Browse
Abstract
Supercell storms are commonly responsible for severe hail, which is the costliest severe storm hazard in the United States and elsewhere. Radar observations of such storms are common and have been leveraged to estimate hail size and severe hail occurrence. However, many established relationships between radar-observed storm characteristics and severe hail occurrence have been found using data from few storms and in isolation from other radar metrics. This study leverages a 10-yr record of polarimetric Doppler radar observations in the United States to evaluate and compare radar observations of thousands of severe hail–producing supercells based on their maximum hail size. In agreement with prior studies, it is found that increasing hail size relates to increasing volume of high (≥50 dBZ) radar reflectivity, increasing midaltitude mesocyclone rotation (azimuthal shear), increasing storm-top divergence, and decreased differential reflectivity and copolar correlation coefficient at low levels (mostly below the environmental 0°C level). New insights include increasing vertical alignment of the storm mesocyclone with increasing hail size and a Doppler velocity spectrum width minimum aloft near storm center that increases in area with increasing hail size and is argued to indicate increasing updraft width. To complement the extensive radar analysis, near-storm environments from reanalyses are compared and indicate that the greatest environmental differences exist in the middle troposphere (within the hail growth region), especially the wind speed perpendicular to storm motion. Recommendations are given for future improvements to radar-based hail-size estimation.
Abstract
Supercell storms are commonly responsible for severe hail, which is the costliest severe storm hazard in the United States and elsewhere. Radar observations of such storms are common and have been leveraged to estimate hail size and severe hail occurrence. However, many established relationships between radar-observed storm characteristics and severe hail occurrence have been found using data from few storms and in isolation from other radar metrics. This study leverages a 10-yr record of polarimetric Doppler radar observations in the United States to evaluate and compare radar observations of thousands of severe hail–producing supercells based on their maximum hail size. In agreement with prior studies, it is found that increasing hail size relates to increasing volume of high (≥50 dBZ) radar reflectivity, increasing midaltitude mesocyclone rotation (azimuthal shear), increasing storm-top divergence, and decreased differential reflectivity and copolar correlation coefficient at low levels (mostly below the environmental 0°C level). New insights include increasing vertical alignment of the storm mesocyclone with increasing hail size and a Doppler velocity spectrum width minimum aloft near storm center that increases in area with increasing hail size and is argued to indicate increasing updraft width. To complement the extensive radar analysis, near-storm environments from reanalyses are compared and indicate that the greatest environmental differences exist in the middle troposphere (within the hail growth region), especially the wind speed perpendicular to storm motion. Recommendations are given for future improvements to radar-based hail-size estimation.
Abstract
Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) is that no ensemble members have nonzero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the nonzero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with nonzero lightning flashes is smaller than a prescribed threshold (N min). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have nonzero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of N min.
Significance Statement
This study develops an effective method to use lightning flash observations for weather prediction. Lightning flash observations include precious information of the inner structure of clouds, but their effective use for weather prediction is not straightforward since a weather prediction model often misses observed lightning flashes. Our new method uses ensemble-generated statistical relationships to compensate for the misses and successfully improves the forecast accuracy of heavy rains in a simulated case. Our future work will test the method with real observation data.
Abstract
Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) is that no ensemble members have nonzero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the nonzero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with nonzero lightning flashes is smaller than a prescribed threshold (N min). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have nonzero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of N min.
Significance Statement
This study develops an effective method to use lightning flash observations for weather prediction. Lightning flash observations include precious information of the inner structure of clouds, but their effective use for weather prediction is not straightforward since a weather prediction model often misses observed lightning flashes. Our new method uses ensemble-generated statistical relationships to compensate for the misses and successfully improves the forecast accuracy of heavy rains in a simulated case. Our future work will test the method with real observation data.
Abstract
High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.
Abstract
High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.
Abstract
Tropical transition (TT) is a cyclogenesis process in which a baroclinic disturbance is transformed into a tropical cyclone. Many studies have analyzed TT events over the North Atlantic. This study assesses TT processes from a possible subtropical cyclone to Tropical Storm Kirogi at a relatively high latitude over the western North Pacific in an environment of enhanced baroclinicity in August 2012. Analyses based on satellite observations, the JRA-55 reanalysis, and a simulation with 2.5-km horizontal grid spacing demonstrate three stages during the TT: the baroclinic, intermediate, and convective stages. Over the baroclinic stage, Kirogi had an asymmetric comma-shaped cloud pattern with convection in the northern and eastern parts of the cyclone. This convection is attributed to quasigeostrophic forcing and frontogenesis associated with advection of warm and moist air. Vorticity locally generated by this convection was advected to the cyclone center by cyclone-relative northerly flow. Kirogi also had a shallow warm-core structure due to the interaction with an upper-level cold trough extending from the midlatitudes. In the intermediate stage, the warm and moist air in the lower troposphere and the cold trough in the upper troposphere wrapped around Kirogi. In the convective stage, Kirogi attained characteristics of a typical tropical cyclone with convection concentrated near the cyclone center and a deep warm-core structure. These results demonstrate that baroclinic processes can directly trigger formation of a tropical storm at relatively high latitudes over the western North Pacific in a similar manner to that over the North Atlantic.
Significance Statement
Tropical cyclogenesis is an important process for early identification of tropical cyclone hazards. Tropical transition is a tropical cyclogenesis process that is triggered by a subtropical or extratropical disturbance. It is unique to relatively high latitudes and has social importance particularly for midlatitude countries. There have been fewer studies on tropical transition over the western North Pacific than over the North Atlantic. This study demonstrates the dynamics of a distinct tropical transition event that led to the formation of Tropical Storm Kirogi (2012) at a relatively high latitude over the western North Pacific.
Abstract
Tropical transition (TT) is a cyclogenesis process in which a baroclinic disturbance is transformed into a tropical cyclone. Many studies have analyzed TT events over the North Atlantic. This study assesses TT processes from a possible subtropical cyclone to Tropical Storm Kirogi at a relatively high latitude over the western North Pacific in an environment of enhanced baroclinicity in August 2012. Analyses based on satellite observations, the JRA-55 reanalysis, and a simulation with 2.5-km horizontal grid spacing demonstrate three stages during the TT: the baroclinic, intermediate, and convective stages. Over the baroclinic stage, Kirogi had an asymmetric comma-shaped cloud pattern with convection in the northern and eastern parts of the cyclone. This convection is attributed to quasigeostrophic forcing and frontogenesis associated with advection of warm and moist air. Vorticity locally generated by this convection was advected to the cyclone center by cyclone-relative northerly flow. Kirogi also had a shallow warm-core structure due to the interaction with an upper-level cold trough extending from the midlatitudes. In the intermediate stage, the warm and moist air in the lower troposphere and the cold trough in the upper troposphere wrapped around Kirogi. In the convective stage, Kirogi attained characteristics of a typical tropical cyclone with convection concentrated near the cyclone center and a deep warm-core structure. These results demonstrate that baroclinic processes can directly trigger formation of a tropical storm at relatively high latitudes over the western North Pacific in a similar manner to that over the North Atlantic.
Significance Statement
Tropical cyclogenesis is an important process for early identification of tropical cyclone hazards. Tropical transition is a tropical cyclogenesis process that is triggered by a subtropical or extratropical disturbance. It is unique to relatively high latitudes and has social importance particularly for midlatitude countries. There have been fewer studies on tropical transition over the western North Pacific than over the North Atlantic. This study demonstrates the dynamics of a distinct tropical transition event that led to the formation of Tropical Storm Kirogi (2012) at a relatively high latitude over the western North Pacific.
Abstract
A new software framework using a well-established high-order spectral element discretization is presented for solving the compressible Navier–Stokes equations for purposes of research in atmospheric dynamics in bounded and unbounded limited-area domains, with a view toward capturing spatiotemporal intermittency that may be particularly challenging to attain using low-order schemes. A review of the discretization is provided, emphasizing properties such as the matrix product formalism and other design considerations that will facilitate its effective use on emerging exascale platforms, and a new geometry-independent, element boundary exchange method is described to maintain continuity. A variety of test problems are presented that demonstrate accuracy of the implementation primarily in wave-dominated or transitional flow regimes; conservation properties are also demonstrated. A strong scaling CPU study in a three-dimensional domain without using threading shows an average parallel efficiency of ≳99% up to 2 × 104 MPI tasks that is not affected negatively by expansion polynomial order. On-node performance is also examined and reveals that, while the primary numerical operations achieve their theoretical arithmetic intensity, the application performance is largely limited by available memory bandwidth.
Significance Statement
This work considers the need for computationally efficient, high-order, low dissipation numerics to fully leverage emerging exascale computing resources in an effort to examine and improve the accuracy of numerical treatments of atmospheric and weather phenomena. A new spectral element implementation is introduced that attempts to address the issues involved. Well-understood tests are presented that illustrate the known efficacy of the method in wave-dominated, quasi-laminar, and relatively strong shear flow regimes, and good conservation properties for mass and total energy are achieved. Importantly, the implementation is shown to exhibit encouraging performance characteristics.
Abstract
A new software framework using a well-established high-order spectral element discretization is presented for solving the compressible Navier–Stokes equations for purposes of research in atmospheric dynamics in bounded and unbounded limited-area domains, with a view toward capturing spatiotemporal intermittency that may be particularly challenging to attain using low-order schemes. A review of the discretization is provided, emphasizing properties such as the matrix product formalism and other design considerations that will facilitate its effective use on emerging exascale platforms, and a new geometry-independent, element boundary exchange method is described to maintain continuity. A variety of test problems are presented that demonstrate accuracy of the implementation primarily in wave-dominated or transitional flow regimes; conservation properties are also demonstrated. A strong scaling CPU study in a three-dimensional domain without using threading shows an average parallel efficiency of ≳99% up to 2 × 104 MPI tasks that is not affected negatively by expansion polynomial order. On-node performance is also examined and reveals that, while the primary numerical operations achieve their theoretical arithmetic intensity, the application performance is largely limited by available memory bandwidth.
Significance Statement
This work considers the need for computationally efficient, high-order, low dissipation numerics to fully leverage emerging exascale computing resources in an effort to examine and improve the accuracy of numerical treatments of atmospheric and weather phenomena. A new spectral element implementation is introduced that attempts to address the issues involved. Well-understood tests are presented that illustrate the known efficacy of the method in wave-dominated, quasi-laminar, and relatively strong shear flow regimes, and good conservation properties for mass and total energy are achieved. Importantly, the implementation is shown to exhibit encouraging performance characteristics.
Abstract
Multiple parallel rainbands (MPRBs) involve the organization of mesoscale convective systems (MCSs) characterized by multiple parallel convective rainbands, which may produce high rainfall accumulation. A total of 178 MPRBs were identified from 2016 to 2020 in China, which were classified into the initiation type (∼40%), where rainbands initiate individually, and differentiation type (∼60%), where rainbands form through the splitting of large rainbands or merging of smaller cells. Results showed that the occurrence frequency of MPRBs peaks in July with a midnight major peak and a morning minor peak. The highest occurrence frequency is observed in the northern Beibu Gulf and its coastal areas, with minor high frequencies in Guangdong, northern Jiangxi, and southern Shandong provinces, typically in a southwesterly low-level jet to the west of the subtropical high. MPRBs mainly contain 3–4 rainbands with a spacing distance of 30–50 km and an orientation generally consistent with the direction of 850-hPa winds and 0–1-km vertical wind shear. MPRBs generally move slower than that of squall lines in East China ranging from 4 to 8 m s−1 with 16% being quasi-stationary, which is mainly due to the occurrence of band back building mainly associated with cold pool. Most MPRBs have training effects with band training as the dominant mode. Because of the band training effect and slower movement of MPRBs mainly due to band back building, 71% of MPRBs are associated with enhanced maximum hourly rainfall. Rainfall severity may be alleviated somewhat by the generally short duration of MPRBs with 78% being shorter than 2 h.
Significance Statement
The purpose of this study is to document the general features of mesoscale convective systems (MCSs) with a specific organization of multiple parallel rainbands (MPRBs). MCSs with this unique organization tend to produce extremely heavy rainfall partly due to the training of multiple rainbands as well as their slow movement because of back building. The organization pattern of MPRBs was previously found in a case study. The possible formation mechanism was also previously examined based on case studies. As a complement to these studies, this work aims to reveal the temporal and spatial distributions, movement and duration, morphology, precipitation patterns, and environmental features of MPRBs in China based on statistics using 5-yr radar reflectivity data.
Abstract
Multiple parallel rainbands (MPRBs) involve the organization of mesoscale convective systems (MCSs) characterized by multiple parallel convective rainbands, which may produce high rainfall accumulation. A total of 178 MPRBs were identified from 2016 to 2020 in China, which were classified into the initiation type (∼40%), where rainbands initiate individually, and differentiation type (∼60%), where rainbands form through the splitting of large rainbands or merging of smaller cells. Results showed that the occurrence frequency of MPRBs peaks in July with a midnight major peak and a morning minor peak. The highest occurrence frequency is observed in the northern Beibu Gulf and its coastal areas, with minor high frequencies in Guangdong, northern Jiangxi, and southern Shandong provinces, typically in a southwesterly low-level jet to the west of the subtropical high. MPRBs mainly contain 3–4 rainbands with a spacing distance of 30–50 km and an orientation generally consistent with the direction of 850-hPa winds and 0–1-km vertical wind shear. MPRBs generally move slower than that of squall lines in East China ranging from 4 to 8 m s−1 with 16% being quasi-stationary, which is mainly due to the occurrence of band back building mainly associated with cold pool. Most MPRBs have training effects with band training as the dominant mode. Because of the band training effect and slower movement of MPRBs mainly due to band back building, 71% of MPRBs are associated with enhanced maximum hourly rainfall. Rainfall severity may be alleviated somewhat by the generally short duration of MPRBs with 78% being shorter than 2 h.
Significance Statement
The purpose of this study is to document the general features of mesoscale convective systems (MCSs) with a specific organization of multiple parallel rainbands (MPRBs). MCSs with this unique organization tend to produce extremely heavy rainfall partly due to the training of multiple rainbands as well as their slow movement because of back building. The organization pattern of MPRBs was previously found in a case study. The possible formation mechanism was also previously examined based on case studies. As a complement to these studies, this work aims to reveal the temporal and spatial distributions, movement and duration, morphology, precipitation patterns, and environmental features of MPRBs in China based on statistics using 5-yr radar reflectivity data.
Abstract
On 7 February 2020 a relatively deep cyclone (∼980 hPa) with midlevel frontogenesis produced heavy snow (20–30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud, while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) Model simulations at 2- and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24-h runs realistically produced a broad sloping region of frontogenesis at midlevels typically; however, there were relatively large (20%–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (<10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20%–30% have too little ice aloft and at low levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5%–10% more precipitation than the 2-km grid.
Significance Statement
Heavy snowfall from U.S. East Coast winter storms can cause major societal problems, yet few studies have investigated these storms using field observations and model data. This study focuses on the 7 February 2020 event, where 20–40 cm of snow fell over west-central New York. Our analysis shows a broad region of ascent, rather than a concentrated region favoring a well-defined snowband was the primary process contributing to snowfall. Last, model microphysics were validated within this storm using the in situ aircraft data. Errors in the snow generation aloft and snow growth at low levels likely contributed to the simulated surface precipitation underprediction, but most of the forecast uncertainty is from initial conditions for this short-term (∼24-h lead time) forecast.
Abstract
On 7 February 2020 a relatively deep cyclone (∼980 hPa) with midlevel frontogenesis produced heavy snow (20–30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud, while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) Model simulations at 2- and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24-h runs realistically produced a broad sloping region of frontogenesis at midlevels typically; however, there were relatively large (20%–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (<10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20%–30% have too little ice aloft and at low levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5%–10% more precipitation than the 2-km grid.
Significance Statement
Heavy snowfall from U.S. East Coast winter storms can cause major societal problems, yet few studies have investigated these storms using field observations and model data. This study focuses on the 7 February 2020 event, where 20–40 cm of snow fell over west-central New York. Our analysis shows a broad region of ascent, rather than a concentrated region favoring a well-defined snowband was the primary process contributing to snowfall. Last, model microphysics were validated within this storm using the in situ aircraft data. Errors in the snow generation aloft and snow growth at low levels likely contributed to the simulated surface precipitation underprediction, but most of the forecast uncertainty is from initial conditions for this short-term (∼24-h lead time) forecast.
Abstract
We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s Z DR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the Z DR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky.
Significance Statement
In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.
Abstract
We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s Z DR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the Z DR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky.
Significance Statement
In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.
Abstract
This study presents observational findings of air–sea turbulent heat flux anomalies during the onset of the South China Sea summer monsoon (SCSSM) in 2021 and explains the mechanism for high-resolution heat flux variations. Turbulent heat flux discrepancies are not uniform throughout the basin but indicate a significant regional disparity in the South China Sea (SCS), which also experiences evident year-to-year variability. Based on buoy- and cruise-based air–sea measurements, high-temporal-resolution (less than hourly) anomalies in the latent heat flux during the SCSSM burst are unexpectedly determined by sea–air humidity differences instead of wind effects under near-neutral and mixed marine atmospheric boundary layer (MABL) stability conditions. However, latent heat anomalies are mainly induced by wind speed under changing MABL conditions. The sensible heat flux is much weaker, with its anomalies dominated by sea–air temperature differences regardless of the boundary layer condition. The observational results are used to examine the discrepancies in turbulent heat fluxes and associated air–sea variables in reanalysis products. The comparisons indicate that latent and sensible heat fluxes in the reanalysis are overestimated by approximately 55 and 3 W m−2, respectively. These overestimations are mainly induced by higher estimates of sea–air humidity/temperature differences. The relative humidity is underestimated by approximately 4.2% in the two high-resolution reanalysis products. The higher SST (near-surface specific humidity) and lower air temperature (specific air humidity) eventually lead to higher estimates of sea–air humidity/temperature differences (1.75 g kg−1/0.25°C), which are the dominant factors controlling the variations in the air–sea turbulent heat fluxes.
Significance Statement
Air–sea interactions are significant in predicting the onset of East Asian monsoon systems, including the SCSSM. During the SCSSM in 2021, four buoys and cruise observations are used to investigate anomalies in the latent and sensible heat fluxes. The physical mechanism of the variations in turbulent heat fluxes under different MABL stability conditions is explored in this study. The humidity and wind speed anomalies play roles under mixed boundary conditions in determining the high-resolution variations in latent heat fluxes. Based on these observational results, the heat fluxes and associated air–sea variables from reanalysis products are compared to identify the differences in the operational systems. These comparison results can help improve the reanalysis to obtain better monsoon predictions.
Abstract
This study presents observational findings of air–sea turbulent heat flux anomalies during the onset of the South China Sea summer monsoon (SCSSM) in 2021 and explains the mechanism for high-resolution heat flux variations. Turbulent heat flux discrepancies are not uniform throughout the basin but indicate a significant regional disparity in the South China Sea (SCS), which also experiences evident year-to-year variability. Based on buoy- and cruise-based air–sea measurements, high-temporal-resolution (less than hourly) anomalies in the latent heat flux during the SCSSM burst are unexpectedly determined by sea–air humidity differences instead of wind effects under near-neutral and mixed marine atmospheric boundary layer (MABL) stability conditions. However, latent heat anomalies are mainly induced by wind speed under changing MABL conditions. The sensible heat flux is much weaker, with its anomalies dominated by sea–air temperature differences regardless of the boundary layer condition. The observational results are used to examine the discrepancies in turbulent heat fluxes and associated air–sea variables in reanalysis products. The comparisons indicate that latent and sensible heat fluxes in the reanalysis are overestimated by approximately 55 and 3 W m−2, respectively. These overestimations are mainly induced by higher estimates of sea–air humidity/temperature differences. The relative humidity is underestimated by approximately 4.2% in the two high-resolution reanalysis products. The higher SST (near-surface specific humidity) and lower air temperature (specific air humidity) eventually lead to higher estimates of sea–air humidity/temperature differences (1.75 g kg−1/0.25°C), which are the dominant factors controlling the variations in the air–sea turbulent heat fluxes.
Significance Statement
Air–sea interactions are significant in predicting the onset of East Asian monsoon systems, including the SCSSM. During the SCSSM in 2021, four buoys and cruise observations are used to investigate anomalies in the latent and sensible heat fluxes. The physical mechanism of the variations in turbulent heat fluxes under different MABL stability conditions is explored in this study. The humidity and wind speed anomalies play roles under mixed boundary conditions in determining the high-resolution variations in latent heat fluxes. Based on these observational results, the heat fluxes and associated air–sea variables from reanalysis products are compared to identify the differences in the operational systems. These comparison results can help improve the reanalysis to obtain better monsoon predictions.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA, and, surprisingly, we find that it depends directly on the number of independent observations but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Significance Statement
The dampening of long-range correlations has been the key to the success of ensemble data assimilation in global numerical weather prediction. In this paper, we show how noise in covariance estimates propagates through the state estimation process and corrupts state estimates. We show that this noise results in a bias and that this bias depends on the number of observations and not, as might be expected, on the state dimension. We go on to show how dampening long-range covariances through a process referred to as “localization” helps to mitigate the detrimental effects of this sampling noise.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA, and, surprisingly, we find that it depends directly on the number of independent observations but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Significance Statement
The dampening of long-range correlations has been the key to the success of ensemble data assimilation in global numerical weather prediction. In this paper, we show how noise in covariance estimates propagates through the state estimation process and corrupts state estimates. We show that this noise results in a bias and that this bias depends on the number of observations and not, as might be expected, on the state dimension. We go on to show how dampening long-range covariances through a process referred to as “localization” helps to mitigate the detrimental effects of this sampling noise.
