Coastal Tropical Convection in a Stochastic Modeling Framework

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dc.contributor.authorBergemann, Martin
dc.contributor.authorKhouider, Boualem
dc.contributor.authorJakob, Christian
dc.date.accessioned2019-06-17T16:22:51Z
dc.date.available2019-06-17T16:22:51Z
dc.date.copyright2017en_US
dc.date.issued2017
dc.description.abstractRecent research has suggested that the overall dependence of convection near coasts on large-scale atmospheric conditions is weaker than over the open ocean or inland areas. This is due to the fact that in coastal regions convection is often supported by meso-scale land-sea interactions and the topography of coastal areas. As these effects are not resolved and not included in standard cumulus parametrization schemes, coastal convection is among the most poorly simulated phenomena in global models. To outline a possible parametrization framework for coastal convection we develop an idealized modeling approach and test its ability to capture the main characteristics of coastal convection. The new approach first develops a decision algorithm, or trigger function, for the existence of coastal convection. The function is then applied in a stochastic cloud model to increase the occurrence probability of deep convection when land-sea interactions are diagnosed to be important. The results suggest that the combination of the trigger function with a stochastic model is able to capture the occurrence of deep convection in atmospheric conditions often found for coastal convection. When coastal effects are deemed to be present the spatial and temporal organization of clouds that has been documented form observations is well captured by the model. The presented modeling approach has therefore potential to improve the representation of clouds and convection in global numerical weather forecasting and climate models.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipWe would like to acknowledge Todd P. Lane for his valuable suggestions that helped to realize develop the presented method. We are also very grateful to the two anonymous reviewers that have helped to improve the quality of the submitted manuscript. This research was supported in part by the Monash University eResearch Centre and eSolutions-Research Support Services through the use of the high-memory capability on the Monash University Campus HPC Cluster. We also acknowledge the Australian Research Council's Centre of Excellence for Climate System Science (CE110001028) for funding this work. The CMORPH satellite based rainfall estimates were obtained from the Climate Prediction Center (CPC) of the National Oceanic and Atmosphere Administration (NOAA). The Erainterim reanalysis data are supplied by the European Center for Medium Weather Forecast (ECMWF). The source code and a documentation of the algorithm that detects coastline associated rainfall can be retrieved from Zenodo (https://doi.org/10.5281/zenodo.44405) or via GitHub (https://github.com/antarcticrainforest/ PatternRecog).en_US
dc.identifier.citationBergemann, M.; Khouider, B.; & Jakob, C. (2017). Coastal tropical convection in a stochastic modeling framework. Journal of Advances in Modeling Earth Systems, 9(7), 2561-2582. DOI: 10.1002/2017MS001048en_US
dc.identifier.urihttps://doi.org/10.1002/2017MS001048
dc.identifier.urihttp://hdl.handle.net/1828/10922
dc.language.isoenen_US
dc.publisherJournal of Advances in Modeling Earth Systemsen_US
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleCoastal Tropical Convection in a Stochastic Modeling Frameworken_US
dc.typeArticleen_US

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