weatherbench2.metrics.RankHistogram
- class weatherbench2.metrics.RankHistogram(ensemble_dim='realization', num_bins=None)
Histogram of truth’s rank with respect to forecast ensemble members.
Given a K member ensemble {Xᵢ}, and ground truth Y, the rank of Y is the count of ensemble members less than Y. This class expresses that rank with one-hot encoding, which facilitates averaging/summation (typically over time) to form rank histograms. The histograms will have K+1 bins, and are indexed by ‘bin’.
This class also allows aggregation of the bins into num_bins ≤ K + 1 provided num_bins evently divides K + 1. This reduces the size of output files, but is equivalent to averaging default results along the ‘bin’ dimension.
If these one-hot encodings are averaged over N times, a well calibrated forecast should contain roughly equal values in the bins. The bin variance will be (num_bins - 1) / (N num_bins²). Since the expected value is 1 / num_bins, the relative error is
Sqrt(variance) / expected = Sqrt((num_bins - 1) / N).
- Parameters:
ensemble_dim (str) –
num_bins (Optional[int]) –
- __init__(ensemble_dim='realization', num_bins=None)
Initializes a RankHistogram.
- Parameters:
ensemble_dim (str) – Dimension indexing ensemble member.
num_bins (Optional[int]) – Number of bins in histogram. If None, the number of bins will be ensemble_size + 1. If provided, num_bins must evenly divide into ensemble_size + 1.
Methods
__init__
([ensemble_dim, num_bins])Initializes a RankHistogram.
compute
(forecast, truth[, region, skipna])Evaluate this metric on datasets with full temporal coverages.
compute_chunk
(forecast, truth[, region])Computes one-hot encoding of rank on a chunk of forecast/truth.
Attributes
ensemble_dim