Abstract
Airframe icing caused by interactions with supercooled cloud droplets and precipitation can pose a risk to aviation operations and life safety. The In-Cloud Icing and Large-drop Experiment (ICICLE) was conducted in January–March 2019 to capture measurements in freezing conditions in support of the Federal Aviation Administration (FAA) Terminal Area Icing Weather Information for NextGen (TAIWIN) program. The National Research Council of Canada’s Convair-580 research aircraft fulfilled the airborne data collection requirements for the ICICLE campaign and sampled icing clouds and atmospheric conditions over the midwestern United States. ICICLE flight 18, conducted on 17 February 2019, collected cloud and precipitation measurements during a widespread storm that generated supercooled small drops and freezing drizzle (FZDZ) within both liquid and mixed-phase regions. Supercooled liquid water content (LWC) typically ranged 0.30–0.45 g m−3 and exceeded 0.70 g m−3 in one instance. Maximum FZDZ diameters of 300–400 μm were commonly sampled near the base of clouds. Missed approaches performed at four Illinois airfields provided measurements of conditions from near ground level to above cloud top and supplied information regarding FZDZ formation and evolution. FZDZ was found to form at altitudes featuring relatively high LWC and sufficiently low droplet number concentrations. FZDZ formation zones were sometimes collocated with regions of atmospheric instability and/or wind shear. Flight through highly variable supercooled cloud droplet and FZDZ conditions resulted in significant Convair-580 airframe icing, highlighting the risk that icing conditions can pose to aircraft safety.
Abstract
Airframe icing caused by interactions with supercooled cloud droplets and precipitation can pose a risk to aviation operations and life safety. The In-Cloud Icing and Large-drop Experiment (ICICLE) was conducted in January–March 2019 to capture measurements in freezing conditions in support of the Federal Aviation Administration (FAA) Terminal Area Icing Weather Information for NextGen (TAIWIN) program. The National Research Council of Canada’s Convair-580 research aircraft fulfilled the airborne data collection requirements for the ICICLE campaign and sampled icing clouds and atmospheric conditions over the midwestern United States. ICICLE flight 18, conducted on 17 February 2019, collected cloud and precipitation measurements during a widespread storm that generated supercooled small drops and freezing drizzle (FZDZ) within both liquid and mixed-phase regions. Supercooled liquid water content (LWC) typically ranged 0.30–0.45 g m−3 and exceeded 0.70 g m−3 in one instance. Maximum FZDZ diameters of 300–400 μm were commonly sampled near the base of clouds. Missed approaches performed at four Illinois airfields provided measurements of conditions from near ground level to above cloud top and supplied information regarding FZDZ formation and evolution. FZDZ was found to form at altitudes featuring relatively high LWC and sufficiently low droplet number concentrations. FZDZ formation zones were sometimes collocated with regions of atmospheric instability and/or wind shear. Flight through highly variable supercooled cloud droplet and FZDZ conditions resulted in significant Convair-580 airframe icing, highlighting the risk that icing conditions can pose to aircraft safety.
Abstract
Since the start of the operational use of ensemble prediction systems, ensemble-based probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of the last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical postprocessing. Nowadays, one can choose from a large variety of postprocessing approaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observation pairs; thus, postprocessed forecasts are usually available only at those locations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the ensemble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) postprocessing technique, in a case study based on 10-m wind speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.
Abstract
Since the start of the operational use of ensemble prediction systems, ensemble-based probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of the last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical postprocessing. Nowadays, one can choose from a large variety of postprocessing approaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observation pairs; thus, postprocessed forecasts are usually available only at those locations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the ensemble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) postprocessing technique, in a case study based on 10-m wind speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.
Abstract
This study presents an evaluation of the skill of 12 global climate models from the CMIP6 archive in capturing convective-storm parameters over the United States. For the historical reference period 1979-2014, we compare the model-simulated 6-hourly CAPE, CIN, 0-1 km wind shear (S01) and 0-6 km wind shear (S06) to those from two independent reanalysis datasets – ERA5 and MERRA2. To obtain a comprehensive picture, we analyze the parameter distribution, climatological mean, extreme, and thresholded frequency of convective parameters. The analysis reveals significant bias in capturing both magnitude and spatial patterns, which also vary across the seasons. The spatial distribution of means and extremes of the parameters indicate that most models tend to overestimate CAPE, whereas S01, and S06 are underrepresented to varying extents. Additionally, models tend to underestimate extremes in CIN. Comparing the model profiles with rawinsonde profiles indicates that most of the high-CAPE models have warm and moist bias. We also find that the near-surface wind speed is generally underestimated by the models. The intermodel spread larger for thermodynamic parameters as compared to kinematic parameters. The models generally have a significant positive bias in CAPE over western and eastern regions of the continental US. More importantly, the bias in thresholded frequency of all four variables is considerably larger than the bias in mean, suggesting a non-uniform bias across the distribution. This likely leads to an under-representation of favorable severe thunderstorm environments, and has the potential to influence dynamical downscaling simulations via initial and boundary conditions.
Abstract
This study presents an evaluation of the skill of 12 global climate models from the CMIP6 archive in capturing convective-storm parameters over the United States. For the historical reference period 1979-2014, we compare the model-simulated 6-hourly CAPE, CIN, 0-1 km wind shear (S01) and 0-6 km wind shear (S06) to those from two independent reanalysis datasets – ERA5 and MERRA2. To obtain a comprehensive picture, we analyze the parameter distribution, climatological mean, extreme, and thresholded frequency of convective parameters. The analysis reveals significant bias in capturing both magnitude and spatial patterns, which also vary across the seasons. The spatial distribution of means and extremes of the parameters indicate that most models tend to overestimate CAPE, whereas S01, and S06 are underrepresented to varying extents. Additionally, models tend to underestimate extremes in CIN. Comparing the model profiles with rawinsonde profiles indicates that most of the high-CAPE models have warm and moist bias. We also find that the near-surface wind speed is generally underestimated by the models. The intermodel spread larger for thermodynamic parameters as compared to kinematic parameters. The models generally have a significant positive bias in CAPE over western and eastern regions of the continental US. More importantly, the bias in thresholded frequency of all four variables is considerably larger than the bias in mean, suggesting a non-uniform bias across the distribution. This likely leads to an under-representation of favorable severe thunderstorm environments, and has the potential to influence dynamical downscaling simulations via initial and boundary conditions.
Abstract
In this study, the cause of rotation in simulated dust-devil-like vortices is investigated. The analysis uses a numerical simulation of an initially resting, dry, atmosphere, in which uniform surface heating leads to the development of a growing convective boundary layer (CBL). As soon as convective mixing sets in, regions of weak vertical vorticity develop at the lowest model level. Using forward trajectories, this vorticity is shown to originate from horizontal baroclinic production and simultaneous reorientation into the vertical within the descending branches of the convective cells. The requirement for vertical vorticity production in the downdraft cells is shown to be a nonaxisymmetric horizontal footprint of the downdraft regions. The resulting vertical vorticity is not initially associated with rotation. However, as the CBL matures, like-signed vortex patches merge, the vertical vorticity magnitude increases due to stretching, and deformation in the vortex patch decreases, leading to the development of vortices. The ultimate origin of the vortices is thus initially horizontal vorticity that has been produced baroclinically and that has subsequently been reoriented into the vertical in sinking air.
Significance Statement
Dust devils are concentrated vortices consisting of rapidly rising buoyant air, which may pose a risk to small aircraft and light structures on the ground. Although these vortices are a common occurrence in convective boundary layers, the origin of the vorticity within these vortices has not yet been fully established. The present study uses a numerical simulation of an evolving convective boundary layer and analyzes air parcel trajectories to identify the origin of vertical vorticity at the surface during dust-devil formation. The work contributes an answer to the long-standing question of what causes dust devils to spin.
Abstract
In this study, the cause of rotation in simulated dust-devil-like vortices is investigated. The analysis uses a numerical simulation of an initially resting, dry, atmosphere, in which uniform surface heating leads to the development of a growing convective boundary layer (CBL). As soon as convective mixing sets in, regions of weak vertical vorticity develop at the lowest model level. Using forward trajectories, this vorticity is shown to originate from horizontal baroclinic production and simultaneous reorientation into the vertical within the descending branches of the convective cells. The requirement for vertical vorticity production in the downdraft cells is shown to be a nonaxisymmetric horizontal footprint of the downdraft regions. The resulting vertical vorticity is not initially associated with rotation. However, as the CBL matures, like-signed vortex patches merge, the vertical vorticity magnitude increases due to stretching, and deformation in the vortex patch decreases, leading to the development of vortices. The ultimate origin of the vortices is thus initially horizontal vorticity that has been produced baroclinically and that has subsequently been reoriented into the vertical in sinking air.
Significance Statement
Dust devils are concentrated vortices consisting of rapidly rising buoyant air, which may pose a risk to small aircraft and light structures on the ground. Although these vortices are a common occurrence in convective boundary layers, the origin of the vorticity within these vortices has not yet been fully established. The present study uses a numerical simulation of an evolving convective boundary layer and analyzes air parcel trajectories to identify the origin of vertical vorticity at the surface during dust-devil formation. The work contributes an answer to the long-standing question of what causes dust devils to spin.
Abstract
Understanding precipitation over complex terrain, such as southwestern China, requires the consideration of both multiscale circulation and topography. First, the dominant synoptic system must be clarified, as it determines how multiscale topography affects precipitation. Here, based on a self-organizing map, large-scale winds are categorized into anomalous-westerly types, anomalous-easterly types, and transitional types. Four synoptic-scale systems (vortex type, cold-front type, tropical-depression type, and weak-synoptic-forcing type) dominate the summer precipitation. The vortex type occurs with strengthened large-scale westerlies, and its precipitation is distributed within the moisture convergence region. The cold-front type, tropical-depression type, and weak-synoptic-forcing type exhibit large-scale easterly anomalies. For the cold-front type, a low-level northeasterly blocked by topography shapes the northwest–southeast-oriented front zone at the upper highland slope. The precipitation frequency and intensity are high within the frontal zone, while the intensity is weak on both sides. For the tropical-depression type, moist low-level easterlies uplifted by westward-rising topography anchor precipitation at the lower slope. Large precipitation for the tropical-depression type is attributed to a high frequency. Large-scale horizontal winds are the weakest for the weak-synoptic-forcing type, and the local topography influences the scattered precipitation distribution. Both the frequency and intensity are high for the weak-synoptic-forcing type. Overall, long-lasting nocturnal events dominate the precipitation of the four synoptic types, while large-scale easterlies favor precipitation events with shorter durations and earlier peaks. For obvious synoptic systems, large-scale topography influences precipitation via a dynamic blocking effect, while the thermodynamic role of local topography is important with a weak-synoptic-forcing.
Significance Statement
Clarifying dominant synoptic systems is highly important for understanding precipitation over complex terrain, as the effect of topography on precipitation varies with different synoptic backgrounds. Taking southwestern China as a representative of complex terrain, this study objectively identified the dominant synoptic systems associated with summer precipitation. The distribution and fine-scale characteristics of precipitation have been further analyzed considering the combined influence of multiscale circulation and topography. In addition to advancing our understanding of precipitation in southwestern China, this study provides a reference for analyzing precipitation in other regions with complex terrains.
Abstract
Understanding precipitation over complex terrain, such as southwestern China, requires the consideration of both multiscale circulation and topography. First, the dominant synoptic system must be clarified, as it determines how multiscale topography affects precipitation. Here, based on a self-organizing map, large-scale winds are categorized into anomalous-westerly types, anomalous-easterly types, and transitional types. Four synoptic-scale systems (vortex type, cold-front type, tropical-depression type, and weak-synoptic-forcing type) dominate the summer precipitation. The vortex type occurs with strengthened large-scale westerlies, and its precipitation is distributed within the moisture convergence region. The cold-front type, tropical-depression type, and weak-synoptic-forcing type exhibit large-scale easterly anomalies. For the cold-front type, a low-level northeasterly blocked by topography shapes the northwest–southeast-oriented front zone at the upper highland slope. The precipitation frequency and intensity are high within the frontal zone, while the intensity is weak on both sides. For the tropical-depression type, moist low-level easterlies uplifted by westward-rising topography anchor precipitation at the lower slope. Large precipitation for the tropical-depression type is attributed to a high frequency. Large-scale horizontal winds are the weakest for the weak-synoptic-forcing type, and the local topography influences the scattered precipitation distribution. Both the frequency and intensity are high for the weak-synoptic-forcing type. Overall, long-lasting nocturnal events dominate the precipitation of the four synoptic types, while large-scale easterlies favor precipitation events with shorter durations and earlier peaks. For obvious synoptic systems, large-scale topography influences precipitation via a dynamic blocking effect, while the thermodynamic role of local topography is important with a weak-synoptic-forcing.
Significance Statement
Clarifying dominant synoptic systems is highly important for understanding precipitation over complex terrain, as the effect of topography on precipitation varies with different synoptic backgrounds. Taking southwestern China as a representative of complex terrain, this study objectively identified the dominant synoptic systems associated with summer precipitation. The distribution and fine-scale characteristics of precipitation have been further analyzed considering the combined influence of multiscale circulation and topography. In addition to advancing our understanding of precipitation in southwestern China, this study provides a reference for analyzing precipitation in other regions with complex terrains.
Abstract
Knowledge of the effects of climate modes on the equatorial intermediate current (EIC) remains limited. This paper investigates exceptional events of the EIC in the Indian Ocean and their relationships with climate modes at various time scales by using observations, reanalysis outputs, and a continuously stratified linear ocean model (LOM). A mooring at 80°E from 2015 to 2019 revealed four exceptionally strong EIC events, occurring in 2015 July–August (JA), 2016 January–February (JF), 2016 JA, and 2019 JF. Component analysis revealed that these exceptional events are attributed to the co-occurrence of the seasonal components peaking during JF and JA, as well as the larger current anomalies associated with intraseasonal and interannual components. In the intraseasonal band, the Madden–Julian oscillation (MJO) generates a significant EIC anomaly through a 40–50-day process involving equatorial waves. The MJO exerts a substantial effect when the amplitude of the MJO index exceeds 1 and the oscillation is in phase 4. In the interannual band, El Niño–Southern Oscillation and the Indian Ocean dipole (IOD) can each independently contribute to the EIC anomaly. This contribution initiates during the mature phase and persists throughout the subsequent year. Concurrent occurrences of these effects can lead to larger interannual anomalies. Notably, El Niño–positive IOD (El Niño–pIOD) and La Niña–negative IOD (La Niña–nIOD) synergies are most pronounced in August and in January of the following year.
Significance Statement
Four exceptional equatorial intermediate current (EIC) events characterized by strong eastward velocities are observed in the Indian Ocean during 2015–19. This study explored their underlying dynamics and their relationships with climate modes. Climatologically, the EIC has seasonal cycles with peaks in January–February and July–August, which is helpful in producing exceptional EIC events. However, the occurrence of these events is attributed to the intensity of the intraseasonal and interannual variability that occurs around the seasonal cycle peaks. This study revealed that climate modes, including the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), and Indian Ocean dipole (IOD), contribute to exceptional EIC events in the intraseasonal and interannual bands. Notably, their influence is comparable, albeit contingent upon specific conditions.
Abstract
Knowledge of the effects of climate modes on the equatorial intermediate current (EIC) remains limited. This paper investigates exceptional events of the EIC in the Indian Ocean and their relationships with climate modes at various time scales by using observations, reanalysis outputs, and a continuously stratified linear ocean model (LOM). A mooring at 80°E from 2015 to 2019 revealed four exceptionally strong EIC events, occurring in 2015 July–August (JA), 2016 January–February (JF), 2016 JA, and 2019 JF. Component analysis revealed that these exceptional events are attributed to the co-occurrence of the seasonal components peaking during JF and JA, as well as the larger current anomalies associated with intraseasonal and interannual components. In the intraseasonal band, the Madden–Julian oscillation (MJO) generates a significant EIC anomaly through a 40–50-day process involving equatorial waves. The MJO exerts a substantial effect when the amplitude of the MJO index exceeds 1 and the oscillation is in phase 4. In the interannual band, El Niño–Southern Oscillation and the Indian Ocean dipole (IOD) can each independently contribute to the EIC anomaly. This contribution initiates during the mature phase and persists throughout the subsequent year. Concurrent occurrences of these effects can lead to larger interannual anomalies. Notably, El Niño–positive IOD (El Niño–pIOD) and La Niña–negative IOD (La Niña–nIOD) synergies are most pronounced in August and in January of the following year.
Significance Statement
Four exceptional equatorial intermediate current (EIC) events characterized by strong eastward velocities are observed in the Indian Ocean during 2015–19. This study explored their underlying dynamics and their relationships with climate modes. Climatologically, the EIC has seasonal cycles with peaks in January–February and July–August, which is helpful in producing exceptional EIC events. However, the occurrence of these events is attributed to the intensity of the intraseasonal and interannual variability that occurs around the seasonal cycle peaks. This study revealed that climate modes, including the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), and Indian Ocean dipole (IOD), contribute to exceptional EIC events in the intraseasonal and interannual bands. Notably, their influence is comparable, albeit contingent upon specific conditions.
Abstract
In this study, ERA5 reanalysis data from 1979 to 2022 were utilized to investigate extreme precipitation in the Central Asian High Mountain (CAHM) region, comprising the Pamir Plateau and Western, Central, and Eastern Tianshan regions. This study found that westerlies and monsoons are the primary drivers of extreme precipitation, with distinct mechanisms in the southwestern and northeastern CAHM (divided at approximately 79°E). In the southwestern CAHM, a weak Indian Summer Monsoon (ISM) leads to negative potential height anomalies, enhancing meridional water vapor flux from the Bay of Bengal and Arabian Sea, thereby increasing precipitation. Conversely, extreme precipitation is associated with the negative phase of the Silk Road pattern in the northeastern CAHM. While the East Asian Summer Monsoon (EASM) plays a lesser role, it influences water vapor supplies and atmospheric circulation in the southwestern CAHM and modulate meridional wind position in the northeastern CAHM with the ISM, contributing to extreme precipitation. Seasonal analysis revealed May as the peak for extreme precipitation in the southwestern CAHM region, while extreme precipitation in the northeastern CAHM region peaked in the mid-monsoon months (June and July) due to the synergy between monsoons and westerlies of different strengths passing through the CAHM.
Abstract
In this study, ERA5 reanalysis data from 1979 to 2022 were utilized to investigate extreme precipitation in the Central Asian High Mountain (CAHM) region, comprising the Pamir Plateau and Western, Central, and Eastern Tianshan regions. This study found that westerlies and monsoons are the primary drivers of extreme precipitation, with distinct mechanisms in the southwestern and northeastern CAHM (divided at approximately 79°E). In the southwestern CAHM, a weak Indian Summer Monsoon (ISM) leads to negative potential height anomalies, enhancing meridional water vapor flux from the Bay of Bengal and Arabian Sea, thereby increasing precipitation. Conversely, extreme precipitation is associated with the negative phase of the Silk Road pattern in the northeastern CAHM. While the East Asian Summer Monsoon (EASM) plays a lesser role, it influences water vapor supplies and atmospheric circulation in the southwestern CAHM and modulate meridional wind position in the northeastern CAHM with the ISM, contributing to extreme precipitation. Seasonal analysis revealed May as the peak for extreme precipitation in the southwestern CAHM region, while extreme precipitation in the northeastern CAHM region peaked in the mid-monsoon months (June and July) due to the synergy between monsoons and westerlies of different strengths passing through the CAHM.
Abstract
Hail research and forecasting models necessarily involve explicit or implicit—and uncertain—physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer. Whereas most models assume spherical hailstones, we relax this assumption by using hailstone shape data from field observations to establish empirical size–shape relationships with reasonable degrees of randomness considering hailstones’ natural shape variability, capturing the observed distribution of triaxial ellipsoidal shapes. We also incorporate explicit, random tumbling of individual hailstones during their growth to simulate their free-falling behavior and the resultant changes in cross-sectional area (which affects growth by hydrometeor collection). These physical attributes are incorporated in calculating hailstones’ fall speeds, using either empirical relationships or analytical relationships based on each hailstone’s Best and Reynolds numbers. Options for drag coefficient modification are added to emulate hailstones’ rough surfaces (lobes), which then modifies their thermal energy and vapor exchange with the environment. We investigate how applying these physical assumptions about nonspherical hail to the Penn State hail growth trajectory model, coupled with Cloud Model 1 supercell simulations, impacts hail production and examine the reasons behind the resulting variability in hail statistics. The choice of hailstone size–mass relation and fall speed scheme have the strongest influence on hail sizes. Using nonspherical, tumbling hailstones reduces the number of large hailstones produced. Applying shape-specific thermal energy transfer coefficients subtly increases sizes; the effects of lobes vary depending on the fall speed scheme used. These physical assumptions, although adding complexity to modeling, can be parameterized efficiently and potentially used in microphysics schemes.
Significance Statement
In numerical modeling of hailstorms, we usually consider hailstones to be spherical to simplify calculations, but in nature, hailstones generally are not spheres and can be rather lumpy and have spikes. The purpose of this study is to examine how the model result would change when nonspherical hailstone shape is implemented. We examine the relationship between hailstone shape and physical processes during hail growth in effort to explain why these changes occur and offer insights on how nonspherical hailstone shape may be parameterized in bulk microphysics schemes.
Abstract
Hail research and forecasting models necessarily involve explicit or implicit—and uncertain—physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer. Whereas most models assume spherical hailstones, we relax this assumption by using hailstone shape data from field observations to establish empirical size–shape relationships with reasonable degrees of randomness considering hailstones’ natural shape variability, capturing the observed distribution of triaxial ellipsoidal shapes. We also incorporate explicit, random tumbling of individual hailstones during their growth to simulate their free-falling behavior and the resultant changes in cross-sectional area (which affects growth by hydrometeor collection). These physical attributes are incorporated in calculating hailstones’ fall speeds, using either empirical relationships or analytical relationships based on each hailstone’s Best and Reynolds numbers. Options for drag coefficient modification are added to emulate hailstones’ rough surfaces (lobes), which then modifies their thermal energy and vapor exchange with the environment. We investigate how applying these physical assumptions about nonspherical hail to the Penn State hail growth trajectory model, coupled with Cloud Model 1 supercell simulations, impacts hail production and examine the reasons behind the resulting variability in hail statistics. The choice of hailstone size–mass relation and fall speed scheme have the strongest influence on hail sizes. Using nonspherical, tumbling hailstones reduces the number of large hailstones produced. Applying shape-specific thermal energy transfer coefficients subtly increases sizes; the effects of lobes vary depending on the fall speed scheme used. These physical assumptions, although adding complexity to modeling, can be parameterized efficiently and potentially used in microphysics schemes.
Significance Statement
In numerical modeling of hailstorms, we usually consider hailstones to be spherical to simplify calculations, but in nature, hailstones generally are not spheres and can be rather lumpy and have spikes. The purpose of this study is to examine how the model result would change when nonspherical hailstone shape is implemented. We examine the relationship between hailstone shape and physical processes during hail growth in effort to explain why these changes occur and offer insights on how nonspherical hailstone shape may be parameterized in bulk microphysics schemes.
Abstract
Regional patterns of the seasonal weather and atmospheric moisture origins can impact the seasonal activities of extreme precipitation in the eastern United States (eUS). At many locations, tracks of atmospheric moisture can have different influence on timing of extreme precipitation based on the moisture origin source. In this study, we evaluate the contribution of atmospheric rivers (ARs) and their moisture origin sources to the distribution and seasonal effectivity of annual maximum precipitation (AMP) across the eUS during 1950–2021. Our results suggest that AR is a dominant mechanism of AMP in the eUS as it contributes to 75% (31 438 out of 41 976) of total AMP events recorded between 1950 and 2021. The seasonal analysis based on the circular density approach shows that spring, summer, and fall seasons display strong signals of seasonality of AMP-AR events. The spatial patterns of AMP associated with the four major moisture sources (the Pacific Ocean, the Atlantic Ocean, the combined source of the Caribbean Sea and the Gulf of Mexico, and the local source of moisture) reinforce the key role ARs play in transporting water vapor to the eUS from both oceanic and inland originated moisture. The results additionally highlight the importance of moisture subsources (major source subregions) in modulating the seasonality of extreme precipitation in the eUS.
Abstract
Regional patterns of the seasonal weather and atmospheric moisture origins can impact the seasonal activities of extreme precipitation in the eastern United States (eUS). At many locations, tracks of atmospheric moisture can have different influence on timing of extreme precipitation based on the moisture origin source. In this study, we evaluate the contribution of atmospheric rivers (ARs) and their moisture origin sources to the distribution and seasonal effectivity of annual maximum precipitation (AMP) across the eUS during 1950–2021. Our results suggest that AR is a dominant mechanism of AMP in the eUS as it contributes to 75% (31 438 out of 41 976) of total AMP events recorded between 1950 and 2021. The seasonal analysis based on the circular density approach shows that spring, summer, and fall seasons display strong signals of seasonality of AMP-AR events. The spatial patterns of AMP associated with the four major moisture sources (the Pacific Ocean, the Atlantic Ocean, the combined source of the Caribbean Sea and the Gulf of Mexico, and the local source of moisture) reinforce the key role ARs play in transporting water vapor to the eUS from both oceanic and inland originated moisture. The results additionally highlight the importance of moisture subsources (major source subregions) in modulating the seasonality of extreme precipitation in the eUS.