Comparing the Spatial Patterns of Rainfall and Atmospheric Moisture among Tropical Cyclones Having a Track Similar to Hurricane Irene (2011)

Matyas, Corene J.

doi:10.3390/atmos8090165

Cited by 16 publications

(15 citation statements)

References 73 publications

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“…Nogueira and Keim (2011) avoided collecting extratropical precipitation by only including storms given a ''tropical'' rating in HURDAT. However, considering the extreme changes in TCP distribution that occur postlandfall (Atallah et al 2007;Matyas 2007Matyas , 2010, the larger 500-km search radius used by Nogueira andKeim (2010, 2011) to extract TCP could have included frontal and other nontropical precipitation. While our selection of a 223-km radius (Matyas 2010) could lead us to underestimate actual TCP, it increases the probability that the extracted precipitation arises primarily from TCs rather than non-tropical-cyclogenic sources.…”

Section: Resultsmentioning

confidence: 99%

“…To extract TCP from the precipitation dataset, we identified all grid points within a 223-km radius (Matyas 2010) from the interpolated TC position. Although the shape of the TC rain field changes after landfall and cannot be fully captured with a circle (Matyas 2007(Matyas , 2013Villarini et al 2011), we used the average size of a TC rain field (223 km; Matyas 2010) to construct a search radius, reducing the probability that nontropical precipitation is captured in TCPDat. Other approaches have been employed by examining TCP over multiple radii (e.g., Konrad et al 2002) as well as using a liberal search radius (e.g., 500 km; Nogueira and Keim 2011).…”

Section: Tropical Cyclone Precipitation Extractionmentioning

confidence: 99%

“…Numerous studies have examined individual storms or TCs over smaller spatial scales to understand the atmospheric circulation patterns and ambient conditions (e.g., sea surface temperatures) giving rise to anomalous amounts of TCP (e.g., Zhu and Zhang 2006;Konrad and Perry 2010;Chien and Kuo 2011;Hall et al 2013;Hernández Ayala and Matyas 2016;Matyas 2017;Trenberth et al 2018). Relatively few studies have examined TCP from the context of regional or global hydroclimates.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

et al. 2020

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Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States.

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Section: Resultsmentioning

confidence: 99%

Section: Tropical Cyclone Precipitation Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

et al. 2020

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“…In addition to examining additional TCs to determine how generalizable the trends discovered in this study are, future work will more closely examine the role of moisture in the evolution of TC rainfall regions. Matyas [56] and Takakura et al [57] discuss the importance of moisture advection for rain rates in TCs, and Matyas et al [19] show how dry air wrapped around the circulation of Hurricane Isabel (2003) during extratropical transition. We aim to trace the advection of dry air into the core of the storm and expand our search radius more than 1500 km to measure the extent of the deep tropical moisture that is advected into some TCs as they approach land.…”

Section: Discussionmentioning

confidence: 99%

Measuring Radial and Tangential Changes in Tropical Cyclone Rain Fields Using Metrics of Dispersion and Closure

Matyas

Tang

2019

Advances in Meteorology

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Although tropical cyclone (TC) rain fields assume varying spatial configurations, many studies only use areal coverage to compare TCs. To provide additional spatial information, this study calculates metrics of closure, or the tangential completeness of reflectivity regions surrounding the circulation center, and dispersion, or the spread of reflectivity outwards from the storm center. Two hurricanes that encountered different conditions after landfall are compared. Humberto (2007) experienced rapid intensification (RI), stronger vertical wind shear, and more moisture than Jeanne (2004), which was more intense, weakened gradually, and became extratropical. A GIS framework was used to convert radar reflectivity regions into polygons and measure their area, closure, and dispersion. Closure corresponded most closely to storm intensity, as the eye became exposed when both TCs weakened to tropical storm intensity. Dispersion increased by 10 km·hr−1 as both TCs developed precipitation along frontal boundaries. As closure tended to change earlier than dispersion and area, closure may be most sensitive to subtle changes in environmental conditions, particularly as the storm’s core experiences the entrainment of dry air and erodes. Displacement provided a combined radial and tangential component to the location of the rainfall regions to confirm placement along the frontal boundaries. Examining area alone cannot reveal these patterns. The spatial metrics reveal changes in TC structure, such as the lag between onset of RI and maximum closure, which should be generalizable to TCs experiencing similar conditions. Future work will calculate these metrics for additional TCs to quantify structural changes in response to their surrounding environment.

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“…Our analysis shows the dynamic relationship between TC strength and landing location in regulating precipitation throughout the YP's diverse regional climates [55,104]. Wilma (H4) was a "rainmaker" causing extraordinary rainfall in the northeastern YP associated to the largest radii of moderate (≥63 km/h) and strong (≥119 km/h) wind speeds (Figure 7); its slow motion (≤12 km/h) provided enough exposure and caused not only more vegetation loss and changes in the Chl-a distribution (see section below) but also heavy rainfall when compared to TC precipitable water, a metric of moisture content in the atmosphere [106,107]. Wilma's precipitable water ranged from 50-60 mm, which is larger than the average value considered to be a boundary favorable to TC rainfall production.…”

Section: Precipitation and River Discharge Spatiotemporal Patterns Induced By Tc Impactsmentioning

confidence: 99%

Tropical Cyclone Landfall Frequency and Large-Scale Environmental Impacts along Karstic Coastal Regions (Yucatan Peninsula, Mexico)

et al. 2020

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Tropical cyclones (TCs) are natural systems that develop over ocean basins and are key components of the atmospheric activity during the warm season. However, there are still knowledge gaps about the combined positive and negative TC impacts on the structure and function of coastal socio-ecosystems. Using remote sensing tools, we analyzed the frequency, trajectory, and intensity of 1894 TCs from 1851–2019 to identify vulnerable “hotspots” across the Yucatan Peninsula (YP), Mexico. A total of 151 events hit the YP, with 96% of landings on the eastern coast. We focused on three major hurricanes (Emily and Wilma, 2005; Dean, 2007) and one tropical storm (Stan, 2005) to determine the impacts on cumulative precipitation, vegetation change, and coastal phytoplankton (Chl-a) distribution across the YP. Despite a short inland incursion, Wilma’s environmental damage was coupled to strong winds (157–241 km/h), slow motion (4–9 km/h), and heavy precipitation (up to 770 mm). Because of an extensive footprint, Wilma caused more vegetation damage (29%) than Dean (20%), Emily (7%), and Stan (2%). All TCs caused a Chl-a increase associated to submarine discharge and upwelling off the peninsula coastlines. Disaster risk along the coast underscores negative economic impacts and positive ecological benefits at the regional scale.

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Comparing the Spatial Patterns of Rainfall and Atmospheric Moisture among Tropical Cyclones Having a Track Similar to Hurricane Irene (2011)

Cited by 16 publications

References 73 publications

Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

Measuring Radial and Tangential Changes in Tropical Cyclone Rain Fields Using Metrics of Dispersion and Closure

Tropical Cyclone Landfall Frequency and Large-Scale Environmental Impacts along Karstic Coastal Regions (Yucatan Peninsula, Mexico)

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