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Abstract
The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis.
Significance Statement
Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.
Abstract
The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis.
Significance Statement
Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.
Abstract
An afternoon heavy rainfall event occurred over coastal Nantong, an area of 70–100 km downstream from the Shanghai–Suzhou–Wuxi–Changzhou city belt over the Yangtze River Delta, under the influences of weak southwesterly monsoonal flows and lake/sea breezes on 26 July 2018. An observational analysis shows the emergence of pronounced urban heat island (UHI) effects along the city belt during the late morning hours. A series of nested-grid cloud-permitting model simulations with the finest grid spacing of 1 km are performed to examine the impacts of urbanization on convection initiation (CI) and the subsequent heavy rainfall event. Results reveal the generation of lake breezes and warm anomalies in the planetary boundary layer along the city belt and low-level convergence, thereby inducing upward motion for CI. The southwesterly flows of the monsoonal warm–moist air, enhanced by the UHI effects along the city belt, allow the development of convective cells along the city belt, some of which merge with convective clusters during their downstream propagation, and the ultimate generation of several distinct heavy rainfall centers by local convective clusters over coastal Nantong where atmospheric columns are more moist and potentially unstable under the influences of sea breezes. Sensitivity simulations show small contribution of Nantong’s UHI effects to the heavy rainfall event. The above findings help elucidate how the UHI effects could assist the CI in a weak-gradient environment, and explain why urbanization can contribute to increased downwind mean and extreme precipitation under the influences of favorable regional forcing conditions.
Significance Statement
The urban heat island (UHI) effects tend to produce more rainfall on its downwind side than that on the other sides, but alone could hardly account for the generation of heavy rainfall. This study examines the influences of the UHI effects associated with a city belt over the Yangtze River Delta on generating an afternoon heavy rainfall event over coastal Nantong that is 70–100 km downwind from the city belt. Results show (i) the downwind advection of the UHI effects; (ii) the initiation of convective storms along the city belt, and their subsequent downstream propagation, leading to the generation of heavy rainfall over Nantong; and (iii) an important role of sea breezes in generating the heavy rainfall event.
Abstract
An afternoon heavy rainfall event occurred over coastal Nantong, an area of 70–100 km downstream from the Shanghai–Suzhou–Wuxi–Changzhou city belt over the Yangtze River Delta, under the influences of weak southwesterly monsoonal flows and lake/sea breezes on 26 July 2018. An observational analysis shows the emergence of pronounced urban heat island (UHI) effects along the city belt during the late morning hours. A series of nested-grid cloud-permitting model simulations with the finest grid spacing of 1 km are performed to examine the impacts of urbanization on convection initiation (CI) and the subsequent heavy rainfall event. Results reveal the generation of lake breezes and warm anomalies in the planetary boundary layer along the city belt and low-level convergence, thereby inducing upward motion for CI. The southwesterly flows of the monsoonal warm–moist air, enhanced by the UHI effects along the city belt, allow the development of convective cells along the city belt, some of which merge with convective clusters during their downstream propagation, and the ultimate generation of several distinct heavy rainfall centers by local convective clusters over coastal Nantong where atmospheric columns are more moist and potentially unstable under the influences of sea breezes. Sensitivity simulations show small contribution of Nantong’s UHI effects to the heavy rainfall event. The above findings help elucidate how the UHI effects could assist the CI in a weak-gradient environment, and explain why urbanization can contribute to increased downwind mean and extreme precipitation under the influences of favorable regional forcing conditions.
Significance Statement
The urban heat island (UHI) effects tend to produce more rainfall on its downwind side than that on the other sides, but alone could hardly account for the generation of heavy rainfall. This study examines the influences of the UHI effects associated with a city belt over the Yangtze River Delta on generating an afternoon heavy rainfall event over coastal Nantong that is 70–100 km downwind from the city belt. Results show (i) the downwind advection of the UHI effects; (ii) the initiation of convective storms along the city belt, and their subsequent downstream propagation, leading to the generation of heavy rainfall over Nantong; and (iii) an important role of sea breezes in generating the heavy rainfall event.
Abstract
Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s−1, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.
Significance Statement
Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.
Abstract
Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s−1, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.
Significance Statement
Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.
Abstract
Accurate predictions of the location and timing of convection initiation (CI) remain a challenge, even in high-resolution convection-allowing models (CAMs). Many of the processes necessary for daytime CI are rooted in the planetary boundary layer (PBL), which numerical models struggle to accurately predict. To improve ensemble forecasts of the PBL and subsequent CI forecasts in CAM ensembles, we explore the use of underused data from both the GOES-16 satellite and the national network of WSR-88D radars. The GOES-16 satellite provides observations of brightness temperature (BT) to better analyze cloud structures, while the WSR-88D radars provide PBL height estimates and clear-air radial wind velocity observations to better analyze PBL structures. The CAM uses the Advanced Research Weather Research and Forecasting (WRF-ARW) Model at 3-km horizontal grid spacing. The ensemble consists of 40 members and observations are assimilated using the Gridpoint Statistical Interpolation (GSI) ensemble Kalman filter (EnKF) system. To evaluate the influence of each observation type on CI, conventional, WSR-88D, and GOES-16 observations are assimilated separately and jointly over a 4-h period and the resulting ensemble analyses and forecasts are compared with available observations for a CI event on 18 May 2018. Results show that the addition of the WSR-88D and GOES-16 observations improves the CI forecasts out several hours in terms of timing and location for this case.
Significance Statement
The location and timing of new thunderstorm development is an important component of severe weather forecasts. Yet the prediction of thunderstorm development in weather prediction models remains challenging. This study explores using a combination of underused satellite and radar observations to better define the atmospheric state used to start the weather prediction models, with the hope that this will lead to better forecasts of new thunderstorm development. Results show that underused observations from routinely available Doppler weather radars and a geostationary satellite, all of which are currently available, can work synergistically to improve forecasts of the location and timing of severe thunderstorm development.
Abstract
Accurate predictions of the location and timing of convection initiation (CI) remain a challenge, even in high-resolution convection-allowing models (CAMs). Many of the processes necessary for daytime CI are rooted in the planetary boundary layer (PBL), which numerical models struggle to accurately predict. To improve ensemble forecasts of the PBL and subsequent CI forecasts in CAM ensembles, we explore the use of underused data from both the GOES-16 satellite and the national network of WSR-88D radars. The GOES-16 satellite provides observations of brightness temperature (BT) to better analyze cloud structures, while the WSR-88D radars provide PBL height estimates and clear-air radial wind velocity observations to better analyze PBL structures. The CAM uses the Advanced Research Weather Research and Forecasting (WRF-ARW) Model at 3-km horizontal grid spacing. The ensemble consists of 40 members and observations are assimilated using the Gridpoint Statistical Interpolation (GSI) ensemble Kalman filter (EnKF) system. To evaluate the influence of each observation type on CI, conventional, WSR-88D, and GOES-16 observations are assimilated separately and jointly over a 4-h period and the resulting ensemble analyses and forecasts are compared with available observations for a CI event on 18 May 2018. Results show that the addition of the WSR-88D and GOES-16 observations improves the CI forecasts out several hours in terms of timing and location for this case.
Significance Statement
The location and timing of new thunderstorm development is an important component of severe weather forecasts. Yet the prediction of thunderstorm development in weather prediction models remains challenging. This study explores using a combination of underused satellite and radar observations to better define the atmospheric state used to start the weather prediction models, with the hope that this will lead to better forecasts of new thunderstorm development. Results show that underused observations from routinely available Doppler weather radars and a geostationary satellite, all of which are currently available, can work synergistically to improve forecasts of the location and timing of severe thunderstorm development.
Abstract
In this paper we present the derivation of two new forms of the Kalman filter equations; the first is for a pure lognormally distributed random variable, while the second set of Kalman filter equations will be for a combination of Gaussian and lognormally distributed random variables. We show that the appearance is similar to that of the Gaussian-based equations, but that the analysis state is a multivariate median and not the mean. We also show results of the mixed distribution Kalman filter with the Lorenz 1963 model with lognormal errors for the background and observations of the z component, and compare them to analysis results from a traditional Gaussian-based extended Kalman filter and show that under certain circumstances the new approach produces more accurate results.
Abstract
In this paper we present the derivation of two new forms of the Kalman filter equations; the first is for a pure lognormally distributed random variable, while the second set of Kalman filter equations will be for a combination of Gaussian and lognormally distributed random variables. We show that the appearance is similar to that of the Gaussian-based equations, but that the analysis state is a multivariate median and not the mean. We also show results of the mixed distribution Kalman filter with the Lorenz 1963 model with lognormal errors for the background and observations of the z component, and compare them to analysis results from a traditional Gaussian-based extended Kalman filter and show that under certain circumstances the new approach produces more accurate results.
Abstract
In this study, all-sky GOES-R ABI infrared radiances at their native resolution are assimilated using an enhanced GSI ensemble Kalman filter (EnKF) data assimilation (DA) system, and the impacts of the data on the analysis and forecast of a mesoscale convective system (MCS) are explored. Results show that all-sky ABI BT data can correctly build up observed storms within the model and effectively remove spurious storms in model background through frequent DA cycles. Both bias and root-mean-squared innovation of the background and analysis are significantly reduced during the DA cycles, and free forecasts are improved when verified subjectively and objectively against observed ABI BTs and independent radar reflectivity observations. A horizontal localization radius of 30 km is found to produce the best results while 5-min DA cycles improve the storm analyses over 15-min cycles, but the differences in forecasts are small. Further analyses show that the clearing of spurious clouds by ABI radiance is correctly accompanied by reduction in moisture through background error cross covariance, but overdrying often occurs, which can cause spurious storm decay in the forecast. The problem is reduced when the ensemble mean of observation prior instead of observation prior of the ensemble mean state is used in the ensemble mean state update equation of EnKF. The significant difference between the two ways that the ensemble mean of observation prior is calculated when the observational operator is very nonlinear has not been recognized in earlier cloudy radiance DA studies.
Significance Statement
Satellite observations in cloudy regions are not used in most current operational weather prediction systems due to complex nonlinear relations between satellite-observed quantities, the radiances, and model state in such regions. The models also must predict clouds reasonably well for cloudy observations to be effectively assimilated. The latest NOAA geostationary satellites can provide radiance observations at high spatial and temporal resolutions and such data in both cloudy- and clear-air regions are assimilated using an advanced data assimilation method into a model that explicitly represents convection. Forecasts up to 4 h are improved by the assimilation while several issues associated with the assimilation are discussed. The study contributes to the eventual use of all-sky satellite radiance data in operational models.
Abstract
In this study, all-sky GOES-R ABI infrared radiances at their native resolution are assimilated using an enhanced GSI ensemble Kalman filter (EnKF) data assimilation (DA) system, and the impacts of the data on the analysis and forecast of a mesoscale convective system (MCS) are explored. Results show that all-sky ABI BT data can correctly build up observed storms within the model and effectively remove spurious storms in model background through frequent DA cycles. Both bias and root-mean-squared innovation of the background and analysis are significantly reduced during the DA cycles, and free forecasts are improved when verified subjectively and objectively against observed ABI BTs and independent radar reflectivity observations. A horizontal localization radius of 30 km is found to produce the best results while 5-min DA cycles improve the storm analyses over 15-min cycles, but the differences in forecasts are small. Further analyses show that the clearing of spurious clouds by ABI radiance is correctly accompanied by reduction in moisture through background error cross covariance, but overdrying often occurs, which can cause spurious storm decay in the forecast. The problem is reduced when the ensemble mean of observation prior instead of observation prior of the ensemble mean state is used in the ensemble mean state update equation of EnKF. The significant difference between the two ways that the ensemble mean of observation prior is calculated when the observational operator is very nonlinear has not been recognized in earlier cloudy radiance DA studies.
Significance Statement
Satellite observations in cloudy regions are not used in most current operational weather prediction systems due to complex nonlinear relations between satellite-observed quantities, the radiances, and model state in such regions. The models also must predict clouds reasonably well for cloudy observations to be effectively assimilated. The latest NOAA geostationary satellites can provide radiance observations at high spatial and temporal resolutions and such data in both cloudy- and clear-air regions are assimilated using an advanced data assimilation method into a model that explicitly represents convection. Forecasts up to 4 h are improved by the assimilation while several issues associated with the assimilation are discussed. The study contributes to the eventual use of all-sky satellite radiance data in operational models.
Abstract
Convection-allowing model (CAM) ensemble forecasts provide quantitative probabilistic guidance of convective hazards that forecasters would otherwise qualitatively assess. Various initial condition (IC) strategies can be used to generate CAM probabilistic forecasts, but it is still unclear how different configurations perform. Schwartz et al. verified five 10-member IC CAM ensembles over one month of 0000 UTC initializations with a focus on precipitation. Here, we initialize four 42-member IC CAM ensembles every 12 h over 6 weeks and verify forecasts of precipitation, column maximum reflectivity, and hourly maximum updraft helicity. The Texas Tech University real-time EnKF ensemble and three additional IC ensemble modeling systems are verified. Holding the model configuration constant, additional ICs are generated by downscaling time-lagged Global Ensemble Forecast System (GEFS) members, applying correlated random noise to Global Forecast System (GFS) analyses, and recentering EnKF perturbations about GFS analyses. We found that ensemble ICs constructed with correlated random noise and EnKF perturbations about GFS analyses both produced higher-quality precipitation forecasts than downscaled GEFS and EnKF strategies. However, downscaled GEFS and EnKF perturbations about GFS analyses frequently initialized more skillful forecasts of reflectivity than ICs with random perturbations, suggesting that flow-dependent perturbations are important for forecasting deep convection. Even with a suboptimal EnKF configuration, our findings still echo those of Schwartz et al. We extend their work by exploring 1) verification of additional convective hazards and 2) empirical scaling of IC perturbations as a computationally inexpensive method for improving CAM ensemble forecasts.
Abstract
Convection-allowing model (CAM) ensemble forecasts provide quantitative probabilistic guidance of convective hazards that forecasters would otherwise qualitatively assess. Various initial condition (IC) strategies can be used to generate CAM probabilistic forecasts, but it is still unclear how different configurations perform. Schwartz et al. verified five 10-member IC CAM ensembles over one month of 0000 UTC initializations with a focus on precipitation. Here, we initialize four 42-member IC CAM ensembles every 12 h over 6 weeks and verify forecasts of precipitation, column maximum reflectivity, and hourly maximum updraft helicity. The Texas Tech University real-time EnKF ensemble and three additional IC ensemble modeling systems are verified. Holding the model configuration constant, additional ICs are generated by downscaling time-lagged Global Ensemble Forecast System (GEFS) members, applying correlated random noise to Global Forecast System (GFS) analyses, and recentering EnKF perturbations about GFS analyses. We found that ensemble ICs constructed with correlated random noise and EnKF perturbations about GFS analyses both produced higher-quality precipitation forecasts than downscaled GEFS and EnKF strategies. However, downscaled GEFS and EnKF perturbations about GFS analyses frequently initialized more skillful forecasts of reflectivity than ICs with random perturbations, suggesting that flow-dependent perturbations are important for forecasting deep convection. Even with a suboptimal EnKF configuration, our findings still echo those of Schwartz et al. We extend their work by exploring 1) verification of additional convective hazards and 2) empirical scaling of IC perturbations as a computationally inexpensive method for improving CAM ensemble forecasts.
Abstract
Covariance localization has been the key to the success of ensemble data assimilation in high dimensional problems, especially in global numerical weather prediction. We review and synthesize optimal and adaptive localization methods that are rooted in sampling error theory and that are defined by optimality criteria, e.g., minimizing errors in forecast covariances or in the Kalman gain. As an immediate result, we note that all optimal localization methods follow a universal law and are indeed quite similar. We confirm the similarity of the various schemes in idealized numerical experiments, where we observe that all localization schemes we test—optimal and nonadaptive schemes—perform quite similarly in a wide array of problems. We explain this perhaps surprising finding with mathematical rigor on an idealized class of problems, first put forward by Bickel and others to study the collapse of particle filters. In combination, the numerical experiments and the theory show that the most important attribute of a localization scheme is the well-known property that one should dampen spurious long-range correlations. The details of the correlation structure, and whether or not these details are used to construct the localization, have a much smaller effect on posterior state errors.
Significance Statement
Covariance localization has been the key to the success of ensemble data assimilation (DA) in global numerical weather prediction. In this paper, we synthesize a large body of literature on optimal localization and then report and explain a number of surprising observations.
Abstract
Covariance localization has been the key to the success of ensemble data assimilation in high dimensional problems, especially in global numerical weather prediction. We review and synthesize optimal and adaptive localization methods that are rooted in sampling error theory and that are defined by optimality criteria, e.g., minimizing errors in forecast covariances or in the Kalman gain. As an immediate result, we note that all optimal localization methods follow a universal law and are indeed quite similar. We confirm the similarity of the various schemes in idealized numerical experiments, where we observe that all localization schemes we test—optimal and nonadaptive schemes—perform quite similarly in a wide array of problems. We explain this perhaps surprising finding with mathematical rigor on an idealized class of problems, first put forward by Bickel and others to study the collapse of particle filters. In combination, the numerical experiments and the theory show that the most important attribute of a localization scheme is the well-known property that one should dampen spurious long-range correlations. The details of the correlation structure, and whether or not these details are used to construct the localization, have a much smaller effect on posterior state errors.
Significance Statement
Covariance localization has been the key to the success of ensemble data assimilation (DA) in global numerical weather prediction. In this paper, we synthesize a large body of literature on optimal localization and then report and explain a number of surprising observations.
Abstract
Over half of the total rainfall and more than 70% of heavy and extreme rainfall in the Melbourne, Australia, region occurs on days with linearly organized precipitation. These systems are typically convective in nature and frequently associated with cold fronts. It is useful to understand the processes that support extreme rainfall in organized convection, for prediction of both near-term and future extreme rainfall, and the topography and climate of Melbourne are different from many of the regions where QLCSs have been studied more extensively (e.g., the U. S. Great Plains region). On both 7 and 8 December 2010, a QLCS passed through the Melbourne region. Both QLCSs resembled classic systems on radar, but heavy rainfall was much more widespread on the second day. The goals of this work are to 1) understand the processes that drive these seemingly similar QLCSs; 2) explore the relationship between the convective inflow layer and moisture sources; and 3) to better understand the characteristics of rain bearing systems in the Melbourne region, which have received little attention to date. A convection-permitting WRF-ARW simulation captures both events. The mesoscale structure is different in each case, but generally is explainable by the existing theory. The development of a mesoscale downdraft, along with more moisture (and CAPE) over a deeper layer, contributed to higher rainfall totals on the second day. Low-level moisture in the QLCS region comes from the east, and parcel trajectories become increasingly westerly with height. On the second day some parcels originate in the tropics; these tend to have the most moisture.
Significance Statement
A lot of the rain that falls in Melbourne, Australia, occurs in storms that are grouped or “organized” in the shape of a line. Many studies have looked at how lines of storms work in other places in the world. In southeast Australia, only one study in the 1980s looked at observations of a line of storms. Since then, our understanding of storms and our ability to use computer models to simulate them has improved considerably. In this study we simulate two lines of storms that happened two days in a row. We found that even though they looked similar on weather radar, they had many differences including how air flows through the storm, the role of rain-cooled air, and where moisture comes from.
Abstract
Over half of the total rainfall and more than 70% of heavy and extreme rainfall in the Melbourne, Australia, region occurs on days with linearly organized precipitation. These systems are typically convective in nature and frequently associated with cold fronts. It is useful to understand the processes that support extreme rainfall in organized convection, for prediction of both near-term and future extreme rainfall, and the topography and climate of Melbourne are different from many of the regions where QLCSs have been studied more extensively (e.g., the U. S. Great Plains region). On both 7 and 8 December 2010, a QLCS passed through the Melbourne region. Both QLCSs resembled classic systems on radar, but heavy rainfall was much more widespread on the second day. The goals of this work are to 1) understand the processes that drive these seemingly similar QLCSs; 2) explore the relationship between the convective inflow layer and moisture sources; and 3) to better understand the characteristics of rain bearing systems in the Melbourne region, which have received little attention to date. A convection-permitting WRF-ARW simulation captures both events. The mesoscale structure is different in each case, but generally is explainable by the existing theory. The development of a mesoscale downdraft, along with more moisture (and CAPE) over a deeper layer, contributed to higher rainfall totals on the second day. Low-level moisture in the QLCS region comes from the east, and parcel trajectories become increasingly westerly with height. On the second day some parcels originate in the tropics; these tend to have the most moisture.
Significance Statement
A lot of the rain that falls in Melbourne, Australia, occurs in storms that are grouped or “organized” in the shape of a line. Many studies have looked at how lines of storms work in other places in the world. In southeast Australia, only one study in the 1980s looked at observations of a line of storms. Since then, our understanding of storms and our ability to use computer models to simulate them has improved considerably. In this study we simulate two lines of storms that happened two days in a row. We found that even though they looked similar on weather radar, they had many differences including how air flows through the storm, the role of rain-cooled air, and where moisture comes from.
Abstract
In the classical model of mesoscale convective systems (MCSs), a system generates new convective cells on the downshear side of its cold pool, with the cells fed at low levels from the front, and the stratiform cloud trailing behind the system in the upshear direction, where “front” and “behind” typically refer to the system’s ground-relative velocity. In this study we present an algorithm for identifying and tracking MCSs in radar reflectivity data, and objectively diagnosing organizational characteristics related to the classical model, namely, the offset of stratiform cloud from convective cloud relative to system velocity, the low-level inflow direction, and the shear-relative tilt and propagation directions. When applied to the 15-yr radar record covering the Darwin region of northern Australia, the algorithm indicates that 65%–80% of MCS observations are consistent with the classical model, at least when the four classifications can be made unambiguously. However, these observed characteristics occur almost entirely in the drier phases of the Australian monsoon. During the humid, active monsoon phase, observed characteristics consistent with the classical model are rare, and most systems exhibit nonclassical upshear propagation.
Significance Statement
In this study we developed a computer program for tracking large storms in radar data. The program tracks storms through time and space, recording their three-dimensional structure. Knowledge of this structure allows our program to automatically put storms into different, commonly used categories. These categories provide clues about the processes that can make storms grow, persist, or decay. Our computer program is useful because the classification process is very time consuming when done by humans. Also, when applied to radar datasets from northern Australia, our program suggests most large storms are consistent with standard theories of what make storms grow and persist. However, when the air is very humid, large storms inconsistent with standard theories become common.
Abstract
In the classical model of mesoscale convective systems (MCSs), a system generates new convective cells on the downshear side of its cold pool, with the cells fed at low levels from the front, and the stratiform cloud trailing behind the system in the upshear direction, where “front” and “behind” typically refer to the system’s ground-relative velocity. In this study we present an algorithm for identifying and tracking MCSs in radar reflectivity data, and objectively diagnosing organizational characteristics related to the classical model, namely, the offset of stratiform cloud from convective cloud relative to system velocity, the low-level inflow direction, and the shear-relative tilt and propagation directions. When applied to the 15-yr radar record covering the Darwin region of northern Australia, the algorithm indicates that 65%–80% of MCS observations are consistent with the classical model, at least when the four classifications can be made unambiguously. However, these observed characteristics occur almost entirely in the drier phases of the Australian monsoon. During the humid, active monsoon phase, observed characteristics consistent with the classical model are rare, and most systems exhibit nonclassical upshear propagation.
Significance Statement
In this study we developed a computer program for tracking large storms in radar data. The program tracks storms through time and space, recording their three-dimensional structure. Knowledge of this structure allows our program to automatically put storms into different, commonly used categories. These categories provide clues about the processes that can make storms grow, persist, or decay. Our computer program is useful because the classification process is very time consuming when done by humans. Also, when applied to radar datasets from northern Australia, our program suggests most large storms are consistent with standard theories of what make storms grow and persist. However, when the air is very humid, large storms inconsistent with standard theories become common.
