A 16-Year Climatology of WPC-Analyzed Drylines and Their Association with Severe Convection

Hosek, Michael J.; Hoogewind, Kimberly A.; Clark, Adam J.; Justin, Andrew D.; Allen, John T.

doi:10.1175/JAMC-D-24-0124.1

i

A 16-Year Climatology of WPC-Analyzed Drylines and Their Association with Severe Convection

2025
By Hosek, Michael J. ; Hoogewind, Kimberly A. ; Clark, Adam J. ; ...

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Journal Title:

Journal of Applied Meteorology and Climatology
Personal Author:

Hosek, Michael J. ; Hoogewind, Kimberly A. ; Clark, Adam J. ; Justin, Andrew D. ; Allen, John T.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations)
Description:

A 16-yr (2007–22) climatology of drylines is presented. Constructed using NOAA Weather Prediction Center (WPC) surface analyses, this climatology addresses the limitations of season, time of day, and region of previous dryline studies by using the full surface analysis archive to include drylines throughout the entire day at 3-h increments for the entire year over the contiguous United States. Severe storm reports and NWS-issued severe thunderstorms and tornado warnings are used to associate individual drylines with severe (or potentially severe) convection. March–June are the months with the greatest frequency of drylines and dryline-associated severe thunderstorms. Regardless of season, average dryline longitude over the course of a day mimics the conceptual model of daily dryline evolution with an eastward shift during the day and westward retreat overnight. The overwhelming majority of WPC-analyzed drylines are located in the southern Great Plains, particularly southwest Texas through the Texas Panhandle, and on average are furthest west in summer and furthest east in winter. A significant increase in the number of analyzed drylines and dryline days per year is identified in the WPC surface analysis archive. This increase is consistent across all analysis times and between drylines associated with and without severe convection. However, using a machine learning model to automate dryline detection, no statistically significant trend is found over this same analysis period. Thus, the increase in WPC-analyzed drylines during the 16-yr period is likely to be the result of nonmeteorological factors.
Keywords:

Machine Learning Synoptic Climatology Machine Learning Synoptic Climatology Drylines Interannual Variability Seasonal Variability
Source:

J. Appl. Meteor. Climatol., 64, 383–399
DOI:

https://doi.org/10.1175/JAMC-D-24-0124.1
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA210AR4320204 ; Grant no. NA20OAR4590347
Rights Information:

Other
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Submitted
Main Document Checksum:

urn:sha-512:ad91b18622054803a3bc7f193238cf52f66e77b9372923507557439ff84516f60a9cc14688c7a488fba41cd95eb639a1c9f1c544032fa3737ea424fe95d77c1c
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https://repository.library.noaa.gov/view/noaa/70617/noaa_70617_DS1.pdf
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[PDF - 11.79 MB ]

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