Large-scale Statistically Meaningful Patterns (LSMPs) associated with precipitation extremes over Northern California

We analyze the large-scale statistically meaningful patterns (LSMPs), also called large-scale meteorological patterns, that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k-means clustering to the two leading principal components of daily 500hPa geopotential height anomalies persisting two days before the onset. A statistical significance test based on the Monte Carlo simulations suggests the existence of a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as the NW continental negative height anomaly, the Eastward positive “PNA”, the Westward negative “PNA”, and the Prominent Alaskan ridge. These four clusters, shown in multiple atmospheric and oceanic variables, evolve very differently and have distant links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula-based framework for predicting PEx events, we show that the LSMP indices are useful predictors of NorCal PEx events, with the moisture-based variables being the best predictors of PEx events at least six days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance.