for the observed data.
groupings	The groups for which to compute the ensemble mean and spread. See group_by for more information of how grouping works.
spread_drop_member	Which members to drop for the calculation of the ensemble variance and standard deviation. For harp_fcst objects, this can be a numeric scalar - in which case it is recycled for all forecast models; a list or numeric vector of the same length as the harp_fcst object, or a named list with the names corresponding to names in the harp_fcst object.
jitter_fcst	A function to perturb the forecast values by. This is used to account for observation error in the spread. For other statistics it is likely to make little difference since it is expected that the observations will have a mean error of zero.
...	Not used.

Value

An object of the same format as the inputs but with data grouped for the groupings column(s) and columns for rmse, spread and mean_bias.

andrew-MET/harpPoint documentation built on Feb. 23, 2023, 1:06 a.m.