This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s−1).
本研究描述了对实时热带气旋(TC)飞机侦察观测资料的自动分析，以估计TC地面风。风分析采用迭代、客观、数据加权的分析方法，在径向和方位角方向上具有不同的平滑约束。当解决方案偏离平滑分析并将飞机信息扩展到未直接观察到的区域时，平滑约束会惩罚数据不拟合。该分析综合了分析时间前5小时和分析时间后3小时内风暴运动后的观测资料，并利用规定的方法将观测资料移至共同飞行高度(CFL;700 hpa)进行分析，减少对地面的侦察观测。对几个观测和模拟风场的分析比较表明，分析结果与观测结果吻合，同时将观测信息扩展到观测不足的地区。然而，随着观测范围的减小，结果分析倾向于更大的对称性，并显示出对未观测半径内的第一次猜测信息的敏感性。分析可以对风场的重要操作特性作出合理而有用的估计。但是，由于典型飞机侦察飞行的径向和方面角平滑以及采样不足，极大风值被低估，最大风半径被略微高估。利用基于模型的合成飞机观测改变观测覆盖范围，这些分析随着观测覆盖范围的增加而改善，对于典型的观测模式(穿过风暴的两个断面)，均方根误差偏差< 10 kt (< 5 m s - 1)。
{"title":"An Automated Method to Analyze Tropical Cyclone Surface Winds from Real-time Aircraft Reconnaissance Observations","authors":"J. Knaff, C. Slocum","doi":"10.1175/waf-d-23-0077.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0077.1","url":null,"abstract":"\u0000This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s−1).","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"39 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593555","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}
On the local afternoon of 29 May 2012, a long-lived, right-moving (RM) supercell formed over northwestern Oklahoma and turned roughly southeastward. For >3 h, as it moved toward the Oklahoma City metro area, this supercell remained nontornadic and visually high-based, producing a nearly tornadic gustnado and a swath of significantly severe, sometimes giant hail up to 5 in (12.7 cm) in diameter. Meanwhile, a left-moving (LM) supercell formed over southwestern Oklahoma about 100 mi (161 km) south-southwest of the RM storm, and moved northeastward, with a rear-flank gust front that became well-defined on radar imagery as the LM storm approached southern and central parts of the metro. The authors, who had been observing the RM supercell in the field since genesis, surmised its potential future interaction with the LM storm’s trailing gust front about 1 h beforehand. We repositioned to near the gust front’s extrapolated collision point with the RM mesocyclone, in anticipation of maximized tornado potential, then witnessed a small tornado from the RM mesocyclone immediately following its interception of the boundary. Synchronized radar and photographic images of this remarkable sequence are presented and discussed in context of more recent findings on tornadic supercell/boundary interactions, with implications for operational utility.
{"title":"Right-Moving Supercell Tornadogenesis during Interaction with a Left-Moving Supercell’s Rear-Flank Outflow","authors":"Roger Edwards, Richard L. Thompson","doi":"10.1175/waf-d-23-0075.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0075.1","url":null,"abstract":"\u0000On the local afternoon of 29 May 2012, a long-lived, right-moving (RM) supercell formed over northwestern Oklahoma and turned roughly southeastward. For >3 h, as it moved toward the Oklahoma City metro area, this supercell remained nontornadic and visually high-based, producing a nearly tornadic gustnado and a swath of significantly severe, sometimes giant hail up to 5 in (12.7 cm) in diameter. Meanwhile, a left-moving (LM) supercell formed over southwestern Oklahoma about 100 mi (161 km) south-southwest of the RM storm, and moved northeastward, with a rear-flank gust front that became well-defined on radar imagery as the LM storm approached southern and central parts of the metro. The authors, who had been observing the RM supercell in the field since genesis, surmised its potential future interaction with the LM storm’s trailing gust front about 1 h beforehand. We repositioned to near the gust front’s extrapolated collision point with the RM mesocyclone, in anticipation of maximized tornado potential, then witnessed a small tornado from the RM mesocyclone immediately following its interception of the boundary. Synchronized radar and photographic images of this remarkable sequence are presented and discussed in context of more recent findings on tornadic supercell/boundary interactions, with implications for operational utility.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" 22","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616845","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}
A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s−1), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal). Near the leading edge of thunderstorm lines, areas of rotation that can produce tornadoes and strong winds (“mesovortices”) often develop rapidly. Despite advances in understanding mesovortices, few operational guidelines exist to anticipate their genesis. One popular method used to forecast mesovortices—the “Three Ingredients Method”—is evaluated in this study. Our work confirms the importance of two of the ingredients—a surge of outflow winds and thunderstorms that stay nearly atop the leading edge of the outflow. However, we find that many mesovortices occur below the threshold of low-level wind shear ascribed by the forecast method. Refinements to the method are suggested, including the favorable distance between the leading edge of the outflow and thunderstorm updrafts and lower bounds of wind shear over multiple layers, below which mesovortices may be unlikely.
{"title":"Using Radiosonde Observations to Assess the “Three Ingredients Method” to Forecast QLCS Mesovortices","authors":"Max D. Ungar, M. Coniglio","doi":"10.1175/waf-d-22-0176.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0176.1","url":null,"abstract":"\u0000A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s−1), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal).\u0000\u0000\u0000Near the leading edge of thunderstorm lines, areas of rotation that can produce tornadoes and strong winds (“mesovortices”) often develop rapidly. Despite advances in understanding mesovortices, few operational guidelines exist to anticipate their genesis. One popular method used to forecast mesovortices—the “Three Ingredients Method”—is evaluated in this study. Our work confirms the importance of two of the ingredients—a surge of outflow winds and thunderstorms that stay nearly atop the leading edge of the outflow. However, we find that many mesovortices occur below the threshold of low-level wind shear ascribed by the forecast method. Refinements to the method are suggested, including the favorable distance between the leading edge of the outflow and thunderstorm updrafts and lower bounds of wind shear over multiple layers, below which mesovortices may be unlikely.\u0000","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"2 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138623808","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}
Kelsey C. Britt, P. Skinner, P. Heinselman, C. Potvin, Montgomery Flora, B. Matilla, K. Knopfmeier, Anthony E. Reinhart
Quasi-linear convective systems (QLCSs) can produce multiple hazards (e.g., straight-line winds, flash flooding, and mesovortex tornadoes) that pose a significant threat to life and property, and are often difficult to accurately forecast. The NSSL Warn-on-Forecast System (WoFS) is a convection-allowing ensemble system developed to provide short-term, probabilistic forecasting guidance for severe convective events. Examination of WoFS’s capability to predict QLCSs has yet to be systematically assessed across a large number of cases for 0–6-hr forecast times. In this study, the quality of WoFS QLCS forecasts for 50 QLCS days occurring between 2017–2020 is evaluated using object-based verification techniques. First, a storm mode identification and classification algorithm is tuned to identify high-reflectivity, linear convective structures. The algorithm is used to identify convective line objects in WoFS forecasts and Multi-Radar Multi-Sensor system (MRMS) gridded observations. WoFS QLCS objects are matched with MRMS observed objects to generate bulk verification statistics. Results suggest WoFS’s QLCS forecasts are skillful with the 3- and 6-hr forecasts having similar probability of detection and false alarm ratio values near 0.59 and 0.34, respectively. The WoFS objects are larger, more intense, and less eccentric than those in MRMS. A novel centerline analysis is performed to evaluate orientation, length, and tortuosity (i.e., curvature) differences, and spatial displacements between observed and predicted convective lines. While no systematic propagation biases are found, WoFS typically has centerlines that are more tortuous and displaced to the northwest of MRMS centerlines, suggesting WoFS may be overforecasting the intensity of the QLCS’s rear-inflow jet and northern bookend vortex.
{"title":"Verification of Quasi-Linear Convective Systems Predicted by the Warn-on-Forecast System (WoFS)","authors":"Kelsey C. Britt, P. Skinner, P. Heinselman, C. Potvin, Montgomery Flora, B. Matilla, K. Knopfmeier, Anthony E. Reinhart","doi":"10.1175/waf-d-23-0106.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0106.1","url":null,"abstract":"Quasi-linear convective systems (QLCSs) can produce multiple hazards (e.g., straight-line winds, flash flooding, and mesovortex tornadoes) that pose a significant threat to life and property, and are often difficult to accurately forecast. The NSSL Warn-on-Forecast System (WoFS) is a convection-allowing ensemble system developed to provide short-term, probabilistic forecasting guidance for severe convective events. Examination of WoFS’s capability to predict QLCSs has yet to be systematically assessed across a large number of cases for 0–6-hr forecast times. In this study, the quality of WoFS QLCS forecasts for 50 QLCS days occurring between 2017–2020 is evaluated using object-based verification techniques. First, a storm mode identification and classification algorithm is tuned to identify high-reflectivity, linear convective structures. The algorithm is used to identify convective line objects in WoFS forecasts and Multi-Radar Multi-Sensor system (MRMS) gridded observations. WoFS QLCS objects are matched with MRMS observed objects to generate bulk verification statistics. Results suggest WoFS’s QLCS forecasts are skillful with the 3- and 6-hr forecasts having similar probability of detection and false alarm ratio values near 0.59 and 0.34, respectively. The WoFS objects are larger, more intense, and less eccentric than those in MRMS. A novel centerline analysis is performed to evaluate orientation, length, and tortuosity (i.e., curvature) differences, and spatial displacements between observed and predicted convective lines. While no systematic propagation biases are found, WoFS typically has centerlines that are more tortuous and displaced to the northwest of MRMS centerlines, suggesting WoFS may be overforecasting the intensity of the QLCS’s rear-inflow jet and northern bookend vortex.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"75 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139214978","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}
The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology towards novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the non-resolved processes, i.e. usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parametrizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier-Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g. artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.
{"title":"Perspectives towards stochastic and learned-by-data turbulence in Numerical Weather Prediction","authors":"M. Shapkalijevski","doi":"10.1175/waf-d-22-0228.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0228.1","url":null,"abstract":"The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology towards novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the non-resolved processes, i.e. usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parametrizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier-Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g. artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"28 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139213340","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}
Jordan Clark, Charles E. Konrad, Andrew Grundstein
Heat is the leading cause of weather-related death in the United States. Wet bulb globe temperature (WBGT) is a heat stress index commonly used among active populations for activity modification, such as outdoor workers and athletes. Despite widespread use globally, WBGT forecasts have been uncommon in the United States until recent years. This research assesses the accuracy of WBGT forecasts developed by NOAA’s Southeast Regional Climate Center (SERCC) and the Carolinas Integrated Sciences and Assessments (CISA). It also details efforts to refine the forecast by accounting for the impact of surface roughness on wind using satellite imagery. Comparisons are made between the SERCC/CISA WBGT forecast and a WBGT forecast modeled after NWS methods. Additionally, both of these forecasts are compared with in situ WBGT measurements (during the summers of 2019-2021) and estimates from weather stations to assess forecast accuracy. The SERCC/CISA WBGT forecast was within 0.6°C of observations on average and showed less bias than the forecast based on NWS methods across North Carolina. Importantly, the SERCC/CISA WBGT forecast was more accurate for the most dangerous conditions (WBGT > 31°C), although this resulted in higher false alarms for these extreme conditions compared to the NWS method. In particular, this work improved the forecast for sites more sheltered from wind by better accounting for the influences of land cover on 2-meter wind speed. Accurate forecasts are more challenging for sites with complex microclimates. Thus, appropriate caution is necessary when interpreting forecasts and onsite, real-time WBGT measurements remain critical.
{"title":"The Development and Accuracy Assessment of Wet Bulb Globe Temperature Forecasts","authors":"Jordan Clark, Charles E. Konrad, Andrew Grundstein","doi":"10.1175/waf-d-23-0076.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0076.1","url":null,"abstract":"Heat is the leading cause of weather-related death in the United States. Wet bulb globe temperature (WBGT) is a heat stress index commonly used among active populations for activity modification, such as outdoor workers and athletes. Despite widespread use globally, WBGT forecasts have been uncommon in the United States until recent years. This research assesses the accuracy of WBGT forecasts developed by NOAA’s Southeast Regional Climate Center (SERCC) and the Carolinas Integrated Sciences and Assessments (CISA). It also details efforts to refine the forecast by accounting for the impact of surface roughness on wind using satellite imagery. Comparisons are made between the SERCC/CISA WBGT forecast and a WBGT forecast modeled after NWS methods. Additionally, both of these forecasts are compared with in situ WBGT measurements (during the summers of 2019-2021) and estimates from weather stations to assess forecast accuracy. The SERCC/CISA WBGT forecast was within 0.6°C of observations on average and showed less bias than the forecast based on NWS methods across North Carolina. Importantly, the SERCC/CISA WBGT forecast was more accurate for the most dangerous conditions (WBGT > 31°C), although this resulted in higher false alarms for these extreme conditions compared to the NWS method. In particular, this work improved the forecast for sites more sheltered from wind by better accounting for the influences of land cover on 2-meter wind speed. Accurate forecasts are more challenging for sites with complex microclimates. Thus, appropriate caution is necessary when interpreting forecasts and onsite, real-time WBGT measurements remain critical.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211617","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}
Sarah M. Griffin, Anthony Wimmers, Christopher S. Velden
This study details a two-method, machine-learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), ‘D-MINT’ and ‘D-PRINT’. D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic, and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-hour lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, North Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally-used RI guidances, but less skillful for yes-no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions.
{"title":"Predicting Short-term Intensity change in Tropical Cyclones using a Convolutional Neural Network","authors":"Sarah M. Griffin, Anthony Wimmers, Christopher S. Velden","doi":"10.1175/waf-d-23-0085.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0085.1","url":null,"abstract":"This study details a two-method, machine-learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), ‘D-MINT’ and ‘D-PRINT’. D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic, and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-hour lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, North Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally-used RI guidances, but less skillful for yes-no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"24 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139214847","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}
This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. Thirty-two widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40-60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.
{"title":"Assessing RRFS vs. HRRR in Predicting Widespread Convective Systems over Eastern CONUS","authors":"Joseph A. Grim, James O. Pinto, David C. Dowell","doi":"10.1175/waf-d-23-0112.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0112.1","url":null,"abstract":"This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. Thirty-two widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40-60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"226 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253937","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}
P. Heinselman, P. Burke, Louis J. Wicker, Adam J. Clark, J. Kain, Jidong Gao, N. Yussouf, Thomas A. Jones, P. Skinner, C. Potvin, Katie A. Wilson, Burkely T. Gallo, Montgomery Flora, Joshua Martin, Gerry Creager, K. Knopfmeier, Yunheng Wang, B. Matilla, David C. Dowell, E. Mansell, Brett Roberts, K. Hoogewind, Derek R. Stratman, Jorge E. Guerra, Anthony E. Reinhart, Christopher A. Kerr, William J. S. Miller
In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a Warn-on-Detection to a Warn-on-Forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the Warn-on-Forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models, 2) ensemble- and variational- based assimilation of weather radar, satellite, and conventional observations, and 3) unique post-processing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the Watch-to-Warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.
{"title":"Warn-on-Forecast System: From Vision to Reality","authors":"P. Heinselman, P. Burke, Louis J. Wicker, Adam J. Clark, J. Kain, Jidong Gao, N. Yussouf, Thomas A. Jones, P. Skinner, C. Potvin, Katie A. Wilson, Burkely T. Gallo, Montgomery Flora, Joshua Martin, Gerry Creager, K. Knopfmeier, Yunheng Wang, B. Matilla, David C. Dowell, E. Mansell, Brett Roberts, K. Hoogewind, Derek R. Stratman, Jorge E. Guerra, Anthony E. Reinhart, Christopher A. Kerr, William J. S. Miller","doi":"10.1175/waf-d-23-0147.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0147.1","url":null,"abstract":"In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a Warn-on-Detection to a Warn-on-Forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the Warn-on-Forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models, 2) ensemble- and variational- based assimilation of weather radar, satellite, and conventional observations, and 3) unique post-processing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the Watch-to-Warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256182","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}
In this study, we evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to forecast cloud characteristics through comparison of observed and simulated satellite brightness temperatures (BTs) and radar reflectivity during different weather phenomena in December 2021: the Mayfield, KY tornado on 11 Dec, a heavy snow event in Minnesota from 10-11 Dec, and the Midwest Derecho on 15 Dec. This is done to illustrate the importance of examining model accuracy across a range of weather phenomena. Observation and forecast objects were created using the Method for Object-Based Diagnostic Evaluation (MODE). HRRR accurately depicted the spatial displacements between observation cloud (defined using BTs) and radar reflectivity objects, namely the centers of cloud objects are to the east of the radar objects for the tornado and derecho events, and generally west of the radar objects for the snow event. However, HRRR had higher (less intense) simulated BTs and higher (more intense) radar reflectivity than the observations for the tornado event. Simulated radar reflectivity is higher and BTs are lower than the observations during the middle of the snow event. Also, simulated radar reflectivity is higher and BTs are lower than the observations during the derecho event. Of the three weather events, the HRRR forecasts are most accurate for the snow event, based on the Object-based Threat Score, followed by the derecho and tornado events. The tornado event has lower accuracy because matches between paired simulated and observation objects are worse than for the snow event, with less similarity in size forecast objects and greater distance between paired object centers.
{"title":"Methods for Validating HRRR Simulated Cloud Properties for Different Weather Phenomena using Satellite and Radar Observations","authors":"S. M. Griffin, J. Otkin, William E. Lewis","doi":"10.1175/waf-d-23-0109.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0109.1","url":null,"abstract":"In this study, we evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to forecast cloud characteristics through comparison of observed and simulated satellite brightness temperatures (BTs) and radar reflectivity during different weather phenomena in December 2021: the Mayfield, KY tornado on 11 Dec, a heavy snow event in Minnesota from 10-11 Dec, and the Midwest Derecho on 15 Dec. This is done to illustrate the importance of examining model accuracy across a range of weather phenomena. Observation and forecast objects were created using the Method for Object-Based Diagnostic Evaluation (MODE). HRRR accurately depicted the spatial displacements between observation cloud (defined using BTs) and radar reflectivity objects, namely the centers of cloud objects are to the east of the radar objects for the tornado and derecho events, and generally west of the radar objects for the snow event. However, HRRR had higher (less intense) simulated BTs and higher (more intense) radar reflectivity than the observations for the tornado event. Simulated radar reflectivity is higher and BTs are lower than the observations during the middle of the snow event. Also, simulated radar reflectivity is higher and BTs are lower than the observations during the derecho event. Of the three weather events, the HRRR forecasts are most accurate for the snow event, based on the Object-based Threat Score, followed by the derecho and tornado events. The tornado event has lower accuracy because matches between paired simulated and observation objects are worse than for the snow event, with less similarity in size forecast objects and greater distance between paired object centers.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"28 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139265736","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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