1. Introduction
Extreme precipitation resulting from any type of weather phenomenon can have significant impacts on populated areas, particularly when flooding ensues. While tropical cyclones (TCs) and post-tropical cyclones (PTCs) are comparatively rare causes of extreme precipitation events over land, they tend to be devastating for the affected areas due to the volume of precipitation, size of the storms, and short time frame in which precipitation falls (Schumacher and Johnson 2006). The challenges in analyzing extreme precipitation resulting from TCs and PTCs must be addressed in order to more accurately predict these events in present and future climates.
Prior studies have analyzed the characteristics of extreme precipitation events over the past several decades for localized regions such as the contiguous United States (CONUS) and for the globe, laying the groundwork for the assessment of future climate scenarios. Observations have indicated that extreme precipitation (90th percentile) events from all causes have become more frequent in North America between 1931 and 1996 (Kunkel et al. 1999), as have 5-, 10-, and 20-yr return period events between 1901 and 2012 (Janssen et al. 2014). Increases in the frequency of 20-yr return period precipitation events over the historical record have also been observed worldwide (Meehl et al. 2000). Heavy precipitation events have been attributed to various storm types to isolate changes over time in precipitation related to specific events such as TCs. In the central and eastern United States, Stevenson and Schumacher (2014) associated 7% of all 100-yr return period precipitation events between 2002 and 2011 with TCs. Kunkel et al. (2012) narrows the region of interest, attributing 51% of the Southeast’s extreme precipitation [defined in Kunkel et al. (2012), one-in-five-year event] and 36% of the Northeast’s extreme precipitation each year to TCs, highlighting the role that TCs play in coastal climates. Note that this variety of metrics used to define extreme precipitation hinders the creation of a cohesive global climatology of heavy rainfall events resulting from any given event type.
The severity of 90th percentile precipitation events is anticipated to be affected by the warming climate throughout the next several decades. Kunkel et al. (1999) calculated an 8% increase in daily accumulated precipitation in the United States and Canada since 1910, attributing most of this increase to 90th percentile events. In the future, TC-related precipitation is also expected to change with the warming climate, with the potential of the total TC precipitation increasing by 7% °C−1 of warming in the coming decades (Knutson et al. 2020). The spatial distribution of precipitation resulting from TCs will likely change as well. Utsumi et al. (2016) finds increasing TC precipitation in the subtropics coupled with decreasing TC rainfall in the tropics in the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. Using a climate model with a high-resolution domain over the North Atlantic and eastern United States, Stansfield et al. (2020b) projects higher precipitation amounts over land per hour of TC impact under future warming scenarios. Despite these advances in the study of extreme precipitation related to TCs, PTC-related precipitation has not yet been extensively studied because it can be difficult to isolate due to the asymmetries of the storms (Jones et al. 2003).
The process of the extratropical transition (ET) of TCs into PTCs poses unique challenges to the quantification of PTC precipitation. Approximately 42% of North Atlantic TCs complete ET, with approximately 51% of those ETs leading to a drop in central pressure after transition is complete (Hart and Evans 2001). During the ET process, the precipitation field typically undergoes dramatic spatial expansion (Konrad II et al. 2002), causing widespread impacts. As ET occurs, precipitation tends to shift left of track in the Northern Hemisphere (Jones et al. 2003; Liu et al. 2017), posing greater risks for the east coast of continents if a transitioning storm approaches land. Furthermore, the number of ETs in the North Atlantic has increased between 1970 and 2012 (Evans et al. 2017). Looking forward, Michaelis (2016) identifies possible effects of the warming climate on the ET of TCs using high-resolution global simulations; the study finds that there could be stronger interactions between TCs and upstream troughs, higher storm intensity at all points during ET, longer durations of the transition, and as much as a 30% increase in precipitation within transitioning storms in the North Atlantic basin (Michaelis 2016). Liu et al. (2018) also confirms a projected increase in ET events in a warmer climate, particularly for the eastern United States. The novel methodology proposed in the present study will contribute a more accurate assessment of observed PTC precipitation, making a precise quantification of future changes in PTC precipitation possible.
The analysis of TC precipitation in prior work has mostly consisted of the isolation of TC-related precipitation using a hard-coded radius or latitude–longitude-based boxes around a storm trajectory point and extracting all precipitation above a set threshold (Skok et al. 2009, 2013; Touma et al. 2018; Dhakal 2019) or above hard-coded thresholds that vary within the set radius (Skok et al. 2013). Stansfield et al. (2020a) expanded on this work by using the radial profile of near-surface wind to determine storm size, estimate a dynamic radius throughout the storm’s life, and extract all precipitation for analysis within the 8 m s−1 wind radius, a measure of outer storm size (Schenkel et al. 2017). However, the decrease in the near-surface winds after a TC makes landfall and the onset of interactions between the TC and the baroclinic environment can create discrepancies in a precipitation field extracted using near-surface winds or hard-coded thresholds. Furthermore, factors such as wind shear and storm motion can cause dramatic asymmetry in a transitioning TC’s precipitation shield (Lonfat et al. 2004; Chen et al. 2006; Lonfat et al. 2007), leaving some precipitation unaccounted for when ET-related precipitation is extracted using a set radius.
This study builds on the ideas of Stansfield et al. (2020a) in an effort to more accurately isolate TC and PTC precipitation during all phases, specifically the post-tropical phase, of the TC life cycle. During and after the ET process, the increasing asymmetry of the storm can cause precipitation to extend beyond the traditional 500-km radius from the TC center (Villarini et al. 2011). To mitigate this discrepancy during the ET process, the present study makes use of the 500-hPa geopotential height field to define the search radius for the association of precipitation with a storm; this allows for the expansion of the storm precipitation radius during processes that produce asymmetries and discontinuities in TC precipitation, as well as accounting for the symmetric rainfall during the TC phase of the storm’s life. In addition to using observational precipitation datasets that are commonly used for the construction of global climatologies, this study also uses the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) observational precipitation dataset to be collocated with observational TC trajectories. The use of this high-resolution data provides unique advantages, including a more detailed representation of extreme precipitation, particularly in smaller-scale features such as TCs (Rios Gaona et al. 2018). The goal of this study is to introduce a novel methodology for the isolation of PTC precipitation and to accurately quantify the role of tropical and post-tropical phases of the TC life cycle and their respective contributions to global TC- and PTC-related extreme precipitation.
A description of the data and methods of analysis are included in section 2. Section 3 describes results found using the methodology for analyzing TCs and PTCs, beginning with climatologies and variations in TC and PTC extreme precipitation due to intensity differences and basin-dependent variability. The ability of the algorithm described in section 2 to analyze trends and interannual variability in TC and PTC extreme precipitation is discussed at the end of section 3, and section 4 includes discussion and conclusions drawn from section 3.
2. Data and methods
a. Observational datasets
This study creates an observed global climatology of heavy precipitation related to TCs and PTCs to demonstrate and evaluate a novel methodology for isolating specifically PTC precipitation. An initial comparison of three observational precipitation datasets of varying spatial resolutions is performed first as a proxy for the certainty of the results found using this novel methodology. All three datasets include satellite measurements and varying degrees of calibration with ground-based rain gauges and radar data. Although each precipitation dataset spans a different range of time, the common years of 2001–19 are used here to create a consistent comparison across the datasets.
1) IMERG
The IMERG Final Run v06 product offers the highest spatial and temporal resolution among the precipitation datasets used in this study. The GPM/IMERG mission uses passive microwave radiometers calibrated with the GPM Dual-Frequency Precipitation Radar and intercalibrated with the Combined Radar–Radiometer Algorithm (CORRA) product to construct 0.1° × 0.1° gridded global precipitation data estimates for every half hour. The intercalibrated CORRA product is climatologically calibrated to monthly Global Precipitation Climate Project (GPCP) satellite–gauge estimates as well, particularly in areas where biases are known to exist such as high-latitude oceans (Huffman et al. 2019). Once the initial calibration is complete, the data are processed once more using two morphing algorithms: the Kalman filter–based Climate Prediction Center morphing technique (CMORPH-KF) and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) recalibration schemes. Finally, the half-hourly estimates for the post-real-time final product are produced by the CMORPH-KF “forward–backward” morphing algorithm using radar and infrared estimates. In this study, the half-hourly data are downsampled to daily data using an averaging method to match the temporal resolution of the other datasets used.
2) PERSIANN
The Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), version 1.1 (Sorooshian et al. 2000) is the second product used for this study. PERSIANN-CDR uses a combination of geosynchronous and low-Earth-orbiting satellites to construct a 0.25° × 0.25° gridded, daily precipitation dataset covering from 60°N to 60°S latitude at all longitudes. Precipitation estimates are calibrated with GPCP 2.5° monthly precipitation totals that include land-based rain gauge data assimilation.
3) GPCP
The Global Precipitation Climatology Project (GPCP) dataset, version 1.3 provides 1° × 1° global daily data collected from various geosynchronous infrared satellites between 40°N and 40°S. The precipitation estimates for the higher latitudes are filled in with additional data from polar-orbiting satellites (Huffman et al. 2001).
4) IBTrACS
The International Best Track Archive for Climate Stewardship (IBTrACS) contains observations of tropical cyclones worldwide from 1851 to the present with a 3–4-day lag. Consistent with the precipitation datasets, data from 2001 through 2019 is used. For this work we make use of IBTrACS TC latitude and longitude positions, surface wind magnitude, and sea level pressure minima (Knapp et al. 2010). PTC locations and intensity data are included in IBTrACS in most areas during the time frame used, but trajectories in some cases do not include the entire storm life cycle through dissipation of the post-tropical low.
b. Reanalysis
ERA5 reanalysis data are used in this study for the tracking of extratropical transitioning TCs. The ERA5 dataset contains global gridded data at a horizontal grid spacing of 31 km (roughly 0.25° × 0.25°) and 1-hourly temporal resolution with atmospheric variables available on 37 vertical levels interpolated from 137 hybrid sigma-pressure coordinates (Hersbach et al. 2020). The variables used in this study from ERA5 include sea level pressure, 10-m wind vectors, 850-hPa wind vectors, and geopotential at several vertical pressure levels ranging from 1000 to 300 hPa, all downsampled to 6-hourly temporal resolution using instantaneous values. While it is possible to use the analysis tools described in the following section to track TCs in reanalysis data such as the ERA5 dataset, IBTrACS TC positions and wind speeds will be used for this study. ERA5 data are still used by ExTraTrack [section 2c(2)] to identify storms that complete ET.
c. Analysis tools
1) TempestExtremes
TempestExtremes version 1.1 (updated 8 August 2020; Ullrich et al. 2021; Ullrich and Zarzycki 2017; Zarzycki and Ullrich 2017) is used for various purposes within the analysis below. This software package is publicly available and uses commonly available parameters to identify and track various meteorological features point by point.
2) ExTraTrack
ExTraTrack (Zarzycki et al. 2017) is a software package that augments TempestExtremes with functionality for tracking TCs during and after ET, therefore including the PTC phase of the storm life cycle. It uses TC trajectories in combination with sea level pressure, 10-m wind vectors, and geopotential in the full vertical column to identify if ET occurs and if so, what type of transition took place. In this study, ERA5 reanalysis data are paired with IBTrACS trajectories to complete ExTraTrack analysis. ET events are defined using a cyclone phase space calculation as in Hart (2003). After extending trajectories where needed, the program also performs some postprocessing, which includes a climatology of the ET events, differentiation of the types of ETs that occur, and duration of the transition.
d. Methods
The process of isolating heavy TC and PTC precipitation uses the TempestExtremes software package; the inclusion of PTC phases of a storm’s life that are not included in IBTrACS trajectories also requires the use of the ExTraTrack software package. This process is illustrated in Fig. 1. All commands and settings are found in the appendix.
Isolation of TC-related extreme precipitation as shown for Hurricane Irene for (left) 26 Aug 2011 and (right) 29 Aug 2011. (a),(b) All precipitation occurring at the selected times. (c),(d) Only the 95th percentile precipitation. (e),(f) The Z500 mask that is used to extract heavy precipitation related to the active TCs, created by summing the 6-hourly positions of the mask throughout the day matching the observed precipitation. On 26 Aug 2011 when Hurricane Irene is comparatively strong and well organized, the mask that generates the Z500 mask for precipitation extraction is very small. This accounts for the organization of precipitation close to the center of strong TCs. However, as the storm weakens, the Z500 mask expands as the precipitation field does. Note: Tropical Storm Jose was active near 20°N, 60°W on 26 Aug 2011 and near Bermuda on 29 Aug 2011, creating the second mask seen in (e) and (f). (g),(h) The final result of the processing, leaving only the extreme precipitation objects that at least partially overlap the TC or PTC Z500 mask. Note: Tropical Storm Jose rainfall is also seen over the open Atlantic in the locations noted.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
First, TC trajectories must be obtained. This study uses IBTrACS TC trajectories, which are then extended to include post-transition points using ExTraTrack. ExTraTrack is used here to create a standardized record of ET events and the trajectories of the subsequent PTCs using the cyclone phase space as defined in Hart (2003). Some observational trajectories already include ET, particularly in the North Atlantic basin, but some points in the post-tropical trajectory may still be added by ExTraTrack until the pressure of the system rises above 1020 hPa or 14 days after ET occurs, indicating the full dissipation of the storm. For this study, all data in areas with surface elevation exceeding 1000 m were removed to exclude topographic lows.
Once the TC and PTC trajectories have been obtained, all daily precipitation totals exceeding the 95th percentile of all daily totals, regardless of the presence or absence of precipitation, are isolated at each grid point (Pendergrass 2018). While there are many widely accepted metrics for extreme precipitation (Jagannathan et al. 2021), the 95th percentile threshold used in this study enables the isolation of some of the heaviest precipitation events each year while providing a great enough sample size for the construction of a climatology. By tracking all 95th percentile precipitation events, this methodology also allows for the attribution of the most intense 18–19 events per year to various causes, particularly TCs and PTCs. The methodology is adaptable to accommodate any threshold desired. Figures 1a and 1b show all precipitation as recorded by IMERG, with Figs. 1c and 1d showing only that which exceeds the 95th percentile threshold.
After defining the 95th percentile threshold, these precipitation events are tracked using TempestExtremes. The DetectBlobs function is used to identify all points in space and time at which a 95th percentile precipitation event is occurring. This contiguous cluster of grid points where an event is occurring is referred to as a “blob.” The StitchBlobs function is subsequently used to “stitch” the events in time and assign a unique identification to each precipitation object as it moves through space and time. The output from this function is a binary file, with “1”s indicating that a 95th percentile precipitation event is occurring and “0”s indicating that no event is occurring. Thus, precipitation amounts are not needed for the analysis of event number or seasonality given that the binary file can be used to determine time and location of events. The search area and time frame are adjustable for these two TempestExtremes functions. For this study, the default settings were used; no artificial limitations on the areal size of a precipitation blob, overlap between a blob from a previous time frame, or distance between separate blobs are imposed. The areas of light rain or no rain that separate heavy precipitation areas from one another act as natural limits for the size of precipitation blobs, as they are not tracked under these conditions.
Following precipitation processing, the radius used to collocate heavy precipitation with active TCs and PTCs must be defined. This radius will allow for the capture of precipitation objects that overlap the radius at any point while allowing the objects to retain irregular shapes and extend beyond the mask wherever necessary, i.e., in a transitioning, asymmetric cyclone. Using TempestExtremes, the 500-hPa geopotential height value (Z500) is used at each time in the storm trajectory. The radius for TC and PTC precipitation extraction is defined as the point at which the 500-hPa geopotential height changes by 10 m from the value observed at the cyclone center. A constant 1° great circle distance radius is overlaid on this dynamical mask as a minimum mask to ensure that strong storms, which at times can have a 10-m change occurring within a single grid point of the TC center depending on the horizontal resolution of the dataset, are still included in the analysis. If the 10-m change in geopotential height does not occur within the 5° great circle distance search radius, the search radius is used as a constant radius for TC precipitation extraction. Typically, this occurs in weak, disorganized systems. The selection of 500 hPa was made to allow for the Z500 mask to change throughout the life cycle of the storm, remaining small when a storm is intense and well organized, but expanding a storm undergoes ET or weakens, as shown in Figs. 1e and 1f at two points in Hurricane Irene’s life cycle. This methodology enables the analysis of both symmetric and asymmetric storms.
Finally, the Z500 mask variable is overlaid on the 95th percentile precipitation field using TempestExtremes, isolating only the TC- and PTC-related extreme precipitation. All precipitation objects that overlap the Z500 mask at any point (even partially overlapping) are included, capturing irregularly shaped precipitation objects that can extend beyond the Z500 mask wherever necessary, as seen in Figs. 1g and 1h. An advantage of the 95th percentile threshold is the natural limitation of precipitation object size. Only contiguous heavy rainfall events are tracked, creating space between different precipitation features and neglecting those that have not merged with a TC or PTC. In the present study, daily precipitation accumulations are used; to match this temporal resolution, the 6-hourly Z500 mask is summed over all time steps of each day before being collocated with the corresponding precipitation. This final result, showing only heavy TC- and PTC-related precipitation, is shown in Figs. 1g and 1h. Once this final step has been completed, further analysis is performed, including the creation of the climatology and seasonality of 95th percentile precipitation events regardless of event type, contribution of TCs and PTCs to the total and 95th percentile precipitation, analysis of TC and PTC precipitation with respect to the intensity or location of the storm, and TC and PTC precipitation changes with the El Niño–Southern Oscillation (ENSO) modes.
3. Results
First, a climatology of global heavy TC- and PTC-related precipitation is constructed for each of the three observational precipitation datasets. Areas with fewer than three TC- or PTC-related heavy rainfall events throughout the 19-yr time frame are excluded so only regions that climatologically experience TC- and PTC-related extreme precipitation are included. Additionally, all areas poleward of 60° are neglected due to the volume of missing data at the higher latitudes in all of the datasets used. While the study by Skok et al. (2013) examined global mean TC precipitation in the Tropical Rainfall Measuring Mission (TRMM) dataset, it did not isolate particularly heavy rainfall related to storms and did not address post-tropical stages of storms’ life cycles. Additionally, Jiang and Zisper (2010) analyzes global seasonal TC precipitation using a hard-coded 500-km radius to create monthly averages of higher temporal frequency data, but does not include PTC precipitation, off-season storms, or impacts poleward of 30°N/S. To the authors’ knowledge, the present study is the first construction of a global climatology of combined TC- and PTC-related heavy rainfall with a breakdown by storm phase with the IMERG dataset.
The general spatial distribution of TC- and PTC-related heavy rainfall is consistent among the three datasets (Fig. 2), with maxima in the west North Pacific and east North Pacific. These patterns match the general distribution observed in the monthly TC precipitation averages and accumulated cyclone energy in Jiang and Zisper (2010), with the addition of data poleward of 30°N/S. Annual TC and PTC precipitation varies among the datasets from 0.5 m yr−1 in the west North Pacific according to the GPCP dataset (Fig. 2c) to over 1 m yr−1 in the same region in the IMERG dataset (Fig. 2a). Similarly, IMERG estimates greater TC and PTC precipitation in the Caribbean than PERSIANN and GPCP. Due to the ability of the IMERG data to capture details such as common storm tracks (Fig. 2a) and the benefits of the radar calibration techniques used to create the dataset (Huffman et al. 2020), we focus on IMERG for the remainder of this study. Results from the GPCP and PERSIANN datasets are in the supplemental material given the notable uncertainty among the representation of heavy precipitation in various observational products (Bador et al. 2020; Alexander et al. 2020).
Average annual TC- and PTC-related extreme precipitation in meters, 2001–19 in the (a) IMERG, (b) PERSIANN, and (c) GPCP datasets. TC- and PTC-related heavy rainfall is extracted using a dynamic radius based on the change in 500-hPa geopotential height from the center of the storm, which is identified using IBTrACS TC trajectories, as extended using ExTraTrack.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
The average annual number of TC and PTC heavy precipitation events mirrors the spatial distribution of the average TC heavy precipitation shown in Fig. 2, indicating the relationship between event number and precipitation. More than a dozen events occur on average every year in the northwest Pacific in some locations, while locations in the south Indian basin average only 5–7 events each year (Fig. 3a). Though the PERSIANN dataset shows similar counts (Fig. S2a in the online supplemental material), GPCP indicates more events due to its coarse resolution (Fig. S1a). Many locations within the North Atlantic tend to have 4 or 5 events each year in all datasets.
Annual count and seasonality of TC- and PTC-related extreme precipitation events in the IMERG dataset for 2001–19. (a) The average annual number of events, (b) the average day of year in which a TC or PTC heavy rainfall event occurs, and (c) the percentage of all 95th percentile precipitation events that result from TCs and PTCs.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
We now examine the seasonality of these events since the seasonality of intense precipitation events of all types is ambiguous in some observational datasets (Bador et al. 2020), making a climatology of the seasonality of specific events helpful in determining their contribution to the overall seasonality of extreme rainfall. Figure 3b shows the average day of year of TC- and PTC-related heavy rainfall events determined using a circular averaging technique. Events tend to occur later in the season at the higher latitudes and in the easternmost areas of basins, with the exception of the bimodal TC activity in the north Indian basin. The seasonal shift of TC track locations within basins is also evident. In the Caribbean Sea, the average day of year of a TC extreme precipitation event is in August, slightly before the peak of the Atlantic season accommodating early season activity near the Gulf of Mexico (Fig. 3b; Kimball and Mulekar 2004). In the main development region of the North Atlantic (approximately 10°–20°N and 80°–20°W), TC precipitation events are most likely to occur in September, reflecting the nature of Cape Verde hurricanes characteristic of the peak of the Atlantic season (Kimball and Mulekar 2004). Similar patterns emerge in the west North Pacific, accounting for the peak of the season in August and development of coastal TCs earlier in the season (Fig. 3b). Both GPCP and PERSIANN show comparable climatologies (Figs. S1b, S2b).
Finally, the contribution of TC- and PTC-related extreme precipitation to the total annual 95th percentile precipitation events is quantified (Fig. 3c). Given the 95th percentile threshold for precipitation analysis, this percentage is calculated with simple division. However, other thresholds (e.g., a 10 mm day−1 threshold within the dynamical radius) may be implemented within this framework, yielding a different measure of the role of TCs and PTCs. On the East Coast of the United States, 10%–20% of all 95th percentile precipitation events each year result from TCs and PTCs, consistent with the findings of Kunkel et al. (2012), which state that 13% of the eastern U.S. 95th percentile precipitation results from TCs. Less than 10% of the 95th percentile precipitation in the Indian subcontinent results from TCs, with similar values over the central Pacific. In the western North Pacific, upward of 50% of the annual 95th percentile events are attributed to TC or PTC events. These values are comparable to Wu et al. (2007), which finds that TCs contribute 60% of the extreme daily precipitation in the South China Sea. Once again, PERSIANN indicates similar percentages to IMERG (Fig. S2c), while GPCP tends to estimate larger percentages on the outer edges of areas with high TC and PTC activity (Fig. S1c).
a. TC intensity breakdown
This methodology can isolate intensity and post-tropical stages within the TC life cycle. Analysis of the intensity phases of tropical cyclones is divided into tropical storms (winds less than 33 m s−1), category 1 and 2 (weak) hurricanes (winds between 33 and 49 m s−1) and category 3 and higher (major) hurricanes (winds greater than 49 m s−1). Analysis of tropical and post-tropical phases of the TC life cycle is completed using a combination of the observed PTC trajectories (both the ET and MX labels in IBTrACS) with those calculated by ExTraTrack for the years 2001–18.
TC rainfall rates and duration increase with increasing intensity (Alvey et al. 2015; Jiang and Zisper 2010), implying that despite being less common, more intense storms produce higher rainfall totals per storm than weaker TCs (Shepherd et al. 2007). Climatologies are constructed based on the intensity of the TC at each point in the storm’s life cycle. The average precipitation resulting from each group of storms reflects the spatial distribution of the storms at each intensity (Fig. 4). Stronger storms achieve their highest intensities at low latitudes, while lower intensity storms exist at higher latitudes and farther zonal extents in each basin. Major hurricanes in all basins typically remain equatorward of 30°. Weaker hurricanes (category 1 and 2 based on wind speeds) reach 40° in all basins, with tracks extending farther north in the Northern Hemisphere. Tropical storms reach all latitudes between 60°N and 60°S globally, confirming environmental controls on TC development and maintenance.
Average annual 95th percentile precipitation resulting from (a) tropical storms, (b) category 1 and 2 hurricanes, and (c) major hurricanes according to the IMERG dataset for the years 2001–18.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
The rainfall resulting from each intensity phase (Fig. 4) reflects the rare nature of the major hurricane phase of storms’ life cycles and the common occurrence of tropical storm–related events. While storms at tropical storm intensity contribute as much as 0.8 m of precipitation each year in the west North Pacific (Fig. 4a), storms in the major hurricane stage contribute 0.2–0.3 m of precipitation each year on average (Fig. 4c). Major hurricanes tend to have higher per-storm precipitation totals than weaker storms but tend to be rare (see Fig. 5c compared to Fig. 3a) and spend a short time at high intensities (Kimball and Mulekar 2004), making the impacts of the events disproportionate. The prevalence of tropical storm strength systems’ rainfall is consistent with the results of Shepherd et al. (2007), which indicate that this intensity breakdown is observed off the southeast coast of the United States. However, Jiang and Zisper (2010) finds that hurricane-strength storms contribute more precipitation than tropical storms. In the development regions of all basins, tropical storms contribute the greatest total 95th percentile TC precipitation each year. Figure 5a indicates that tropical storm precipitation arises from the high number of events each year. As many as 9 tropical storm-related events occur in the west and east Pacific, but only 1–2 major hurricane events per location each year occur in the region. The North Atlantic experiences 3–4 tropical storm events (Fig. 5a), 2–3 weak hurricane events (Fig. 5b), and 0–2 major hurricane events (Fig. 5c) each year depending on the location. The Caribbean and southern Gulf of Mexico have more major hurricane events than the rest of the North Atlantic (Landsea 1993). While the west and east Pacific show clear differences in the frequency of different intensities of storms, these differences are less evident in the North Atlantic and south Indian Oceans.
Average annual number of heavy precipitation events resulting from (a) tropical storms, (b) category 1 and 2 hurricanes, and (c) major hurricanes in the IMERG dataset for the years 2001–18.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
Strong patterns in the timing of events of various instantaneous intensities are observed (Fig. 6). While tropical storm events tend to span the entire TC season of each basin, major hurricane precipitation events typically occur during the peak of the basin’s TC season (Figs. 6a,c). In the North Atlantic basin, tropical storm precipitation events occur on average in July and August in the westernmost part of the basin (Fig. 6a). Late-season tropical storm-related events typically occur in the far eastern Atlantic, while events in the main development region occur during the late August–early September peak of the season. Category 1 and 2 storm precipitation events exhibit a similar pattern to tropical storm events in the eastern North Atlantic and a shift toward late-season events in the western Atlantic, but major hurricane events exhibit different seasonality patterns. Most major hurricane-related events in the North Atlantic occur in the peak of the season, with the exception of late season events near the coast of Central America (Fig. 6c); these results remain consistent with Landsea (1993).
Average day of year of heavy precipitation events resulting from (a) tropical storms, (b) category 1 and 2 hurricanes, and (c) major hurricanes in the IMERG dataset for the years 2001–18.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
Other basins exhibit seasonality shifts among different intensity stages. The north Indian basin, known for its bimodal distribution of TC activity (Balaji et al. 2018), tends to have tropical storm precipitation events on the west coast of the subcontinent in June, while the southern coast of India experiences these events in November (Fig. 6a). The Arabian Sea has category 1 and 2 hurricane events in July or October, but the Bay of Bengal has similar events in November (Fig. 6b). Seasonality shifts in the Southern Hemisphere and in the Pacific Ocean are similar to the patterns examined in the North Atlantic, with events attributed to strong storms occurring during the height of the season and those attributed to weak storms occurring throughout the season.
b. Post-tropical cyclone precipitation
The present study also creates an objective climatology of global PTC events that have undergone ET and their related precipitation for the years 2001 through 2019. Throughout the time frame of interest, 33.2% of all TCs completed ET during their life cycle. Not only do the number of TCs and ETs favor the Northern Hemisphere, but the percentage of storms that complete ET does as well, with 36.1% of Northern Hemisphere storms completing ET and only 24.8% of Southern Hemisphere storms transitioning. More than half of the ET events that occur follow the asymmetric warm core transition pathway (56.6%). These results compare to Zarzycki et al. (2017), which finds that 69.4% of North Atlantic ET events follow this same ET pathway, with the other two pathways being less common methods of transition. Basin-dependent biases such as those observed in ET completion and pathway are known to exist (Hart and Evans 2001; Zarzycki et al. 2017; Evans et al. 2017; Klein et al. 2000; Sinclair 2002).
PTCs tend to occur in the western and poleward sides of the North Atlantic and west Pacific Oceans, as shown in Fig. 7. These storms can contribute significant wind and precipitation in downstream areas (Jones et al. 2003), making the isolation of PTC precipitation useful for understanding risks in areas far removed from the traditional TC basins. While some storms that transition near the mountainous regions of Mexico and the Himalayas are likely missing from this climatology due to the topography masking employed in this study, the spatial coverage of storms captures the majority of ET cases globally (Bieli et al. 2019). The precipitation resulting from PTCs exceeds 0.35 m in the western North Pacific near Japan and along the northern coast of Australia (Fig. 7a). On the contrary, GPCP and PERSIANN do not record annual average PTC precipitation totals greater than 0.35 m (Figs. S3a, S4a). Most of the North Atlantic basin receives at least 0.1 m of precipitation from PTCs each year in the IMERG dataset (Fig. 7a), with the south Indian Ocean exhibiting similar precipitation totals. Areas downstream of the North Atlantic and west North Pacific receive at least 0.05 m of PTC-related precipitation per year, specifically in parts of western Europe, southern Alaska, and the west coast of Canada.
Average (a) annual heavy precipitation, (b) number of events, (c) day of year, and (d) percentage of all 95th percentile precipitation events resulting from PTCs in the IMERG dataset for the years 2001–19.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
While PTC-related precipitation events are less common than TC-related events (refer to Fig. 3a), many of the areas in the ocean basins that typically experience TCs also experience at least one PTC precipitation event per year. These events tend to be spread out in the North Atlantic with a maximum of 1.5 events per year along the East Coast (Fig. 7b). The events in the west Pacific and south Indian Oceans tend to be more localized within the basin. Approximately 3 events per year occur in the south Indian Ocean east of Madagascar. The west North Pacific tends to have the most PTC precipitation events, with parts of Japan and the surrounding areas having 7 or more events per year (Fig. 7b). GPCP does not record as many events, with the western North Pacific only having approximately 5 events per year (Fig. S3b). The PERSIANN dataset similarly has a lower the number of events at each location with biases of only 1 event per year rather than 2 (Fig. S4b). These minor discrepancies indicate that despite resolution differences among the datasets, there is a consistent pattern of PTC related precipitation observed.
Finally, ET and PTCs tend to be fringe-season phenomena, occurring either very early or very late in the basin’s season. In the Northern Hemisphere, PTC precipitation events are most likely to occur in October and November in the easternmost parts of the basins (Fig. 7c). The western parts of the basins tend to have ET events occurring in June or in the August and September peak of the season due to the long-lived TCs that tend to occur at those times. In the Southern Hemisphere, most areas tend to experience PTC precipitation events in February and March, toward the end of their respective TC seasons (Fig. 7c). Despite discrepancies in precipitation amounts and number of events, GPCP and PERSIANN show similar seasonality patterns to the IMERG dataset (Figs. S3c, S4c), indicating a consistent representation of the timing of such precipitation events in all three observational products.
c. Other analysis tools
1) Trends
In addition to the climatology construction enabled by these methods, this analysis framework has the capability to quantify trends over the time frame of the dataset being studied. However, the time frame used in this particular study includes only 19 years of observational data, making trends indistinguishable from natural variability. Aside from computing trends over time, this framework is also able to isolate the first and second halves of the time frame and compare the evolution of TC and PTC precipitation in this manner. Figure 8 illustrates an example of this feature, showing the zonal average of TC and PTC heavy rainfall totals and number of days with events for the first and second halves of the time frame in all three observational datasets. This capability enables the comparison of the behavior of the datasets for these types of events. For example, GPCP includes more TC and PTC days per unit area than IMERG and PERSIANN (Fig. 8b), indicating that although the same storms have been tracked, their respective precipitation fields appear larger due to the lower spatial resolution. The heavy precipitation totals exhibit the greatest differences among datasets. When comparing the first half and second half of the time frame, this methodology indicates that more TC and PTC heavy rainfall occurred in the deep tropics during the first decade studied according to IMERG and GPCP, with PERSIANN indicating a more similar total in both halves of the time frame. This is a helpful way to visualize the TC and PTC precipitation observed both in various datasets and in different time frames.
Zonal average annual TC- and PTC-related (a) heavy precipitation and (b) events per unit area in all three observational precipitation datasets for the years 2001–19. The dotted lines indicate the IMERG data, the solid lines correspond to GPCP, and the dashed lines correspond to PERSIANN data. Red lines are used for the second half of the time frame while blue lines indicate the first half of the time frame.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
2) Interannual variability
Finally, TC and PTC activity is affected by various long-term cycles (Chan and Shi 2000). While the time frame analyzed in this study is short, the methods also enable the analysis of interannual cycles and their effects on TC and PTC precipitation in longer time frames. Figure 9 indicates that more precipitation than average is observed in the North Atlantic during the La Niña years of the studied time frame, which is characteristic of the increase in TC activity in the basin during such years (Klotzbach 2011). A deficit of as much as 0.18 m of TC extreme precipitation is observed in the western North Pacific, consistent with Camargo and Sobel (2005) and Kubota and Wang (2009). Consistent with the observations made in Nicholls (1979), there is an increase in TC precipitation during La Niña years in the South Pacific. These results confirm previous work (Rodgers et al. 2000, 2001; Nicholls 1979; Camargo and Sobel 2005; Jiang and Zisper 2010), indicating the usefulness of these tools when longer time frames are available for evaluation.
Anomaly of TC-related heavy precipitation during La Niña years (2005, 2007, 2008, 2010, 2011, 2016, and 2017) compared to the full time frame of 2001–19 in the IMERG dataset.
Citation: Journal of Hydrometeorology 23, 10; 10.1175/JHM-D-21-0153.1
4. Conclusions
This study presents a novel methodology for the analysis of heavy TC- and PTC-related precipitation, relying on dynamic definitions of TC size rather than hard coded radii for the more accurate isolation of PTC-related precipitation. The use of the 500-hPa geopotential height to create a varying radius over which to extract heavy TC- and PTC-related rainfall allows for the quantification of such variables throughout the entire life cycle of the TC, including post-ET. This radius decreases when storms are compact and organized, but tends to expand during ET and weakening processes, allowing for the capture of more asymmetric and discontinuous precipitation fields (Konrad et al. 2002), whereas a hard coded radius could underestimate precipitation in asymmetric or transitioning storms (Villarini et al. 2011).
To evaluate the proposed novel methodology, three observational precipitation datasets are analyzed. GPCP, PERSIANN, and IMERG all show similar patterns in heavy TC and PTC rainfall while magnitude of precipitation totals varies by as much as 0.4 m due to the varying spatial resolution and calibration techniques of the products. All products confirm the seasonality and spatial distribution of TC precipitation noted in prior studies (Jiang and Zisper 2010; Skok et al. 2013; Kimball and Mulekar 2004; Shepherd et al. 2007; Rodgers et al. 2000, 2001), providing confidence in the novel PTC heavy precipitation results. The IMERG observational precipitation dataset is the focus of this study for its more precise detail in the TC and PTC heavy precipitation distribution. We find that as much as 1 m of TC- and PTC-related heavy precipitation falls in the west North Pacific each year. Portions of the east North Pacific receive approximately 0.6 m of such precipitation, and the rest of the world tends to receive less than 0.5 m of TC and PTC precipitation per year. PTC-related heavy rainfall alone accounts for as much as 40% of all heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% (3.64%) of all heavy precipitation globally (between 60°S and 60°N).
Regardless of tropical or post-tropical phase, the relative contribution to total TC and PTC heavy rainfall of storms is dependent on their instantaneous intensities, consistent with Shepherd et al. (2007). The tropical storm–strength stage tends to be most common and contributes an areal average 85.7% of the heavy TC- and PTC-related rainfall for any given location. The category 1 and 2 stages contribute only 11.7% when averaged spatially over regions that experience such events. Storms in the major hurricane stage contribute the least amount of TC- and PTC-related heavy precipitation at only 2.6%.
This analysis framework will be adapted to examine the role of PTC rainfall in various datasets. A longer record of reliable satellite-based observational data becomes available over time, which will allow for the study of rare events with long return periods using this approach. Additionally, higher resolution climate simulations have become available in recent years. This methodology can be utilized to more accurately isolate PTC precipitation in these climate simulations, granting new perspectives on possible rainfall changes in warmer climate scenarios. Another application of this algorithm allows for comparisons among operational forecast model runs. In operational forecast models, differing track scenarios can be analyzed to interpret the effects of track error on precipitation forecasts. This methodology can also be utilized to quantify the performance of precipitation forecasts by various models by verifying model forecasts using observations. When examining specific storm cases, the framework can be adapted to include composite analysis for the examination of storm characteristics. The use of approaches like the one utilized in this study can prove useful for the prediction and analysis of heavy precipitation events worldwide.
Acknowledgments.
Bower, Reed, and Pendergrass acknowledge the funding support of NASA under Grant 80NSSC19K0717. Reed, Ullrich, and Zarzycki also acknowledge the funding support of the Department of Energy Office of Science award number DE-SC0016605, “A Framework for Improving Analysis and Modeling of Earth System and Intersectoral Dynamics at Regional Scales (HyperFACETS),” and Pendergrass acknowledges DE-SC0022070 and National Science Foundation (NSF) IA 1947282. Pendergrass was also supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Reed was also supported in part through NSF Grant AGS1648629. Finally, the authors would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation.
Data availability statement.
GPCP daily precipitation data provided by NOAA/NCDC from their website at https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/. PERSIANN daily precipitation data provided by NOAA/NCDC from their website at https://www.ncei.noaa.gov/data/precipitation-persiann/access/. IMERG Final Run 30-min precipitation data provided by NASA from their website at https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGHH.06/. IBTrACS v4 TC data provided by NOAA/NCEI from their website at https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access. ERA5 reanalysis data are available at https://cds.climate.copernicus.eu/#!/search?text=ERA5 type=dataset. TempestExtremes is publicly available on GitHub at https://github.com/ClimateGlobalChange/tempestextremes. ExTraTrack is also available at https://github.com/zarzycki/ExTraTrack.
APPENDIX
TempestExtremes Command Lines
The TempestExtremes command lines for the tracking of TCs and isolation of TC-related extreme precipitation are provided below.
Tracking TCS
./DetectNodes --in_data_list “$infiles” --timestride 1
--verbosity 0 --out $outfile_DN
--closedcontourcmd “PSL,200.0,5.5,0; _DIFF(Z300,Z500),-6.0,6.5,1.0”
--mergedist 6.0 --searchbymin PSL
--outputcmd “PSL,min,0;_VECMAG(U850,V850), max,2;PHIS,max,0”
./StitchNodes --format “i,j,lon,lat,slp,wind, phis”
--range 8.0 --minlength 10 --maxgap 3
--in $outfile_DN --out $outfile_SN
--threshold “wind,>=,10,10;lat,<=,50,10;lat,>=, -50,10;phis,<=,150,10”
Tracking extreme precipitation events
Requires that a 95th percentile threshold latitude × longitude grid be in a separate file or included in the precipitation file as a separate variable.
./DetectBlobs --in_data “$precip_file; $95th_file”
--out $ext_file --thresholdcmd “_DIFF(pr_var,95_var),>=,0,0”
./StitchBlobs --in $ext_file --out $blob_file --var binary_tag
Creating masks for 500-hPa geopotential height to extract TC precipitation
./NodeFileFilter --in_nodefile $outfile_SN
--in_fmt “lon,lat,psl,maxwind,phis” --in_data $geo_file
--out_data $contour_file --bycontour “Z500,10.0,5.0,1”
--var “Z500” --maskvar “mask” --bydist “1.0”
Here, geo_file contains geopotential height data at 500 hPa.
Filtering extreme precipitation resulting from TCs
./DetectBlobs--in_data “$blob_file; $contour_file”
--out $tc_ext_pr_file
--thresholdcmd “ar_binary_tagtag,>,0,0”
--filtercmd “mask,>,0.5,1”
Remapping functions using TEMPESTREMAP
./GenerateOverlapMesh --a $pr_grid --b $reanalysis_grid
--out $out_grid
./GenerateOfflineMap --in_type fv --in_np 1 --out_type fv
--out_np 1 --correct_areas --in_mesh $pr_grid
--out_mesh $reanalysis_grid
--ov_mesh $out_grid --out_map $out_map
./ApplyOfflineMap --map $out_map --in_data $blob_file --out_data $remap_blob_file --var ar_binary_tagtag
If remapping is required, substitute $remap_blob_file for $blob_file in the filtering command line.
REFERENCES
Alexander, L. V., M. Bador, R. Roca, S. Contractor, M. G. Donat, and P. L. Nguyen, 2020: Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based, and reanalysis products. Environ. Res. Lett., 15, 055002, https://doi.org/10.1088/1748-9326/ab79e2.
Alvey, G., III, J. Zawislak, and E. Zisper, 2015: Precipitation properties observed during tropical cyclone intensity change. Mon. Wea. Rev., 143, 4476–4492, https://doi.org/10.1175/MWR-D-15-0065.1.
Bador, M., L. V. Alexander, S. Contractor, and R. Roca, 2020: Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets. Environ. Res. Lett., 15, 035005, https://doi.org/10.1088/1748-9326/ab6a22.
Balaji, M., A. Chakraborty, and M. Mandal, 2018: Changes in tropical cyclone activity in north Indian Ocean during satellite era (1981–2014). Int. J. Climatol., 38, 2819–2837, https://doi.org/10.1002/joc.5463.
Bieli, M., S. Camargo, A. Sobel, J. Evans, and T. Hall, 2019: A global climatology of extratropical transition. Part I: Characteristics across basins. J. Climate, 32, 3557–3582, https://doi.org/10.1175/JCLI-D-17-0518.1.
Camargo, S., and A. Sobel, 2005: Western North Pacific tropical cyclone intensity and ENSO. J. Climate, 18, 2996–3006, https://doi.org/10.1175/JCLI3457.1.
Chan, J. C., and J.-E. Shi, 2000: Frequency of typhoon landfall over Guangdong Province of China during the period 1470–1931. Int. J. Climatol., 20, 183–190, https://doi.org/10.1002/(SICI)1097-0088(200002)20:2<183::AID-JOC479>3.0.CO;2-U.
Chen, S. S., J. A. Knaff, and F. D. Marks Jr., 2006: Effects of vertical wind shear and storm motion on tropical cyclone rainfall asymmetries deduced from TRMM. Mon. Wea. Rev., 134, 3190–3208, https://doi.org/10.1175/MWR3245.1.
Dhakal, N., 2019: Changing impacts of North Atlantic tropical cyclones on extreme precipitation distribution across the Mid-Atlantic United States. Geosciences, 9, 207, https://doi.org/10.3390/geosciences9050207.
Evans, C., and Coauthors, 2017: The extratropical transition of tropical cyclones. Part I: Cyclone evolution and direct impacts. Mon. Wea. Rev., 145, 4317–4344, https://doi.org/10.1175/MWR-D-17-0027.1.
Hart, R., 2003: A cyclone phase space derived from thermal wind and thermal asymmetry. Mon. Wea. Rev., 131, 585–616, https://doi.org/10.1175/1520-0493(2003)131<0585:ACPSDF>2.0.CO;2.
Hart, R., and J. Evans, 2001: A climatology of the extratropical transition of Atlantic tropical cyclones. J. Climate, 14, 546–564, https://doi.org/10.1175/1520-0442(2001)014<0546:ACOTET>2.0.CO;2.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Huffman, G., R. Adler, M. Morrissey, D. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.
Huffman, G., E. Stocker, D. Bolvin, E. Nelkin, and J. Tan, 2019: GPM IMERG final precipitation L3 half hourly 0.1 degree × 0.1 degree V06. Goddard Earth Sciences Data and Information Services Center (GES DISC), last accessed 26 March 2020, https://doi.org/10.5067/GPM/IMERG/3B-HH/06.
Huffman, G., D. Bolvin, E. Nelkin, E. Stocker, and J. Tan, 2020: V06 IMERG release notes. NASA GPM, 15 pp., https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_V06_release_notes_201006_0.pdf.
Jagannathan, K., A. D. Jones, and I. Ray, 2021: The making of a metric: Co-producing decision-relevant climate science. Bull. Amer. Meteor. Soc., 102, E1579–E1590, https://doi.org/10.1175/BAMS-D-19-0296.1.
Janssen, E., D. Wuebbles, K. Kunkel, S. Olsen, and A. Goodman, 2014: Observational- and model-based trends and projections of extreme precipitation over the contiguous United States. Earth’s Future, 2, 99–113, https://doi.org/10.1002/2013EF000185.
Jiang, H., and E. Zisper, 2010: Contribution of tropical cyclones to the global precipitation from eight seasons of TRMM data: Regional, seasonal, and interannual variations. J. Climate, 23, 1526–1543, https://doi.org/10.1175/2009JCLI3303.1.
Jones, S., and Coauthors, 2003: The extratropical transition of tropical cyclones: Forecast challenges, current understanding, and future directions. Wea. Forecasting, 18, 1052–1092, https://doi.org/10.1175/1520-0434(2003)018<1052:TETOTC>2.0.CO;2.
Kubota, H., and B. Wang, 2009: How much do tropical cyclones affect seasonal and interannual rainfall variability over the western North Pacific? J. Climate, 22, 5495–5510, https://doi.org/10.1175/2009JCLI2646.1.
Kimball, S., and M. Mulekar, 2004: A 15-year climatology of North Atlantic tropical cyclones. Part I: Size parameters. J. Climate, 17, 3555–3575, https://doi.org/10.1175/1520-0442(2004)017<3555:AYCONA>2.0.CO;2.
Klein, P. M., P. A. Harr, and R. L. Elsberry, 2000: Extratropical transition of western North Pacific tropical cyclones: An overview and conceptual model of the transformation stage. Wea. Forecasting, 15, 373–395, https://doi.org/10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2.
Klotzbach, P., 2011: El Niño–Southern Oscillation’s impact on Atlantic basin Hurricanes and U.S. landfalls. J. Climate, 24, 1252–1263, https://doi.org/10.1175/2010JCLI3799.1.
Knapp, K. R., M. Kruk, D. Levinson, H. Diamond, and C. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1.
Knutson, T., and Coauthors, 2020: Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming. Bull. Amer. Meteor. Soc., 101, E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1.
Konrad, C., II, M. Meaux, and D. Meaux, 2002: Relationships between tropical cyclone attributes and precipitation totals: Considerations of scale. Int. J. Climatol., 22, 237–247, https://doi.org/10.1002/joc.721.
Kunkel, K. E., K. Andsager, and D. R. Easterling, 1999: Long-term trends in extreme precipitation events over the conterminous United States and Canada. J. Climate, 12, 2515–2527, https://doi.org/10.1175/1520-0442(1999)012<2515:LTTIEP>2.0.CO;2.
Kunkel, K. E., D. Easterling, D. Kristovich, B. Gleason, L. Stoecker, and R. Smith, 2012: Meteorological causes of the secular variations in observed extreme precipitation events for the conterminous United States. J. Hydrometeor., 13, 1131–1141, https://doi.org/10.1175/JHM-D-11-0108.1.
Landsea, C., 1993: A climatology of intense (or major) Atlantic hurricanes. Mon. Wea. Rev., 121, 1703–1713, https://doi.org/10.1175/1520-0493(1993)121<1703:ACOIMA>2.0.CO;2.
Liu, M., G. Vecchi, J. Smith, and H. Murakami, 2017: The present-day simulation and twenty-first century projection of the climatology of extratropical transition in the North Atlantic. J. Climate, 30, 2739–2756, https://doi.org/10.1175/JCLI-D-16-0352.1.
Liu, M., G. Vecchi, J. Smith, and H. Murakami, 2018: Projection of landfalling tropical cyclone rainfall in the eastern United States under anthropogenic warming. J. Climate, 31, 7269–7286, https://doi.org/10.1175/JCLI-D-17-0747.1.
Lonfat, M., F. Marks Jr., and S. Chen, 2004: Precipitation distribution in tropical cyclones using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager: A global perspective. Mon. Wea. Rev., 132, 1645–1660, https://doi.org/10.1175/1520-0493(2004)132<1645:PDITCU>2.0.CO;2.
Lonfat, M., R. Rogers, T. Marchok, and F. Marks Jr., 2007: A parametric model for predicting hurricane rainfall. Mon. Wea. Rev., 135, 3086–3097, https://doi.org/10.1175/MWR3433.1.
Meehl, G., F. Zwiers, J. Evans, T. Knutson, L. Mearns, and P. Whetton, 2000: Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bull. Amer. Meteor. Soc., 81, 427–436, https://doi.org/10.1175/1520-0477(2000)081<0427:TIEWAC>2.3.CO;2.
Michaelis, A., 2016: Climate change effects on the extratropical transition of tropical cyclones in high-resolution global simulations. Ph.D. dissertation, North Carolina State University Libraries, 156 pp., http://www.lib.ncsu.edu/resolver/1840.20/36292.
Nicholls, N., 1979: A possible method for predicting seasonal tropical cyclone activity in the Australian region. Mon. Wea. Rev., 107, 1221–1224, https://doi.org/10.1175/1520-0493(1979)107<1221:APMFPS>2.0.CO;2.
Pendergrass, A., 2018: What precipitation is extreme? Science, 360, 1072–1073, https://doi.org/10.1126/science.aat1871.
Rios Gaona, M., G. Villarini, W. Zhang, and G. Vecchi, 2018: The added value of IMERG in characterizing rainfall in tropical cyclones. Atmos. Res., 209, 95–102, https://doi.org/10.1016/j.atmosres.2018.03.008.
Rodgers, E. B., R. F. Adler, and H. F. Pierce, 2000: Contribution of tropical cyclones to the North Pacific climatological rainfall as observed from satellites. J. Appl. Meteor., 39, 1658–1678, https://doi.org/10.1175/1520-0450(2000)039<1658:COTCTT>2.0.CO;2.
Rodgers, E. B., R. F. Adler, and H. F. Pierce, 2001: Contribution of tropical cyclones to the North Atlantic climatological rainfall as observed from satellites. J. Appl. Meteor., 40, 1785–1800, https://doi.org/10.1175/1520-0450(2001)040<1785:COTCTT>2.0.CO;2.
Schenkel, B., N. Lin, N. Chavas, M. Oppenheimer, and A. Brammer, 2017: Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT data. J. Climate, 30, 8745–8762, https://doi.org/10.1175/JCLI-D-17-0122.1.
Schumacher, R., and R. Johnson, 2006: Characteristics of U.S. extreme rain events during 1999-2003. Wea. Forecasting, 21, 69–85, https://doi.org/10.1175/WAF900.1.
Shepherd, J. M., A. Grundstein, and T. L. Mote, 2007: Quantifying the contribution of tropical cyclones to extreme rainfall along the coastal southeastern United States. Geophys. Res. Lett., 34, L23810, https://doi.org/10.1029/2007GL031694.
Sinclair, M. R., 2002: Extratropical transition of southwest Pacific tropical cyclones. Part I: Climatology and mean structure changes. Mon. Wea. Rev., 130, 590–609, https://doi.org/10.1175/1520-0493(2002)130<0590:ETOSPT>2.0.CO;2.
Skok, G., J. Tribbia, J. Rakovec, and B. Brown, 2009: Object-based analysis of satellite-derived precipitation systems over the low- and mid-latitude Pacific Ocean. Mon. Wea. Rev., 137, 3196–3218, https://doi.org/10.1175/2009MWR2900.1.
Skok, G., J. Bacmeister, and J. Tribbia, 2013: Analysis of tropical cyclone precipitation using an object-based algorithm. J. Climate, 26, 2563–2579, https://doi.org/10.1175/JCLI-D-12-00135.1.
Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035–2046, https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.
Stansfield, A. M., K. A. Reed, C. M. Zarzycki, P. A. Ullrich, and D. R. Chavas, 2020a: Assessing tropical cyclones’ contribution to precipitation over the eastern United States and sensitivity to variable resolution domain extent. J. Hydrometeor., 21, 1425–1445, https://doi.org/10.1175/JHM-D-19-0240.1.
Stansfield, A. M., K. A. Reed, and C. M. Zarzycki, 2020b: Changes in precipitation from North Atlantic tropical cyclones under RCP scenarios in the variable-resolution community atmosphere model. Geophys. Res. Lett., 47, e2019GL086930, https://doi.org/10.1029/2019GL086930.
Stevenson, S. N., and R. S. Schumacher, 2014: A 10-year survey of extreme rainfall events in the central and eastern United States using gridded multisensor precipitation analysis. Mon. Wea. Rev., 142, 3147–3162, https://doi.org/10.1175/MWR-D-13-00345.1.
Touma, D., A. M. Michalak, D. L. Swain, and N. S. Diffenbaugh, 2018: Characterizing the spatial scales of extreme daily precipitation in the United States. J. Climate, 31, 8023–8037, https://doi.org/10.1175/JCLI-D-18-0019.1.
Ullrich, P., and C. Zarzycki, 2017: TempestExtremes: A framework for scale-insensitive pointwise feature tracking on unstructured grids. Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017.
Ullrich, P., C. Zarzycki, E. E. McLenny, M. C. Pinheiro, A. M. Stansfield, and K. A. Reed, 2021: TempestExtremes v2.1: A community framework for feature detection, tracking and analysis in large datasets. Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021.
Utsumi, N., H. Kim, S. Kanae, and T. Oki, 2016: Which weather systems are projected to cause future changes in mean and extreme precipitation inCMIP5 simulations? J. Geophys. Res. Atmos., 121, 10 522–10 537, https://doi.org/10.1002/2016JD024939.
Villarini, G., J. Smith, M. Baeck, T. Marchok, and G. Vecchi, 2011: Characterization of rainfall distribution and flooding associated with U.S. land falling tropical cyclones: Analyses of Hurricanes Frances, Ivan, and Jeanne (2004). J. Geophys. Res., 116, D23116, https://doi.org/10.1029/2011JD016175.
Wu, Y., S. Wu, and P. Zhai, 2007: The impact of tropical cyclones on Hainan Island’s extreme and total precipitation. Int. J. Climatol., 27, 1059–1064, https://doi.org/10.1002/joc.1464.
Zarzycki, C. M., and P. Ullrich, 2017: Assessing sensitivities in algorithmic detection of tropical cyclones in climate data. Geophys. Res. Lett., 44, 1141–1149, https://doi.org/10.1002/2016GL071606.
Zarzycki, C. M., D. Thatcher, and C. Jablonowski, 2017: Objective tropical cyclone extratropical transition detection in high-resolution reanalysis and climate model data. J. Adv. Model. Earth Syst., 9, 130–148, https://doi.org/10.1002/2016MS000775.
