my_results <- arrow::open_dataset("s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet/project_id=neon4cast/duration=P1D/variable=nee/model_id=bookcast_forest?endpoint_override=sdsc.osn.xsede.org")
df <- my_results |>
filter(site_id == "OSBS",
reference_datetime > as_date("2024-03-15")) |>
collect()22 Analyzing forecasts
The scores for the forecast generated by the book have been found in the catalog. The catalog provides metadata and code that is used for data access.
22.1 Analyzing submitted forecasts
The code to download all forecasts generated by the model used in this book is:
The code above can be found in the catalog link at the top of the page.
22.2 Aggregated scores
We can look at the mean score for the process model but this provides very little context for the quality of forecast. It is more informative to compare the score to the score from other models.
df |>
summarise(mean_crps = mean(crps, na.rm = TRUE))# A tibble: 1 × 1
mean_crps
<dbl>
1 0.88922.3 Comparing to baselines
We will benchmark our process model forecast against to two “naive” baselines of climatology and persistenceRW.
all_results <- arrow::open_dataset("s3://anonymous@bio230014-bucket01/challenges/scores/bundled-parquet/project_id=neon4cast/duration=P1D/variable=nee?endpoint_override=sdsc.osn.xsede.org")
df_with_baselines <- all_results |>
filter(site_id == "OSBS",
reference_datetime > as_date("2024-03-15"),
model_id %in% c("bookcast_forest", "climatology", "persistenceRW")) |>
collect()22.4 Visualization
How do the forecasts look for a single reference_datetime
df_with_baselines |>
filter(as_date(reference_datetime) == as_date("2024-10-01")) |>
ggplot(aes(x = datetime)) +
geom_ribbon(aes(ymin = quantile02.5, ymax = quantile97.5, fill = model_id), alpha = 0.3) +
geom_line(aes(y = median, color = model_id)) +
geom_point(aes(y = observation)) +
labs(y = "forecast") +
theme_bw()
22.5 Aggregated scores
We can first look at the aggregated scores (all reference_datetime and datetime combinations). Importantly, the code below uses pivot_wider and pivot_longer to ensure we only include datetime values where all three models provided forecasts. Otherwise, there would be different periods from the three models in the aggregated score.
df_with_baselines |>
select(model_id, crps, datetime, reference_datetime) |>
group_by(model_id, datetime, reference_datetime) |>
slice(1) |>
ungroup() |>
pivot_wider(names_from = model_id, values_from = crps) |>
na.omit() |>
pivot_longer(-c(datetime, reference_datetime), names_to = "model_id", values_to = "crps") |>
summarise(mean_crps = mean(crps), .by = c("model_id")) |>
ggplot(aes(x = model_id, y = mean_crps)) +
geom_bar(stat="identity") +
labs(y = "mean CRPS") +
theme_bw()
22.6 By horizon
How does forecast performance change as forecasts extend farther in the future (increasing horizon), regardless of when the forecast was produced?
df_with_baselines |>
group_by(model_id, datetime, reference_datetime) |>
slice(1) |>
ungroup() |>
mutate(horizon = as.numeric(datetime - reference_datetime) / 86400) |>
select(model_id, horizon, datetime, reference_datetime, crps) |>
pivot_wider(names_from = model_id, values_from = crps) |>
na.omit() |>
pivot_longer(-c(horizon, datetime, reference_datetime), names_to = "model_id", values_to = "crps") |>
summarize(mean_crps = mean(crps), .by = c("model_id", "horizon")) |>
ggplot(aes(x = horizon, y = mean_crps, color = model_id)) +
geom_line() |>
labs(y = "mean CRPS") +
theme_bw()
22.7 By reference datetime
How does forecast performance vary across the dates that the forecasts are generated, regardless of horizon?
df_with_baselines |>
select(model_id, datetime, reference_datetime, crps) |>
group_by(model_id, datetime, reference_datetime) |>
slice(1) |>
ungroup() |>
pivot_wider(names_from = model_id, values_from = crps) |>
na.omit() |>
pivot_longer(-c(datetime, reference_datetime), names_to = "model_id", values_to = "crps") |>
summarize(mean_crps = mean(crps), .by = c("model_id", "reference_datetime")) |>
ggplot(aes(x = reference_datetime, y = mean_crps, color = model_id)) +
geom_line() +
labs(y = "mean CRPS") +
theme_bw()
22.8 Additional comparisons
Forecasts can be compared across site_id (aggregating across all reference_datetime and horizon) if there are multiple sites and datetime (aggregating across all horizon). Since CRPS is in the naive units of the variable, it can not be compared across variables.
22.9 Reading
Lewis, A. S. L., Woelmer, W. M., Wander, H. L., Howard, D. W., Smith, J. W., McClure, R. P., et al. (2022). Increased adoption of best practices in ecological forecasting enables comparisons of forecastability. Ecological Applications, 32(2), e02500. https://doi.org/10.1002/eap.2500