Preprints
https://doi.org/10.5194/wcd-2022-6
https://doi.org/10.5194/wcd-2022-6
 
03 Feb 2022
03 Feb 2022
Status: a revised version of this preprint is currently under review for the journal WCD.

The Cyclogenesis Butterfly: Uncertainty growth and forecast reliability during extratropical cyclogenesis

Mark John Rodwell1 and Heini Wernli2 Mark John Rodwell and Heini Wernli
  • 1European Centre for Medium–Range Weather Forecasts, Reading, UK
  • 2Institute for Atmospheric and Climate Science, ETH Zürich, Switzerland

Abstract. Global numerical weather prediction is often limited by Lorenz–type “butterflies” in the flow. Here we think of these as local flow configurations associated with pronounced uncertainty growth–rates, as demonstrated in short–range ensemble predictions. Some of these configurations correspond to potential instabilities of the flow. Often, forecasts for severe downstream weather only show an improvement in skill when these butterflies have passed. Here we focus on the “cyclogenesis butterfly” – associated with baroclinic and convective instabilities in the extratropics. One question addressed is how do different operational ensemble forecast systems (within the “TIGGE” archive) represent the associated uncertainty growth–rates? To test which might be “better”, an extended spread–error equation is used to investigate how well these non–linear models maintain short–range statistical reliability during cyclogenesis. For the European Centre for Medium–Range Weather Forecasts (ECMWF) ensemble, considerable short–range over–spread is found in the North Atlantic stormtrack during winter 2020/21 – representing a source of untapped predictability (this result appears to generalise to other stormtracks and at least one other model). Flow–type clustering demonstrates that this over–spread is directly associated with the representation of cyclogenesis. We attempt to quantify the contributions to the total spread in cyclogenesis cases from initial uncertainty (as derived from ensemble data assimilation), singular vector perturbations, and model uncertainty. At day 2, we find that up to 25 % can be associated with the singular vectors and up to 25 % with model uncertainty. The over–spread suggests that reductions in forecast error over recent years would permit further development of these uncertainty aspects. The sensitivities of spread to resolution, the explicit representation of convection, and the assimilation of local observations provide additional insight for future development.

Mark John Rodwell and Heini Wernli

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2022-6', Ron McTaggart-Cowan, 30 Mar 2022
    • AC1: 'Reply on RC1', Mark Rodwell, 07 Jun 2022
  • RC2: 'Comment on wcd-2022-6', Anonymous Referee #2, 07 Apr 2022
    • AC2: 'Reply on RC2', Mark Rodwell, 07 Jun 2022
  • AC3: 'Comment on wcd-2022-6', Mark Rodwell, 05 Jul 2022
  • AC4: 'Comment on wcd-2022-6', Mark Rodwell, 05 Jul 2022

Mark John Rodwell and Heini Wernli

Mark John Rodwell and Heini Wernli

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Short summary
In 1972, Prof. Ed. Lorenz gave a talk titled "Predictability; Does the Flap of a Butterfly's wings in Brazil Set Off a Tornado in Texas?". The chaotic error growth that under-pins the "Butterfly Effect" is a strong limiter to weather forecast skill. Here, we look at the metaphorical butterfly associated with midlatitude storm formation. We show that models differ widely in the predicted error growth, and that a better representation in the ECMWF model could improve probabilistic forecast skill.