Predictability of a Nested Limited-Area Model

René Laprise Cooperative Centre for Research in Mesometeorology, and Department of Earth and Atmosphere Sciences, University of Québec at Montréal, Montreal, Quebec, Canada

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Mundakkara Ravi Varma Cooperative Centre for Research in Mesometeorology, and Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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Bertrand Denis Cooperative Centre for Research in Mesometeorology, and Department of Earth and Atmosphere Sciences, University of Québec at Montréal, and Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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Daniel Caya Cooperative Centre for Research in Mesometeorology, and Department of Earth and Atmosphere Sciences, University of Québec at Montréal, Montreal, Quebec, Canada

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Isztar Zawadzki Cooperative Centre for Research in Mesometeorology, and Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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Abstract

This note investigates the nature of the extended predictability commonly attributed to high-resolution limited-area models (LAM) nested with low-resolution data at their lateral boundaries. LAM simulations are performed with two different sets of initial, nesting, and verification data: one is a set of regional objective analyses and the other is a synthetic high-resolution model-generated dataset. The simulation differences (equivalent to forecast errors in an operational framework) are studied in terms of their horizontal scale distribution normalized by the natural variability in each scale, as a measure of predictability, which constitutes an original contribution of this note. The results suggest that the extended predictability in LAM is confined to those scales that are present both in the initial condition and lateral boundary conditions (LBCs). No evidence is found for extended predictability of scales that are not forced through the LBCs. Instead, these smaller scales exhibit predictive timescales in direct relation to their spatial scales, similar to the behavior in autonomous global models.

Current affiliation: Desert Research Institute, University of Nevada System, Reno, Nevada.

* Corresponding author address: Prof. René Laprise, Département des Sciences de la Terre et de l’Atmosphère, UQAM, B.P. 8888, Succ. Centre-ville, Montréal, PQ H3C 3P8, Canada.

Email: Laprise.Rene@uqam.ca

Abstract

This note investigates the nature of the extended predictability commonly attributed to high-resolution limited-area models (LAM) nested with low-resolution data at their lateral boundaries. LAM simulations are performed with two different sets of initial, nesting, and verification data: one is a set of regional objective analyses and the other is a synthetic high-resolution model-generated dataset. The simulation differences (equivalent to forecast errors in an operational framework) are studied in terms of their horizontal scale distribution normalized by the natural variability in each scale, as a measure of predictability, which constitutes an original contribution of this note. The results suggest that the extended predictability in LAM is confined to those scales that are present both in the initial condition and lateral boundary conditions (LBCs). No evidence is found for extended predictability of scales that are not forced through the LBCs. Instead, these smaller scales exhibit predictive timescales in direct relation to their spatial scales, similar to the behavior in autonomous global models.

Current affiliation: Desert Research Institute, University of Nevada System, Reno, Nevada.

* Corresponding author address: Prof. René Laprise, Département des Sciences de la Terre et de l’Atmosphère, UQAM, B.P. 8888, Succ. Centre-ville, Montréal, PQ H3C 3P8, Canada.

Email: Laprise.Rene@uqam.ca

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