Ensemble Predictability of Week 3 to 4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation.

Forecasting the Week 3/4 period presents many challenges, as numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize better predicted large-scale circulation regimes to make forecasts of temperature and precipitation anomalies and extreme events, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of cluster analysis of geopotential heights over the broad Pacific-North American (PNA) region in forecasting surface weather. Using 14-day running means of ECMWF Reanalysis v5 (ERA5) 500-hPa geopotential heights (Z500) for the wintertime (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing compositing of any reanalysis or observational variable to form cluster maps. Maps of Z500, 2-m temperature, precipitation, and storm track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. The Figure shows an example ensemble forecast for the week 3/4 period of Feb. 1-14, 2023 (initialized on 17 Jan. 2023). The top left figure is the multi-model ensemble mean z500 anomaly (m) 14-day mean forecast from 185 ensemble members. The top right shows the corresponding z500 cluster ensemble mean forecast map for Cluster 5, the cluster assigned to the multi-model ensemble mean forecast. The cluster ensemble weighted z500 anomaly forecast (bottom left) uses the cluster weights (bottom right). Weights are derived from the proportions of assigned clusters. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster derived forecasts. Our results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather statistics. A further two-step clustering approach will be discussed, in which occurrences of each Z500 regime over the broad PNA region are further analyzed using a cluster analysis of the wind fields over only the United States, leading to closer association between the large-scale and regional circulation and weather extremes.