Given a data frame of ensemble forecasts the ensemble mean, the ensemble standard deviation (spread), ensemble variance, ensemble minimum, and ensemble are calculated.
Usage
ens_stats(
.fcst,
mean = TRUE,
sd = TRUE,
var = FALSE,
min = FALSE,
max = FALSE,
keep_members = FALSE,
...
)
# S3 method for harp_ens_point_df
ens_stats(
.fcst,
mean = TRUE,
sd = TRUE,
var = FALSE,
min = FALSE,
max = FALSE,
keep_members = FALSE,
median = FALSE,
...
)Arguments
- .fcst
A
harp_ens_grid_dforharp_ens_point_dfdata frame, or aharp_listcontaining data frames with those classes.- mean
Logical. Whether to compute the ensemble mean.
- sd
Logical. Whether to compute the ensemble standard deviation.
- var
Logical. Whether to compute the ensemble variance.
- min
Logical. Whether to compute the ensemble minumum.
- max
Logical. Whether to compute the ensemble maximum.
- keep_members
Logical. Whether to keep the ensemble members in the returned object. The default is to only return the statistics.
- ...
Not used.
- median
Logical. Whether to compute the ensemble median.
Details
By default only the ensemble mean and standard deviation are computed. Note that the ensemble median is not yet implemented for gridded data.
Examples
ens_stats(ens_point_df)
#> # A tibble: 48 × 6
#> fcst_dttm lead_time valid_dttm SID ens_mean ens_sd
#> * <dttm> <dbl> <dttm> <dbl> <dbl> <dbl>
#> 1 2021-01-01 00:00:00 0 2021-01-01 00:00:00 1001 0.533 0.362
#> 2 2021-01-01 00:00:00 1 2021-01-01 01:00:00 1001 0.597 0.0752
#> 3 2021-01-01 00:00:00 2 2021-01-01 02:00:00 1001 0.528 0.104
#> 4 2021-01-01 00:00:00 3 2021-01-01 03:00:00 1001 0.552 0.177
#> 5 2021-01-01 00:00:00 4 2021-01-01 04:00:00 1001 0.306 0.320
#> 6 2021-01-01 00:00:00 5 2021-01-01 05:00:00 1001 0.434 0.463
#> 7 2021-01-01 00:00:00 6 2021-01-01 06:00:00 1001 0.377 0.0425
#> 8 2021-01-01 00:00:00 7 2021-01-01 07:00:00 1001 0.273 0.304
#> 9 2021-01-01 00:00:00 8 2021-01-01 08:00:00 1001 0.692 0.359
#> 10 2021-01-01 00:00:00 9 2021-01-01 09:00:00 1001 0.562 0.141
#> # ℹ 38 more rows
ens_stats(ens_point_df, keep_members = TRUE)
#> ::ensemble point forecast:: # A tibble: 48 × 8
#> fcst_dttm lead_time valid_dttm SID point_mbr000
#> <dttm> <dbl> <dttm> <dbl> <dbl>
#> 1 2021-01-01 00:00:00 0 2021-01-01 00:00:00 1001 0.277
#> 2 2021-01-01 00:00:00 1 2021-01-01 01:00:00 1001 0.650
#> 3 2021-01-01 00:00:00 2 2021-01-01 02:00:00 1001 0.601
#> 4 2021-01-01 00:00:00 3 2021-01-01 03:00:00 1001 0.427
#> 5 2021-01-01 00:00:00 4 2021-01-01 04:00:00 1001 0.0798
#> 6 2021-01-01 00:00:00 5 2021-01-01 05:00:00 1001 0.762
#> 7 2021-01-01 00:00:00 6 2021-01-01 06:00:00 1001 0.347
#> 8 2021-01-01 00:00:00 7 2021-01-01 07:00:00 1001 0.488
#> 9 2021-01-01 00:00:00 8 2021-01-01 08:00:00 1001 0.438
#> 10 2021-01-01 00:00:00 9 2021-01-01 09:00:00 1001 0.662
#> # ℹ 38 more rows
#> # ℹ 3 more variables: point_mbr001 <dbl>, ens_mean <dbl>, ens_sd <dbl>
ens_stats(ens_point_df, var = TRUE, min = TRUE, max = TRUE)
#> # A tibble: 48 × 9
#> fcst_dttm lead_time valid_dttm SID ens_mean ens_sd
#> * <dttm> <dbl> <dttm> <dbl> <dbl> <dbl>
#> 1 2021-01-01 00:00:00 0 2021-01-01 00:00:00 1001 0.533 0.362
#> 2 2021-01-01 00:00:00 1 2021-01-01 01:00:00 1001 0.597 0.0752
#> 3 2021-01-01 00:00:00 2 2021-01-01 02:00:00 1001 0.528 0.104
#> 4 2021-01-01 00:00:00 3 2021-01-01 03:00:00 1001 0.552 0.177
#> 5 2021-01-01 00:00:00 4 2021-01-01 04:00:00 1001 0.306 0.320
#> 6 2021-01-01 00:00:00 5 2021-01-01 05:00:00 1001 0.434 0.463
#> 7 2021-01-01 00:00:00 6 2021-01-01 06:00:00 1001 0.377 0.0425
#> 8 2021-01-01 00:00:00 7 2021-01-01 07:00:00 1001 0.273 0.304
#> 9 2021-01-01 00:00:00 8 2021-01-01 08:00:00 1001 0.692 0.359
#> 10 2021-01-01 00:00:00 9 2021-01-01 09:00:00 1001 0.562 0.141
#> # ℹ 38 more rows
#> # ℹ 3 more variables: ens_var <dbl>, ens_min <dbl>, ens_max <dbl>
ens_stats(ens_grid_list)
#> • a
#> # A tibble: 24 × 6
#> fcst_model fcst_dttm lead_time valid_dttm ens_mean ens_sd
#> * <chr> <dttm> <dbl> <dttm> <geolis> <geoli>
#> 1 a 2021-01-01 00:00:00 0 2021-01-01 00:00:00 [5 × 5] [5 × 5]
#> 2 a 2021-01-01 00:00:00 1 2021-01-01 01:00:00 [5 × 5] [5 × 5]
#> 3 a 2021-01-01 00:00:00 2 2021-01-01 02:00:00 [5 × 5] [5 × 5]
#> 4 a 2021-01-01 00:00:00 3 2021-01-01 03:00:00 [5 × 5] [5 × 5]
#> 5 a 2021-01-01 00:00:00 4 2021-01-01 04:00:00 [5 × 5] [5 × 5]
#> 6 a 2021-01-01 00:00:00 5 2021-01-01 05:00:00 [5 × 5] [5 × 5]
#> 7 a 2021-01-01 00:00:00 6 2021-01-01 06:00:00 [5 × 5] [5 × 5]
#> 8 a 2021-01-01 00:00:00 7 2021-01-01 07:00:00 [5 × 5] [5 × 5]
#> 9 a 2021-01-01 00:00:00 8 2021-01-01 08:00:00 [5 × 5] [5 × 5]
#> 10 a 2021-01-01 00:00:00 9 2021-01-01 09:00:00 [5 × 5] [5 × 5]
#> # ℹ 14 more rows
#>
#> • b
#> # A tibble: 24 × 6
#> fcst_model fcst_dttm lead_time valid_dttm ens_mean ens_sd
#> * <chr> <dttm> <dbl> <dttm> <geolis> <geoli>
#> 1 b 2021-01-01 00:00:00 0 2021-01-01 00:00:00 [5 × 5] [5 × 5]
#> 2 b 2021-01-01 00:00:00 1 2021-01-01 01:00:00 [5 × 5] [5 × 5]
#> 3 b 2021-01-01 00:00:00 2 2021-01-01 02:00:00 [5 × 5] [5 × 5]
#> 4 b 2021-01-01 00:00:00 3 2021-01-01 03:00:00 [5 × 5] [5 × 5]
#> 5 b 2021-01-01 00:00:00 4 2021-01-01 04:00:00 [5 × 5] [5 × 5]
#> 6 b 2021-01-01 00:00:00 5 2021-01-01 05:00:00 [5 × 5] [5 × 5]
#> 7 b 2021-01-01 00:00:00 6 2021-01-01 06:00:00 [5 × 5] [5 × 5]
#> 8 b 2021-01-01 00:00:00 7 2021-01-01 07:00:00 [5 × 5] [5 × 5]
#> 9 b 2021-01-01 00:00:00 8 2021-01-01 08:00:00 [5 × 5] [5 × 5]
#> 10 b 2021-01-01 00:00:00 9 2021-01-01 09:00:00 [5 × 5] [5 × 5]
#> # ℹ 14 more rows
#>