Abstract
Abstract. With numerical weather prediction ensembles unable to produce sufficiently calibrated forecasts, statistical post-processing is needed to correct deterministic and probabilistic biases. Over the past decades, a number of methods addressing this issue have been proposed, with ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) among the most popular. They are able to produce skillful deterministic and probabilistic forecasts for a wide range of applications. These methods are usually applied to the newest model run as soon as it has finished, before the entire forecast trajectory is issued. RAFT (rapid adjustment of forecast trajectories), a recently proposed novel approach, aims to improve these forecasts even further, utilizing the error correlation patterns between lead times. As soon as the first forecasts are verified, we start updating the remainder of the trajectory based on the newly gathered error information. As RAFT works particularly well in conjunction with other post-processing methods like EMOS and techniques designed to reconstruct the multivariate dependency structure like ensemble copula coupling (ECC), we look to identify the optimal combination of these methods. In our study, we apply multi-stage post-processing to wind speed forecasts from the UK Met Office's convective-scale MOGREPS-UK ensemble and analyze results for short-range forecasts at a number of sites in the UK and the Republic of Ireland.
Highlights
Numerical weather prediction (NWP) is an inherently uncertain process, and even with present-day computational resources, ensembles can not produce perfect forecasts (Buizza, 2018)
Calibration is the statistical consistency between the forecasts and the observations, and sharpness refers to the amount of predictive uncertainty and the extent of information contained in the forecast
It will be important to see whether ensemble copula coupling (ECC) should be run once, like ensemble model output statistics (EMOS), subsequent to the end of the NWP model run, or whether it should be continuously applied every time the rapid adjustment of forecast trajectories (RAFT) adjustment occurs
Summary
Numerical weather prediction (NWP) is an inherently uncertain process, and even with present-day computational resources, ensembles can not produce perfect forecasts (Buizza, 2018). Statistical post-processing methods have been successfully applied to address these deficiencies, aiming to resolve a multitude of issues. Two important properties of probabilistic forecasts are calibration and sharpness (Gneiting et al, 2007). Calibration is the statistical consistency between the forecasts and the observations, and sharpness refers to the amount of predictive uncertainty and the extent of information contained in the forecast. NWP ensembles lack calibration, as they can not consider all sources of atmospheric uncertainty, but they are quite sharp. The main goal of any statistical post-processing process should be to maximize the forecast’s sharpness, subject to it being calibrated (Gneiting et al, 2007)
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