Rapid adjustment and post-processing of temperature forecast trajectories

TL;DR

This paper introduces a method for rapidly adjusting temperature forecast trajectories between forecast cycles by leveraging error correlation structures, significantly improving short-term forecast accuracy before the next NWP update.

Contribution

It proposes a novel rapid adjustment technique that enhances temperature forecast trajectories between cycles using error correlation, outperforming traditional updates in the initial hours.

Findings

Rapid adjustment outperforms new forecasts in the first few hours.

Error correlation structure can be exploited for better short-term forecasts.

Method improves forecast accuracy before the next NWP cycle.

Abstract

Modern weather forecasts are commonly issued as consistent multi-day forecast trajectories with a time resolution of 1-3 hours. Prior to issuing, statistical post-processing is routinely used to correct systematic errors and misrepresentations of the forecast uncertainty. However, once the forecast has been issued, it is rarely updated before it is replaced in the next forecast cycle of the numerical weather prediction (NWP) model. This paper shows that the error correlation structure within the forecast trajectory can be utilized to substantially improve the forecast between the NWP forecast cycles by applying additional post-processing steps each time new observations become available. The proposed rapid adjustment is applied to temperature forecast trajectories from the UK Met Office's convective-scale ensemble MOGREPS-UK. MOGREPS-UK is run four times daily and produces hourly forecasts for up to 36 hours ahead. Our results indicate that the rapidly adjusted forecast from the previous NWP forecast cycle outperforms the new forecast for the first few hours of the next cycle, or until the new forecast itself can be rapidly adjusted, suggesting a new strategy for updating the forecast cycle.