"The Simplest Idea One Can Have" for Seamless Forecasts with Postprocessing

TL;DR

This paper introduces a simple yet effective method for seamless multimodel weather forecasting by using model and observation persistence, eliminating forecast jumps and improving forecast continuity.

Contribution

It proposes a straightforward approach to maintain forecast smoothness across model transitions by leveraging the latest observations and model persistence, enhancing forecast quality.

Findings

Seamless forecasts show no visible jumps at model transition points.

Using persistence improves forecast continuity and accuracy.

Method extends to various predictors and postprocessing techniques.

Abstract

Seamless forecasts are based on a combination of different sources to produce the best possible forecasts. Statistical multimodel postprocessing helps to combine various sources to achieve these seamless forecasts. However, when one of the combined sources of the forecast is not available due to reaching the end of its forecasting horizon, forecasts can be temporally inconsistent and sudden drops in skill can be observed. To obtain a seamless forecast, the output of multimodel postprocessing is often blended across these transitions, although this unnecessarily worsens the forecasts immediately before the transition. Additionally, large differences between the latest observation and the first forecasts can be present. This paper presents an idea to preserve a smooth temporal prediction until the end of the forecast range and increase its predictability. This optimal seamless forecast is simply accomplished by not excluding any model from the multimodel by using the latest possible lead time as model persistence into the future. Furthermore, the gap between the latest available observation and the first model step is seamlessly closed with the persistence of the observation by using the latest observation as additional predictor. With this idea, no visible jump in forecasts is observed and the verification presents a seamless quality in terms of scores. The benefit of accounting for observation and forecast persistence in multimodel postprocessing is illustrated using a simple temperature example with linear regression but can also be extended to other predictors and postprocessing methods.