Additive Model Perturbations Scaled by Physical Tendencies for Use in Ensemble Prediction

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.16993/tellusa.3224. This is version 4 of this Preprint.

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Authors

Michael Tsyrulnikov, Elena Astakhova, Dmitry Gayfulin

Abstract

Imperfections and uncertainties in forecast models are often represented in ensemble prediction systems by stochastic perturbations of model equations. In this article, we present a new technique to generate model perturbations. The technique is termed Additive Model-uncertainty perturbations scaled by Physical Tendencies (AMPT). The generated perturbations are independent between different model variables and scaled by the local-area-averaged modulus of physical tendency. The previously developed Stochastic Pattern Generator is used to generate space and time-correlated pseudo-random fields. AMPT attempts to address some weak points of the popular model perturbation scheme known as Stochastically Perturbed Parametrization Tendencies (SPPT). Specifically, AMPT can produce non-zero perturbations even at grid points where the physical tendency is zero and avoids perfect correlations in the perturbation fields in the vertical and between different variables. Due to a non-local link from physical tendency to the local perturbation magnitude, AMPT can generate significantly greater perturbations than SPPT without causing instabilities. Relationships between the bias and the spread caused by AMPT and SPPT were studied in an ensemble of forecasts. The non-hydrostatic, convection-permitting forecast model COSMO was used. In ensemble prediction experiments, AMPT perturbations led to statistically significant improvements (compared to SPPT) in probabilistic performance scores such as spread-skill relationship, CRPS, Brier Score, and ROC area for near-surface temperature. AMPT had similar but weaker effects on near-surface wind speed and mixed effects on precipitation.

DOI

https://doi.org/10.31223/X5T94G

Subjects

Earth Sciences, Other Earth Sciences, Physical Sciences and Mathematics

Keywords

Model uncertainty, ensemble prediction, stochastic perturbations, SPPT

Dates

Published: 2022-11-23 08:35

Last Updated: 2023-11-19 06:40

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
The dataset on which this paper is based is too large to be retained or publicly archived with available resources. Documentation and methods used to support this study are available from the first author.