skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Development of frontal boundaries during the extratropical transition of tropical cyclones
Abstract This study seeks to characterize the development of atmospheric fronts during the extratropical transition (ET) of tropical cyclones (TCs) as a function of their evolution during ET. Composite histograms indicate that the magnitude of the lower atmospheric frontogenesis and average sea‐surface temperature is different based on the nature of the TC's structural change during ET. We find that the development of cold and warm fronts evolves as expected from conceptual models of extratropical cyclones. Composites of these fronts relative to the completion of ET show that azimuth, storm motion, and deep‐layer shear all appear to have equal influence on the frontal positions. TCs that have more fronts at the time of ET onset complete ET more quickly, suggesting that pre‐existing fronts before ET begins may contribute to a shorter ET duration. The orientations of fronts at ET completion in the North Atlantic and west Pacific align with the climatological distributions of the sea‐surface temperatures associated with the western boundary currents in each of those basins. These results provide a perspective on the locations of frontal development within TCs undergoing ET.  more » « less
Award ID(s):
2023585
PAR ID:
10521904
Author(s) / Creator(s):
; ;
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Quarterly Journal of the Royal Meteorological Society
Volume:
150
Issue:
759
ISSN:
0035-9009
Page Range / eLocation ID:
995 to 1011
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract This study investigates Gulf Stream (GS) sea surface temperature (SST) anomalies associated with the extratropical transition (ET) of tropical cyclones (TCs) in the North Atlantic. Composites of western North Atlantic TCs indicate that GS SSTs are warmer, and both large‐ and fine‐scale SST gradients are weaker than average, for TCs that begin the ET process but do not complete it, compared with TCs that do. Further analysis suggests that the associated fine‐scale GS SST gradient anomalies are related to atmospheric processes but not the same as those that are typically associated with the onset of ET. As sensible heat flux gradients and surface diabatic frontogenesis are shown to generally scale with the local SST gradient strength, these results suggest that knowledge of the fine‐scale GS SST gradient in the weeks prior to the arrival of a TC might potentially provide additional information regarding the likelihood of that system completing ET. 
    more » « less
  2. Atmospheric fronts embedded in extratropical cyclones are high‐impact weather phenomena, contributing significantly to mid‐latitude winter precipitation. The three vital characteristics of the atmospheric fronts, high wind speeds, abrupt change in wind direction, and rapid translation, force the induced surface waves to be misaligned with winds exclusively behind the cold fronts. The effects of the misaligned waves under atmospheric cold fronts on air‐sea fluxes remain undocumented. Using the multi‐year in situ near‐surface observations and direct covariance flux measurements from the Pioneer Array off the coast of New England, we find that the majority of the passing cold fronts generate misaligned waves behind the cold front. Once generated, the waves remain misaligned, on average, for about 8 hr. The parameterized effect of misaligned waves in a fully coupled model significantly increases the roughness length (185%), drag coefficient (19%), and air‐sea momentum flux (11%). The increased surface drag reduces the wind speeds in the surface layer. The upward turbulent heat flux is weakly decreased by the misaligned waves because of the decrease in temperature and humidity scaling parameters being greater than the increase in friction velocity. The misaligned wave effect is not accurately represented in a commonly used wave‐based bulk flux algorithm. Yet, considering this effect in the current formulation improves the overall accuracy of parameterized momentum flux estimates. The results imply that better representing a directional wind‐wave coupling in the bulk formula of the numerical models may help improve the air‐sea interaction simulations under the passing atmospheric fronts in the mid‐latitudes. 
    more » « less
  3. Abstract FrontFinder artificial intelligence (AI) is a novel machine learning algorithm trained to detect cold, warm, stationary, and occluded fronts and drylines. Fronts are associated with many high-impact weather events around the globe. Frontal analysis is still primarily done by human forecasters, often implementing their own rules and criteria for determining front positions. Such techniques result in multiple solutions by different forecasters when given identical sets of data. Numerous studies have attempted to automate frontal analysis through numerical frontal analysis. In recent years, machine learning algorithms have gained more popularity in meteorology due to their ability to learn complex relationships. Our algorithm was able to reproduce three-quarters of forecaster-drawn fronts over CONUS and NOAA’s unified surface analysis domain on independent testing datasets. We applied permutation studies, an explainable artificial intelligence method, to identify the importance of each variable for each front type. The permutation studies showed that the most “important” variables for detecting fronts are consistent with observed processes in the evolution of frontal boundaries. We applied the model to an extratropical cyclone over the central United States to see how the model handles the occlusion process, with results showing that the model can resolve the early stages of occluded fronts wrapping around cyclone centers. While our algorithm is not intended to replace human forecasters, the model can streamline operational workflows by providing efficient frontal boundary identification guidance. FrontFinder has been deployed operationally at NOAA’s Weather Prediction Center. Significance StatementFrontal boundaries drive many high-impact weather events worldwide. Identification and classification of frontal boundaries is necessary to anticipate changing weather conditions; however, frontal analysis is still mainly performed by human forecasters, leaving room for subjective interpretations during the frontal analysis process. We have introduced a novel machine learning method that identifies cold, warm, stationary, and occluded fronts and drylines without the need for high-end computational resources. This algorithm can be used as a tool to expedite the frontal analysis process by ingesting real-time data in operational environments. 
    more » « less
  4. Abstract One of the most costly effects of climate change will be its impact on extreme weather events, including tropical cyclones (TCs). Understanding these changes is of growing importance, and high resolution global climate models are providing potential for such studies, specifically for TCs. Beyond the difficulties associated with TC behavior in a warming climate, the extratropical transition (ET) of TCs into post-tropical cyclones (PTCs) creates another challenge when understanding these events and any potential future changes. PTCs can produce excessive rainfall despite losing their original tropical characteristics. The present study examines the representation of PTCs and their precipitation in three high resolution (25–50 km) climate models: CNRM, MRI, and HadGEM. All three of these models agree on a simulated decrease in TC and PTC events in the future warming scenario, yet they lack consistency in simulated regional patterns of these changes, which is further evident in regional changes in PTC-related precipitation. The models also struggle with their represented intensity evolution of storms during and after the ET process. Despite these limitations in simulating intensity and regional characteristics, the models all simulate a shift toward more frequent rain rates above 10 mm h−1in PTCs. These high rain rates become 4%–12% more likely in the warmer climate scenario, resulting in a 5%–12% increase in accumulated rainfall from these rates. 
    more » « less
  5. Abstract High-resolution observations have demonstrated the presence of strong time-mean near-surface wind convergence (NSWC) anchored across oceanic frontal zones, such as the western boundary currents. Initial analyses appeared to show a close association between this time-mean NSWC and time-mean properties of the underlying sea surface temperature (SST), such as the gradients and second derivatives (e.g., Laplacian of SST), acting through pressure-adjustment and vertical-mixing mechanisms. However, a series of recent papers have revealed the instantaneous NSWC to be dominated by atmospheric fronts and have suggested the importance of air–sea processes occurring instead on shorter, synoptic time scales. In this paper, using the ERA5 reanalysis dataset in the Gulf Stream region, we aim to reconcile these viewpoints by investigating the spatial and temporal dependence of NSWC and its relationship to SST. It is revealed that while atmospheric frontal processes govern the day-to-day variability of NSWC, the relatively weak but persistent pressure-adjustment and vertical-mixing mechanisms provide lower-frequency modulations in conditions both with and without atmospheric fronts. In addition to their temporal characteristics, each mechanism is shown through spectral analysis to dominate on specific spatial scales. In light of recent work that has tied remote atmospheric responses to NSWC anomalies in western boundary current regions, these results emphasize the importance of oceanic frontal zones for atmospheric variability on all spatiotemporal scales. 
    more » « less