Improved Analog Ensemble Formulation for 3-Hourly Precipitation Forecasts

Abstract

Abstract Analog ensembles (AnEns) traditionally use a single numerical weather prediction (NWP) model to make a forecast, then search an archive to find a number of past similar forecasts (analogs) from that same model, and finally retrieve the actual observations corresponding to those past forecasts to serve as members of an ensemble forecast. This study investigates new statistical methods to combine analogs into ensemble forecasts and validates them for 3-hourly precipitation over the complex terrain of British Columbia, Canada. Applying the past analog error to the target forecast (instead of using the observations directly) reduces the AnEn dry bias and makes prediction of heavy-precipitation events probabilistically more reliable—typically the most impactful forecasts for society. Two variants of this new technique enable AnEn members to obtain values outside the distribution of the finite archived observational dataset—that is, they are theoretically capable of forecasting record events, whereas traditional analog methods cannot. While both variants similarly improve heavier precipitation events, one variant predicts measurable precipitation more often, which enhances accuracy during winter. A multimodel AnEn further improves predictive skill, albeit at higher computational cost. AnEn performance shows larger sensitivity to the grid spacing of the NWP than to the physics configuration. The final AnEn prediction system improves the skill and reliability of point forecasts across all precipitation intensities. Significance Statement The analog ensemble (AnEn) technique is a data-driven method that can improve local weather forecasts. It improves raw model forecasts using past similar model predictions and observations, reducing future forecast errors and providing probabilities for a range of possible outcomes. One limitation of AnEns is that they commonly tend to make rare-event (e.g., heavy precipitation) forecasts appear less extreme. Usually, heavier precipitation events have a higher impact on society and the economy. This study introduces two new AnEn techniques that make operational forecasts of both probabilities and most likely amounts more accurate for heavy precipitation.