Dry Return Flow: A Critical Fire Weather Pattern on the Southern Great Plains
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2025
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Details
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Journal Title:Journal of Operational Meteorology
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Description:Cyclogenesis in the lee of the Rocky Mountains frequently induces strong southerly winds on the southern Great Plains. From late fall through early spring, initial low-level moisture advection within this flow is sometimes insufficient to preclude problematic fire spread. When preceded by surface high pressure and light winds—ideal for intentional burning—this dry return flow (DRF) can lead to escapes of pre-existing fire. The emergence of wildfires in DRF is most prevalent where timber fuel types exist on the southern Great Plains. Red flag warnings have historically misrepresented threats in DRF because wildfires evolve from pre-existing fires in conditions that burners and forecasters perceive as relatively benign. Owing to the reason that fire-effectiveness of DRF is dependent upon adverse spread of pre-existing fire, the pattern is most impactful during innocuous states of vegetative fuel dryness and in the absence of government-issued burn bans. With these inherent weather and fuel-based limitations of the fire environment, DRF fire episodes tend to be less volatile than southern Great Plains wildfire outbreaks associated with midlatitude cyclones. DRF fire episodes, however, are a challenge for land/fire/emergency management agencies given their propensity for high volumes of fire dispatches that can overwhelm response capabilities. This paper presents reanalysis composites of atmospheric features, as well as fuelscape characteristics, associated with 21 fire-effective DRF events in Oklahoma, Kansas, and Texas between 2016 and 2020. Operational prediction of a subsequent DRF episode in 2022 is shown and a mitigation messaging template to communicate rapid changes in DRF fire environments is proposed.
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Source:Journal of Operational Meteorology (2025)
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DOI:
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ISSN:2325-6184
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Rights Information:Other
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Compliance:Submitted
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Main Document Checksum:urn:sha-512:b7424b920687d522a2e3d82a20053fa3afc28cfb3798d21807cb06221501fbd2e88f6d4ba91806375078b0de2d29f8ab78bbe8c74eee1800d2a4d8701e1cf83c
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