Diana R. Stovern, Thomas M. Hamill, Lesley L. Smith
Abstract This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h) −1 . Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
{"title":"Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part II: Results","authors":"Diana R. Stovern, Thomas M. Hamill, Lesley L. Smith","doi":"10.1175/mwr-d-22-0310.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0310.1","url":null,"abstract":"Abstract This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h) −1 . Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135061152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christoforus Bayu Risanto, J. Moker, A. Arellano, C. Castro, Y. Serra, T. Luong, D. Adams
Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA), 2) kinematics via modification of cumulus parameterization, and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research Forecasting (WRF) model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain-Fritsch scheme and double moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.
{"title":"On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain","authors":"Christoforus Bayu Risanto, J. Moker, A. Arellano, C. Castro, Y. Serra, T. Luong, D. Adams","doi":"10.1175/mwr-d-22-0221.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0221.1","url":null,"abstract":"\u0000Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA), 2) kinematics via modification of cumulus parameterization, and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research Forecasting (WRF) model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain-Fritsch scheme and double moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44237947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scott D. Loeffler, M. Kumjian, P. Markowski, Brice E Coffer, M. Parker
The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR) and specific differential phase (KDP) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.
{"title":"Investigating the relationship between polarimetric radar signatures of hydrometeor size sorting and tornadic potential in simulated supercells","authors":"Scott D. Loeffler, M. Kumjian, P. Markowski, Brice E Coffer, M. Parker","doi":"10.1175/mwr-d-22-0228.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0228.1","url":null,"abstract":"\u0000The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR) and specific differential phase (KDP) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43669375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.
{"title":"Exploring the Value of a High-Precision Targeted Observation Strategy for Mobile Radiosonde Deployment","authors":"Isaac Arseneau, B. Ancell","doi":"10.1175/mwr-d-22-0352.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0352.1","url":null,"abstract":"\u0000Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42171826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the conditional nonlinear optimal perturbation (CNOP) approach, the predictability of mei-yu heavy precipitation and its underlying physical processes is investigated. As an extension of our previous work, the practical predictability of heavy precipitation events is studied using more realistic initial perturbations than previously considered. The initial perturbation reflects certain physical connections among multiple variables including zonal and meridional winds, potential temperature (T), and water vapor mixing ratio (Q). Two types of initial perturbations for the CNOP are identified, with similar spatial distributions but opposite signs and resulting effects. The accumulated precipitation is strengthened with mostly positive perturbations in the T and Q components for the CNOP, and weakened by negative perturbations. Comparing downscaling (DOWN) perturbations and random perturbations (RPs) with the CNOP, it is found that the CNOP and DOWN perturbations exhibit particularly large- and mesoscale spatial structures, respectively, while the RPs yield a spatial distribution with mostly convective-scale features. Also, the CNOP results in the largest error growth and forecast uncertainty, especially for Q, followed by the DOWN perturbations, and then those in the RPs are the smallest. These results provide important implications for optimizing the initial perturbations of convection-permitting ensemble prediction systems, especially precipitation forecasts. Moreover, it is suggested that small-scale related variables, i.e., those associated with vertical motion and microphysical processes, are much less predictable than thermodynamic variables, and the errors grow through distinct physical processes for the three types of initial perturbations, i.e., with flow-dependent features.
{"title":"Influence of Physically Constrained Initial Perturbations on the Predictability of Mei-Yu Heavy Precipitation","authors":"Jiaying Ke, M. Mu, X. Fang","doi":"10.1175/mwr-d-22-0302.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0302.1","url":null,"abstract":"\u0000Based on the conditional nonlinear optimal perturbation (CNOP) approach, the predictability of mei-yu heavy precipitation and its underlying physical processes is investigated. As an extension of our previous work, the practical predictability of heavy precipitation events is studied using more realistic initial perturbations than previously considered. The initial perturbation reflects certain physical connections among multiple variables including zonal and meridional winds, potential temperature (T), and water vapor mixing ratio (Q). Two types of initial perturbations for the CNOP are identified, with similar spatial distributions but opposite signs and resulting effects. The accumulated precipitation is strengthened with mostly positive perturbations in the T and Q components for the CNOP, and weakened by negative perturbations. Comparing downscaling (DOWN) perturbations and random perturbations (RPs) with the CNOP, it is found that the CNOP and DOWN perturbations exhibit particularly large- and mesoscale spatial structures, respectively, while the RPs yield a spatial distribution with mostly convective-scale features. Also, the CNOP results in the largest error growth and forecast uncertainty, especially for Q, followed by the DOWN perturbations, and then those in the RPs are the smallest. These results provide important implications for optimizing the initial perturbations of convection-permitting ensemble prediction systems, especially precipitation forecasts. Moreover, it is suggested that small-scale related variables, i.e., those associated with vertical motion and microphysical processes, are much less predictable than thermodynamic variables, and the errors grow through distinct physical processes for the three types of initial perturbations, i.e., with flow-dependent features.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48740292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anders Jensen, G. Thompson, K. Ikeda, S. Tessendorf
Methods to improve the representation of hail in the Thompson-Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson-Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson-Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper-levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (> 2-inch diameter) hail that was observed during this case.
{"title":"Improving the representation of hail in the Thompson microphysics scheme","authors":"Anders Jensen, G. Thompson, K. Ikeda, S. Tessendorf","doi":"10.1175/mwr-d-21-0319.1","DOIUrl":"https://doi.org/10.1175/mwr-d-21-0319.1","url":null,"abstract":"\u0000Methods to improve the representation of hail in the Thompson-Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson-Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson-Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper-levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (> 2-inch diameter) hail that was observed during this case.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44900974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimating and predicting the state of the atmosphere is a probabilistic problem, and often employs an ensemble modeling approach to represent uncertainty in the system. Common methods for examining uncertainty and assessing performance for ensembles emphasize pointwise statistics or marginal distributions. However, these methods lose specific information about individual ensemble members. This paper explores contour band depth (cBD), a method of analyzing uncertainty in terms of contours of scalar fields. cBD is fully nonparametric and induces an ordering on ensemble members that leads to box-and-whisker-plot-type visualizations of uncertainty for two-dimensional data. By applying cBD to synthetic ensembles, we demonstrate that it provides enhanced information about the spatial structure of ensemble uncertainty. We also find that the usefulness of the cBD analysis depends on the presence of multiple modes and multiple scales in the ensemble of contours. Finally, we apply cBD to compare various convection-permitting forecasts from different ensemble prediction systems, and find that the value it provides in real-world applications compared to standard analysis methods exhibits clear limitations. In some cases, contour boxplots can provide deeper insight into differences in spatial characteristics between the different ensemble forecasts. Nevertheless, identification of outliers using cBD is not always intuitive, and the method can be especially challenging to implement for flow that exhibits multiple spatial scales; e.g., discrete convective cells embedded within a mesoscale weather system.
{"title":"Evaluating Contour Band Depth as a Method for Understanding Ensemble Uncertainty","authors":"Henry Santer, J. Poterjoy, Joshua McCurry","doi":"10.1175/mwr-d-22-0281.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0281.1","url":null,"abstract":"\u0000Estimating and predicting the state of the atmosphere is a probabilistic problem, and often employs an ensemble modeling approach to represent uncertainty in the system. Common methods for examining uncertainty and assessing performance for ensembles emphasize pointwise statistics or marginal distributions. However, these methods lose specific information about individual ensemble members. This paper explores contour band depth (cBD), a method of analyzing uncertainty in terms of contours of scalar fields. cBD is fully nonparametric and induces an ordering on ensemble members that leads to box-and-whisker-plot-type visualizations of uncertainty for two-dimensional data. By applying cBD to synthetic ensembles, we demonstrate that it provides enhanced information about the spatial structure of ensemble uncertainty. We also find that the usefulness of the cBD analysis depends on the presence of multiple modes and multiple scales in the ensemble of contours. Finally, we apply cBD to compare various convection-permitting forecasts from different ensemble prediction systems, and find that the value it provides in real-world applications compared to standard analysis methods exhibits clear limitations. In some cases, contour boxplots can provide deeper insight into differences in spatial characteristics between the different ensemble forecasts. Nevertheless, identification of outliers using cBD is not always intuitive, and the method can be especially challenging to implement for flow that exhibits multiple spatial scales; e.g., discrete convective cells embedded within a mesoscale weather system.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48028846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-minute intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to formtracks. This process is performed for twelve 4-day sample windows in the months May-August of 2017-2019. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2-3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 16-17 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.
{"title":"Lifetimes of Overshooting Convective Events using High-Frequency Gridded Radar Composites","authors":"Daniel Jellis, K. Bowman, A. Rapp","doi":"10.1175/mwr-d-23-0032.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0032.1","url":null,"abstract":"\u0000Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-minute intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to formtracks. This process is performed for twelve 4-day sample windows in the months May-August of 2017-2019. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2-3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 16-17 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44138279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juanzhen Sun, Rumeng Li, Qinghong Zhang, S. Trier, Zhuming Ying, Jun Xu
The purpose of this study is to diagnose mesoscale factors responsible for the formation and development of an extreme rainstorm that occurred on 20 July 2021 in Zhengzhou, China. The rainstorm produced 201.9 mm rainfall in one hour, breaking the record of mainland China for 1-h rainfall accumulation in the past 73 years. Using 2-km continuously cycled analyses with 6-min updates that were produced by assimilating observations from radar and dense surface networks with a four-dimensional variational (4DVar) data assimilation system, we illustrate that the modification of environmental easterlies by three mesoscale disturbances played a critical role in the development of the rainstorm. Among the three systems, a meso-beta-scale low pressure system (mesolow) that developed from an inverted trough southwest of Zhengzhou was key to the formation and intensification of the rainstorm. We show that the rainstorm formed via sequential merging of three convective cells, which initiated along the convergence bands in the mesolow. Further, we present evidence to suggest that the mesolow and two terrain-influenced flows near the Taihang mountains north of Zhengzhou, including a barrier jet and a downslope flow, contributed to the local intensification of the rainstorm and the intense 1-h rainfall. The three mesoscale features co-existed near Zhengzhou in the several hours before the extreme one-hour rainfall and enhanced local wind convergence and moisture transport synergistically. Our analysis also indicated that the strong midlevel south/southwesterly winds from the mesolow along with the gravity-current-modified low-level northeasterly barrier jet enhanced the vertical wind shear, which provided favorable local environment supporting the severe rainstorm.
{"title":"Mesoscale factors contributing to the extreme rainstorm on 20 July 2021 in Zhengzhou, China as revealed by rapid update 4DVar analysis","authors":"Juanzhen Sun, Rumeng Li, Qinghong Zhang, S. Trier, Zhuming Ying, Jun Xu","doi":"10.1175/mwr-d-22-0337.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0337.1","url":null,"abstract":"\u0000The purpose of this study is to diagnose mesoscale factors responsible for the formation and development of an extreme rainstorm that occurred on 20 July 2021 in Zhengzhou, China. The rainstorm produced 201.9 mm rainfall in one hour, breaking the record of mainland China for 1-h rainfall accumulation in the past 73 years. Using 2-km continuously cycled analyses with 6-min updates that were produced by assimilating observations from radar and dense surface networks with a four-dimensional variational (4DVar) data assimilation system, we illustrate that the modification of environmental easterlies by three mesoscale disturbances played a critical role in the development of the rainstorm. Among the three systems, a meso-beta-scale low pressure system (mesolow) that developed from an inverted trough southwest of Zhengzhou was key to the formation and intensification of the rainstorm. We show that the rainstorm formed via sequential merging of three convective cells, which initiated along the convergence bands in the mesolow. Further, we present evidence to suggest that the mesolow and two terrain-influenced flows near the Taihang mountains north of Zhengzhou, including a barrier jet and a downslope flow, contributed to the local intensification of the rainstorm and the intense 1-h rainfall. The three mesoscale features co-existed near Zhengzhou in the several hours before the extreme one-hour rainfall and enhanced local wind convergence and moisture transport synergistically. Our analysis also indicated that the strong midlevel south/southwesterly winds from the mesolow along with the gravity-current-modified low-level northeasterly barrier jet enhanced the vertical wind shear, which provided favorable local environment supporting the severe rainstorm.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44325735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Majumdar, L. Magnusson, P. Bechtold, J. Bidlot, J. Doyle
Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (“EC4”) with upgraded moist physics is compared against a 9-km version (“EC9”) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS-TC system (“CO4”) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4 resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum wind (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36 h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure-wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses a global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex (for TCs with initial intensity ≥55 kt) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid-upper-tropospheric heating.
{"title":"Advanced tropical cyclone prediction using the experimental global ECMWF and operational regional COAMPS-TC systems","authors":"S. Majumdar, L. Magnusson, P. Bechtold, J. Bidlot, J. Doyle","doi":"10.1175/mwr-d-22-0236.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0236.1","url":null,"abstract":"\u0000Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (“EC4”) with upgraded moist physics is compared against a 9-km version (“EC9”) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS-TC system (“CO4”) to compare systems with similar resolutions.\u0000EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4 resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum wind (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36 h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure-wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses a global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex (for TCs with initial intensity ≥55 kt) based on data provided by forecasters.\u0000Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid-upper-tropospheric heating.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44342840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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