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- Hartung, Kerstin, et al.
(author)
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Diagnosing topographic forcing in an atmospheric dataset : The case of the North American Cordillera
- 2020
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In: Quarterly Journal of the Royal Meteorological Society. - : Wiley. - 0035-9009 .- 1477-870X. ; 146:726, s. 314-326
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Journal article (peer-reviewed)abstract
- It is well known from modelling studies that surface topography influences the large-scale atmospheric circulation and that several model biases are associated with incorrect representation of topography. The textbook explanation of topographic effects on large-scale circulation appeals to the theoretical relationship between surface forcing and vortex stretching along trajectories in single-layer models. The goal of this study is to design and use a simple diagnostic of the large-scale forcing on the atmosphere when air is passing over topography, directly from atmospheric fields, based on this theoretical relationship. The study examines the interaction of the atmosphere with the North American Cordillera and samples the flow by means of trajectories during Northern Hemisphere winter. We detect a signal of topographic forcing in the atmospheric dataset, which, although much less distinct than in the theoretical relationship, nevertheless exhibits a number of expected properties. Namely, the signal increases with latitude, is usually stronger upslope than downslope, and is enhanced if the flow is more orthogonal to the mountain ridge, for example during periods of positive Pacific-North American index (PNA). Furthermore, a connection is found between an enhanced signal of topographic forcing downslope of the North American Cordillera and periods of more frequent downstream European blocking.
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- Richardson, David s., et al.
(author)
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Jumpiness in Ensemble Forecasts of Atlantic Tropical Cyclone Tracks
- 2024
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In: Weather and forecasting. - : AMER METEOROLOGICAL SOC. - 0882-8156 .- 1520-0434. ; 39:1, s. 203-215
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Journal article (peer-reviewed)abstract
- We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office, and NCEP. We use a divergence function to quantify the change in cross -track position between consecutive ensemble forecasts initialized at 12-h intervals. Results for the 2019-21 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80-90 km in the cross-track direction between a forecast for 120 h ahead and the updated forecast made 12 h later for the same valid time. This quantitative information can support users' decision-making, for example, in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multimodel combinations to reduce the effects of jumpiness. SIGNIFICANCE STATEMENT: Forecasting the tracks of tropical cyclones is essential to mitigate their impacts on society. Numerical weather prediction models provide valuable guidance, but occasionally there is a large jump in the predicted track from one run to the next. This jumpiness complicates the creation and communication of consistent forecast advisories and early warnings. In this work we aim to better understand forecast jumpiness and we provide practical information to forecasters to help them better use the model guidance. We show that the jumpiest cases are different for different modeling centers, that recent model upgrades have reduced forecast jumpiness, and that there is not a strong link between jumpiness and forecast skill.
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