Algorithmic tuning of spread–skill relationship in ensemble forecasting systems

Ekblom, Madeleine; Tuppi, Lauri; Shemyakin, Vladimir; Laine, Marko; Ollinaho, Pirkka; Haario, Heikki; Järvinen, Heikki

doi:10.1002/qj.3695

Cited by 3 publications

(2 citation statements)

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“…This was done, for example, by Hakkarainen et al (2012) to tune closure parameters in forecast models. Solonen and Järvinen (2013) used likelihood score to tune parameters related to the ensemble system and Ekblom et al (2020) used Kalman filter likelihood cost function to tune the spreadskill relationship of an ensemble forecasting system.…”

Section: Appendix Appendix a Kalman Filter Motivation For The Scorementioning

confidence: 99%

“…In previous studies, filter likelihood has been used as a cost function for optimisation of parameters in idealised set-ups with the Lorenz'95 model (Hakkarainen et al 2012(Hakkarainen et al , 2013Solonen and Järvinen 2013;Ekblom et al 2020). Hakkarainen et al (2012) study three different methods for estimating closure parameters in a Lorenz'95 system, and one of these methods is the filter likelihood approach.…”

Section: Introductionmentioning

confidence: 99%

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Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

Ekblom

Tuppi

Räty

et al. 2023

Tellus A: Dynamic Meteorology and Oceanography

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In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called filter likelihood score which is deep-rooted in Bayesian estimation theory and fits naturally to the filtering setup of NWP. The filter likelihood score considers observation errors, ensemble mean skill, and ensemble spread in one metric. Importantly, it can be made multivariate and effortlessly expanded to simultaneous verification against all observation types through the observation operators contained in the parental data assimilation scheme.Here observations from the global radiosonde network and satellites (AMSU-A channel 5) are included in the verification of OpenIFS-based ensemble forecasts using different types of initial state perturbations. Our results show that the filter likelihood score is sensitive to the ensemble prediction system quality and compares consistently with other verification metrics such as the relationships between ensemble spread and ensemble mean forecast error, and Dawid-Sebastiani score. Our conclusion is that the filter likelihood score provides a very well-behaving verification metric, that can be made truly multivariate by including covariances, for ensemble prediction systems with a strong foundation in estimation theory.

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Section: Appendix Appendix a Kalman Filter Motivation For The Scorementioning

confidence: 99%