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Abstract The Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted during February–March 2022 to observe multiscale processes impacting the variability and predictability of precipitation type and amount under near-freezing conditions over the Saint Lawrence River valley. Intensive observation period (IOP) 4 of the campaign occurred 17–18 February 2022 in association with an upper-level trough positioned over the north-central United States and a surface cyclone that traversed the study domain along a frontal boundary that extended northeast of the cyclone. The timing of precipitation-type transitions during the event was consistently too slow within operational forecast models at 2–5-day lead times. Consequently, this study aims to understand how forecast model representations of dynamical and thermodynamical processes on the synoptic scale to mesoscale may have influenced the predictability of precipitation type during IOP4. To do so, an ensemble of operational forecasts from the Global Ensemble Forecast System initialized 5 days prior to IOP4 was divided into three clusters according to the strength and position of the frontal zone over the Saint Lawrence River Valley during the event. Ensemble sensitivity analyses and spatial composites suggest that differences in the position of the frontal zone between clusters are dynamically linked to the differences in the structure of the associated upstream upper-level trough at prior forecast lead times. A diagnosis of the divergent circulation prior to the event suggests that feedback mechanisms between the surface cyclone, its attendant frontal boundaries, and the upper-level flow pattern help to further explain differences in the frontal zone between clusters. Significance StatementMixed-phase precipitation events, which can produce rain, freezing rain, ice pellets, and snow, are difficult to accurately forecast. This study investigates the large-scale processes influencing our ability to accurately forecast the precipitation type and amount during one of these events that was observed by a field campaign in February 2022. In forecasts initialized 5 days prior to the event, differences in the forecast upper-level atmospheric conditions led to differences in the forecast interactions between the upper-level flow and a low pressure system at the surface. As a result, there was large uncertainty in the predicted position of a surface front associated with the low pressure system and the precipitation-type distribution during the event.
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This content will become publicly available on December 28, 2026
Predictability of Compound Impacts From Hurricane Helene and Predecessor Rain Event in CFSv2 Operational Forecasts
Abstract In late September 2024, Hurricane Helene contributed to catastrophic flooding in the Southeastern United States. The impacts of the hurricane were compounded by a predecessor rain event (PRE) 1‐day earlier, inducing unusually high precipitation and soil moisture (SM). In this case study, we examined the predictability of precipitation and SM conditions associated with these events in NOAA's operation Coupled Forecast System model (CFSv2). Specifically, we investigated the predictability of Helene and the PRE as a function forecast lead time (LT). To assess the model's ability to represent both Helene and PRE, as well as the predictability of their resulting precipitation and SM, we applied tracking of both systems with different LTs from 3 to 6 days. Our results show that the predictability drops around 4‐ to 5‐day LTs, in association with biases in the timing and location of Helene and PRE, as well as underestimated precipitation associated with the PRE.
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- Award ID(s):
- 2244917
- PAR ID:
- 10659231
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 52
- Issue:
- 24
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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