Fig. 1: Performance metrics for each benchmark phase partitioning method (PPM). | Nature Communications

Fig. 1: Performance metrics for each benchmark phase partitioning method (PPM).

From: Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology

Fig. 1

These metrics include accuracy (a), snow bias (b), and rain bias (c) plotted by air temperature for the crowdsourced and synoptic datasets. The vertical dashed line in each panel represents the 0 °C isotherm. Note: the −100% snow and rain bias values in (b) and (c), respectively, correspond to air temperatures where the method does not predict the given phase. For example, Ta1.0 predicts only rain above 1.0°C and thus presents a −100% bias for all observed snowfall above that threshold. Source data are provided as a Source Data file.

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