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Abstract
To understand why convection initiation and heavy rain sometimes occur ahead of fronts over South China in the presummer rainy season but sometimes do not, a climatology of 137 fronts is constructed, in which 34% of the fronts exhibit no prefrontal convection initiation (NoPCI), 31% of the fronts exhibit prefrontal convection initiation (PCI), and 35% of the fronts exhibit prefrontal convection initiation and heavy rain (PCI+HR). An anticyclonically curved upper-level jet streak and midtropospheric QG forcing produce synoptic-scale descent for the prefrontal region in NoPCI events, whereas the right-entrance region of a straight upper-level jet streak and forcing for ascent dominate the prefrontal region in PCI and PCI+HR events. Whether prefrontal convection and heavy rain occur is also related to the character of low-level flows. NoPCI features anticyclonic southerly winds, with an environment having low dewpoint throughout the troposphere, unfavorable for convection initiation. However, synoptic circulation of PCI and PCI+HR events favors a broad prefrontal surface low, which determines the greater cyclonic character of airflows in PCI+HR events, in contrast with that of the PCI events. Convective available potential energy is useful in distinguishing NoPCI and PCI events, and the three events have statistically significant differences in precipitable water. Moreover, larger magnitudes of precipitable water and bulk wind shear in PCI+HR events are conducive for prefrontal convection to produce heavy rain compared to those of PCI events. These results indicate the importance of the upper-level forcing on the prefrontal convection initiation, and heavy rain is sensitive to the changes in prefrontal airflow and moisture.
Abstract
To understand why convection initiation and heavy rain sometimes occur ahead of fronts over South China in the presummer rainy season but sometimes do not, a climatology of 137 fronts is constructed, in which 34% of the fronts exhibit no prefrontal convection initiation (NoPCI), 31% of the fronts exhibit prefrontal convection initiation (PCI), and 35% of the fronts exhibit prefrontal convection initiation and heavy rain (PCI+HR). An anticyclonically curved upper-level jet streak and midtropospheric QG forcing produce synoptic-scale descent for the prefrontal region in NoPCI events, whereas the right-entrance region of a straight upper-level jet streak and forcing for ascent dominate the prefrontal region in PCI and PCI+HR events. Whether prefrontal convection and heavy rain occur is also related to the character of low-level flows. NoPCI features anticyclonic southerly winds, with an environment having low dewpoint throughout the troposphere, unfavorable for convection initiation. However, synoptic circulation of PCI and PCI+HR events favors a broad prefrontal surface low, which determines the greater cyclonic character of airflows in PCI+HR events, in contrast with that of the PCI events. Convective available potential energy is useful in distinguishing NoPCI and PCI events, and the three events have statistically significant differences in precipitable water. Moreover, larger magnitudes of precipitable water and bulk wind shear in PCI+HR events are conducive for prefrontal convection to produce heavy rain compared to those of PCI events. These results indicate the importance of the upper-level forcing on the prefrontal convection initiation, and heavy rain is sensitive to the changes in prefrontal airflow and moisture.
Abstract
The Gridpoint Statistical Interpolation (GSI)-based four- and three-dimensional ensemble–variational (4DEnVar and 3DEnVar) methods are compared as a smoother and filter, respectively, for rapidly changing storms using the convective-scale direct radar reflectivity data assimilation (DA) framework. Two sets of experiments with varying DA window lengths (WLs; 20, 40, 100, and 160 min) and radar observation intervals (RIs; 20 and 5 min) are conducted for the 5–6 May 2019 case. The RI determines the temporal resolution of ensemble perturbations for the smoother and the DA interval for the filter spanning the WL. For experiments with a 20-min RI, evaluations suggest that filter and smoother have comparable performance with a 20-min WL; however, extending the WL results in the outperformance of filter over smoother. Diagnostics reveal that the degradation of smoother is attributed to the increased degree of nonlinearity and the issue of time-independent localization as the WL extends. Evaluations for experiments with different RIs under the same WL indicate that the outperformance of filter over smoother diminishes for most forecast hours at thresholds of 30 dBZ and above when shortening the RI. Diagnostics show that more frequent interruptions of the model introduce model imbalance for the filter, and the increased temporal resolution of ensemble perturbations enhances the degree of nonlinearity for the smoother. The impact of model imbalance on the filter overwhelms the enhanced nonlinearity on the smoother as the RI reduces.
Abstract
The Gridpoint Statistical Interpolation (GSI)-based four- and three-dimensional ensemble–variational (4DEnVar and 3DEnVar) methods are compared as a smoother and filter, respectively, for rapidly changing storms using the convective-scale direct radar reflectivity data assimilation (DA) framework. Two sets of experiments with varying DA window lengths (WLs; 20, 40, 100, and 160 min) and radar observation intervals (RIs; 20 and 5 min) are conducted for the 5–6 May 2019 case. The RI determines the temporal resolution of ensemble perturbations for the smoother and the DA interval for the filter spanning the WL. For experiments with a 20-min RI, evaluations suggest that filter and smoother have comparable performance with a 20-min WL; however, extending the WL results in the outperformance of filter over smoother. Diagnostics reveal that the degradation of smoother is attributed to the increased degree of nonlinearity and the issue of time-independent localization as the WL extends. Evaluations for experiments with different RIs under the same WL indicate that the outperformance of filter over smoother diminishes for most forecast hours at thresholds of 30 dBZ and above when shortening the RI. Diagnostics show that more frequent interruptions of the model introduce model imbalance for the filter, and the increased temporal resolution of ensemble perturbations enhances the degree of nonlinearity for the smoother. The impact of model imbalance on the filter overwhelms the enhanced nonlinearity on the smoother as the RI reduces.
Abstract
The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the “training line” systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics.
Significance Statement
Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia’s current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases.
Abstract
The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the “training line” systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics.
Significance Statement
Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia’s current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases.
Abstract
The radius of maximum wind (Rmax ), an important parameter in tropical cyclones (TCs) ocean surface wind structure, is currently resolved by only a few sensors, so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semi-empirical model relying on an outer wind radius, intensity and latitude was fit to best-track data. In this study we revise this semi-empirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an inter-calibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010-2020 and yields Rmax reanalyses and trends more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.
Abstract
The radius of maximum wind (Rmax ), an important parameter in tropical cyclones (TCs) ocean surface wind structure, is currently resolved by only a few sensors, so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semi-empirical model relying on an outer wind radius, intensity and latitude was fit to best-track data. In this study we revise this semi-empirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an inter-calibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010-2020 and yields Rmax reanalyses and trends more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.
Abstract
The devastating winds in extratropical cyclones can be assigned to different mesoscale flows. How these strong winds are transported to the surface is discussed for the Mediterranean windstorm Adrian (Vaia), which caused extensive damage in Corsica in October 2018. A mesoscale analysis based on a kilometer-scale simulation with the Meso-NH model shows that the strongest winds come from a cold conveyor belt (CCB). The focus then shifts to a large-eddy simulation (LES) for which the strongest winds over the sea are located in a convective boundary layer. Convection is organized into coherent turbulent structures in the form of convective rolls. It is their downward branches that contribute most to the nonlocal transport of strong winds from the CCB to the surface layer. On landing, the convective rolls break up because of the complex topography of Corsica. Sensitivity experiments to horizontal grid spacing show similar organization of boundary layer rolls across the resolution. A comparative analysis of the kinetic energy spectra suggests that a grid spacing of 200 m is sufficient to represent the vertical transport of strong winds through convective rolls. Contrary to LES, convective rolls are not resolved in the kilometer-scale simulation and surface winds are overestimated due to excessive momentum transport. These results highlight the importance of convective rolls for the generation of surface wind gusts and the need to better represent them in boundary layer parameterizations.
Abstract
The devastating winds in extratropical cyclones can be assigned to different mesoscale flows. How these strong winds are transported to the surface is discussed for the Mediterranean windstorm Adrian (Vaia), which caused extensive damage in Corsica in October 2018. A mesoscale analysis based on a kilometer-scale simulation with the Meso-NH model shows that the strongest winds come from a cold conveyor belt (CCB). The focus then shifts to a large-eddy simulation (LES) for which the strongest winds over the sea are located in a convective boundary layer. Convection is organized into coherent turbulent structures in the form of convective rolls. It is their downward branches that contribute most to the nonlocal transport of strong winds from the CCB to the surface layer. On landing, the convective rolls break up because of the complex topography of Corsica. Sensitivity experiments to horizontal grid spacing show similar organization of boundary layer rolls across the resolution. A comparative analysis of the kinetic energy spectra suggests that a grid spacing of 200 m is sufficient to represent the vertical transport of strong winds through convective rolls. Contrary to LES, convective rolls are not resolved in the kilometer-scale simulation and surface winds are overestimated due to excessive momentum transport. These results highlight the importance of convective rolls for the generation of surface wind gusts and the need to better represent them in boundary layer parameterizations.
Abstract
This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts.
Significance Statement
Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.
Abstract
This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts.
Significance Statement
Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.
Abstract
This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the northern and southern hemispheres respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
Abstract
This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the northern and southern hemispheres respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
Abstract
Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed.
Abstract
Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed.
Abstract
We present the formulation and optimization of a Runge-Kutta-type time-stepping scheme for solving the shallow water equations, aimed at substantially increasing the effective allowable time-step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward-backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann-type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time-steps up to 2.8 times as large as a popular three-stage, third-order strong stability preserving Runge-Kutta method in a quasi-linear test case. In fully nonlinear shallow water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time-step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality.
Abstract
We present the formulation and optimization of a Runge-Kutta-type time-stepping scheme for solving the shallow water equations, aimed at substantially increasing the effective allowable time-step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward-backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann-type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time-steps up to 2.8 times as large as a popular three-stage, third-order strong stability preserving Runge-Kutta method in a quasi-linear test case. In fully nonlinear shallow water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time-step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality.
Abstract
The scientific community has long acknowledged the importance of high-temporal resolution radar observations to advance science research and improve high-impact weather prediction. Development of innovative rapid-scan radar technologies over the past two decades has enabled radar volume scans of 10–60 s, compared to 3–5 min with traditional parabolic dish research radars and the WSR-88D radar network. This review examines the impact of rapid-scan radar technology, defined as radars collecting volume scans in 1 min or less, on atmospheric science research spanning different subdisciplines and evaluates the strengths and weaknesses of the use of rapid-scan radars. In particular, a significant body of literature has accumulated for tornado and severe thunderstorm research and forecasting applications, in addition to a growing number of studies of convection. Convection research has benefited substantially from more synchronous vertical views, but could benefit more substantially by leveraging multi-Doppler wind retrievals and complementary in-situ and remote sensors. In addition, several years of forecast evaluation studies are synthesized from radar testbed experiments, and the benefits of assimilating rapid-scan radar observations are analyzed. Although the current body of literature reflects the considerable utility of rapid-scan radars to science research, a weakness is that limited advancements in understanding of the physical mechanisms behind observed features have been enabled. There is considerable opportunity to bridge the gap in physical understanding with the current technology using coordinated efforts to include rapid-scan radars in field campaigns and expanding the breadth of meteorological phenomena studied.
Abstract
The scientific community has long acknowledged the importance of high-temporal resolution radar observations to advance science research and improve high-impact weather prediction. Development of innovative rapid-scan radar technologies over the past two decades has enabled radar volume scans of 10–60 s, compared to 3–5 min with traditional parabolic dish research radars and the WSR-88D radar network. This review examines the impact of rapid-scan radar technology, defined as radars collecting volume scans in 1 min or less, on atmospheric science research spanning different subdisciplines and evaluates the strengths and weaknesses of the use of rapid-scan radars. In particular, a significant body of literature has accumulated for tornado and severe thunderstorm research and forecasting applications, in addition to a growing number of studies of convection. Convection research has benefited substantially from more synchronous vertical views, but could benefit more substantially by leveraging multi-Doppler wind retrievals and complementary in-situ and remote sensors. In addition, several years of forecast evaluation studies are synthesized from radar testbed experiments, and the benefits of assimilating rapid-scan radar observations are analyzed. Although the current body of literature reflects the considerable utility of rapid-scan radars to science research, a weakness is that limited advancements in understanding of the physical mechanisms behind observed features have been enabled. There is considerable opportunity to bridge the gap in physical understanding with the current technology using coordinated efforts to include rapid-scan radars in field campaigns and expanding the breadth of meteorological phenomena studied.
