Bound on forecasting skill for models of North Atlantic tropical cyclone counts
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909340001107968 |
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| author | Wesley, Daniel Mann, Michael E. Jain, Bhuvnesh Twomey, Colin R. Christiansen, Shannon |
| author_facet | Wesley, Daniel Mann, Michael E. Jain, Bhuvnesh Twomey, Colin R. Christiansen, Shannon |
| contents | Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find that this bound is already saturated by a simple linear model that explains roughly 50 percent of the annual variance using three climate indices: El Niño Southern Oscillation (ENSO), average sea surface temperature (SST) in the main development region (MDR) of the North Atlantic and the North Atlantic oscillation (NAO) atmospheric circulation index (Kozar et al 2012). As expected under the bound, increased model complexity does not help: we demonstrate that allowing for quadratic and interaction terms, or using an Elastic Net to forecast TC counts using global SST maps, produces no detectable increase in skill. We provide evidence that observed TC counts are consistent with a Poisson process, limiting possible improvements in TC modeling by relaxing the Poisson assumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05503 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bound on forecasting skill for models of North Atlantic tropical cyclone counts Wesley, Daniel Mann, Michael E. Jain, Bhuvnesh Twomey, Colin R. Christiansen, Shannon Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find that this bound is already saturated by a simple linear model that explains roughly 50 percent of the annual variance using three climate indices: El Niño Southern Oscillation (ENSO), average sea surface temperature (SST) in the main development region (MDR) of the North Atlantic and the North Atlantic oscillation (NAO) atmospheric circulation index (Kozar et al 2012). As expected under the bound, increased model complexity does not help: we demonstrate that allowing for quadratic and interaction terms, or using an Elastic Net to forecast TC counts using global SST maps, produces no detectable increase in skill. We provide evidence that observed TC counts are consistent with a Poisson process, limiting possible improvements in TC modeling by relaxing the Poisson assumption. |
| title | Bound on forecasting skill for models of North Atlantic tropical cyclone counts |
| topic | Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2410.05503 |