Synoptic-scale Meteorological Patterns Associated with Heavy Rainfall in the Minnesota Region

Rory Laiho aDepartment of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado

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Katja Friedrich aDepartment of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado

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Andrew C. Winters aDepartment of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado

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Abstract

Situated in the Upper Midwest, Minnesota’s mid-continental location places it in a climate transition zone between eastern U.S. humid conditions and western semi-arid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May-September 1959-2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural-network, self-organizing map (SOM) technique to identify sea-level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed favorable conditions related to moisture and lift were associated with heavy rainfall.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rory Laiho, rory.laiho@Colorado.EDU

Abstract

Situated in the Upper Midwest, Minnesota’s mid-continental location places it in a climate transition zone between eastern U.S. humid conditions and western semi-arid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May-September 1959-2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural-network, self-organizing map (SOM) technique to identify sea-level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed favorable conditions related to moisture and lift were associated with heavy rainfall.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rory Laiho, rory.laiho@Colorado.EDU
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