Andrew C. Winters, N. Bassill, J. Gyakum, J. Minder
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation, and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, a surface cyclone climatology, and hourly station observations from Montréal, Québec and Burlington, VT, during October–April 2000–2018 to investigate the spectrum of synoptic-scale weather regimes that induce cold season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a Central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. GEFSv12 reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
圣劳伦斯河谷在寒冷季节会出现多种降水类型（p-type），如降雨、冻雨、冰粒和降雪。这些不同的降水类型对航空、公路运输、发电和配电以及冬季娱乐活动产生了相当大的影响，并由与该地区复杂地形相互作用的各种多尺度过程形成。本研究利用ERA5再分析数据、地表气旋气候学以及2000年10月至2018年4月期间魁北克省蒙特利尔市和弗吉尼亚州伯灵顿市的每小时观测站观测数据，研究了诱发圣劳伦斯河谷冷季降水的各种同步尺度天气机制。其中，K-均值聚类和自组织地图（SOM）用于将经过圣劳伦斯河谷附近的气旋轨迹及其伴随的热动力剖面划分为一系列事件类型，包括美国东海岸轨迹、美国中部轨迹和两个加拿大飓风轨迹。随后进行综合分析，以揭示每种事件类型最常伴随的同步尺度环境和特征 p 型。然后使用 GEFSv12 重新预测来检验气旋特征的相对可预测性，以及在 0-5 天预报提前期与每种事件类型相关的本地热动力剖面。分析表明，圣劳伦斯河谷附近的预报气旋发展过快，而且相对于再分析的平均值偏离了轨道，这对气温接近 0℃时整个地区的当地热动力剖面和 p 型的预报产生了影响。
{"title":"Regime-Dependent Characteristics and Predictability of Cold Season Precipitation Events in the St. Lawrence River Valley","authors":"Andrew C. Winters, N. Bassill, J. Gyakum, J. Minder","doi":"10.1175/waf-d-23-0218.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0218.1","url":null,"abstract":"\u0000The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation, and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, a surface cyclone climatology, and hourly station observations from Montréal, Québec and Burlington, VT, during October–April 2000–2018 to investigate the spectrum of synoptic-scale weather regimes that induce cold season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a Central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. GEFSv12 reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. García‐Franco, Chia-Ying Lee, Suzana J. Camargo, Michael K. Tippett, Neljon G. Emlaw, Daehyun Kim, Young-Kwon Lim, A. Molod
This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Global Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm-scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structure of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation and moisture. The analysis of prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the Western North Pacific and Southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden-Julian Oscillation (MJO) as a source of predictability of TC occurrence beyond the 14 day lead-time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution there are notable gaps between MJO-related prediction skill and predictability which require further study.
{"title":"Tropical cyclones in the GEOS-S2S-2 subseasonal forecasts","authors":"J. García‐Franco, Chia-Ying Lee, Suzana J. Camargo, Michael K. Tippett, Neljon G. Emlaw, Daehyun Kim, Young-Kwon Lim, A. Molod","doi":"10.1175/waf-d-23-0208.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0208.1","url":null,"abstract":"\u0000This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Global Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm-scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structure of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation and moisture. The analysis of prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the Western North Pacific and Southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden-Julian Oscillation (MJO) as a source of predictability of TC occurrence beyond the 14 day lead-time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution there are notable gaps between MJO-related prediction skill and predictability which require further study.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbine within wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that, compared to the simulations without assimilation, the predicted ultra-short term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly, while by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly, and could obviously reduce prediction error. Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.
数据同化是提高近地面风和风电预测性能的重要方法。本研究基于四维变分技术，提出了一种通过提取和同化安装在风电场内风机上的机舱雷达径向风观测数据的主分量来改进近地面风和风电预测的方法。一系列强弱垂直风切变条件下的验证表明，与未同化的模拟相比，直接同化径向风后，近地面风和单机风功率的超短期（0-4 h）平均绝对误差可减少 0.09-1.17 m s-1 和 53-209 kW，而同化主分量后可减少 0.33-1.38 m s-1 和 62-239 kW。进一步研究表明，提取径向风主分量对观测资料的密度和分布影响不大，但可以明显降低观测资料的波动性和观测资料之间的相关性。同化径向风主分量对预报的改善主要是由于同化了观测资料中涉及的低频和低相关信息。
{"title":"Improvement of Wind Power Prediction by Assimilating Principal Components of Cabin Radar Observations","authors":"Feimin Zhang, Shang Wan, Shuanglong Jin, Hao Wang","doi":"10.1175/waf-d-24-0058.1","DOIUrl":"https://doi.org/10.1175/waf-d-24-0058.1","url":null,"abstract":"\u0000Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbine within wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that, compared to the simulations without assimilation, the predicted ultra-short term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly, while by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly, and could obviously reduce prediction error.\u0000Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. W. Miller, C. Li, K. Xu, S. Caparotta, R.V. Rohli
On 13 April 2021, a mesoscale convective system (MCS) swept across the southeastern Louisiana coast, capsizing the 39-m Seacor Power roughly 7 km from shore and leaving 13 mariners drowned or missing. In addition to the severe straight-line winds that sank the vessel, sustained surface winds >20 m s−1 behind the leading convection persisted well after the main convective band, inhibiting search and rescue efforts. Though complete historical fatality statistics are unavailable, the 13 deaths associated with this event likely represent one of the deadliest severe convective weather events in modern U.S. maritime history. This analysis integrates in-situ, remotely sensed, and reanalysis datasets to reconstruct the 2021 Seacor Power accident as well as ascertain its depiction in day-of operational convection-allowing model (CAM) guidance. Results suggest that the MCS formed along an unanalyzed coastal boundary and developed a strong meso-high to the east of the wreck as it moved offshore. The resulting zonally oriented pressure gradient directed stiff easterly winds over the wreck for several hours, even as the squall line had propagated well away from the coast. This multi-hour period of severe weather along the Louisiana coast was relatively well resolved by morning-of CAM guidance, providing optimism that future such events may be anticipated with the lead times required by vulnerable sea craft to reach safe harbor. Future severe convective weather watches containing marine zones might include a “marine” section detailing the potential sea conditions, analogous to the “aviation” section in current severe weather watches.
{"title":"The evolution of the 2021 Seacor Power Tragedy in Coastal Louisiana","authors":"P. W. Miller, C. Li, K. Xu, S. Caparotta, R.V. Rohli","doi":"10.1175/waf-d-23-0179.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0179.1","url":null,"abstract":"\u0000On 13 April 2021, a mesoscale convective system (MCS) swept across the southeastern Louisiana coast, capsizing the 39-m Seacor Power roughly 7 km from shore and leaving 13 mariners drowned or missing. In addition to the severe straight-line winds that sank the vessel, sustained surface winds >20 m s−1 behind the leading convection persisted well after the main convective band, inhibiting search and rescue efforts. Though complete historical fatality statistics are unavailable, the 13 deaths associated with this event likely represent one of the deadliest severe convective weather events in modern U.S. maritime history. This analysis integrates in-situ, remotely sensed, and reanalysis datasets to reconstruct the 2021 Seacor Power accident as well as ascertain its depiction in day-of operational convection-allowing model (CAM) guidance. Results suggest that the MCS formed along an unanalyzed coastal boundary and developed a strong meso-high to the east of the wreck as it moved offshore. The resulting zonally oriented pressure gradient directed stiff easterly winds over the wreck for several hours, even as the squall line had propagated well away from the coast. This multi-hour period of severe weather along the Louisiana coast was relatively well resolved by morning-of CAM guidance, providing optimism that future such events may be anticipated with the lead times required by vulnerable sea craft to reach safe harbor. Future severe convective weather watches containing marine zones might include a “marine” section detailing the potential sea conditions, analogous to the “aviation” section in current severe weather watches.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Timothy D. Corrie, B. Geerts, Tatiana G. Smirnova, Stanley G. Benjamin, Michael Charnick, Matthew Brothers, Siwei He, Zachary J. Lebo, Eric P. James
Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the RUC Land-Surface Model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. In order to improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions.
{"title":"Representation of Blowing Snow and Associated Visibility Reduction in an Operational High-Resolution Weather Model","authors":"Timothy D. Corrie, B. Geerts, Tatiana G. Smirnova, Stanley G. Benjamin, Michael Charnick, Matthew Brothers, Siwei He, Zachary J. Lebo, Eric P. James","doi":"10.1175/waf-d-23-0195.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0195.1","url":null,"abstract":"\u0000Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the RUC Land-Surface Model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. In order to improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stochastic representations of model uncertainties are of great importance for the performance of ensemble prediction systems (EPSs). The stochastically perturbed parametrization tendencies (SPPT) scheme with a single-scale random pattern has been used in the operational global EPS of China Meteorological Administration (CMA-GEPS) since 2018. To deal with deficiencies in this operational single-scale SPPT scheme, a combined scheme based on the multi-scale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is developed. In the combined scheme, the mSPPT component aims to expand model uncertainties characterized by SPPT at mesoscale, synoptic scale, and planetary scale. The SPP-PBL component with six vital parameters is used to capture uncertainties in PBL processes, which is under-represented by SPPT for the tapering treatment within PBL. Comparisons between the operational SPPT scheme and the mSPPT scheme reveal that the mSPPT scheme can generate more improvements in both ensemble reliability and forecast skills mainly in tropics. Besides, additional benefits from SPP-PBL on top of mSPPT are shown to be primarily distributed in tropics at the lower layers below 850 hPa and surface. Furthermore, the combined scheme of mSPPT and SPP-PBL is suggested to yield better spread-error relationships and forecast skills than the operational SPPT scheme in terms of objective verification scores for standard upper-air variables and surface parameters. A case study for the extreme precipitation event on 20 July 2021 in Henan Province of China also demonstrates the better ability of the combined scheme in forecasting the precipitation intensity and location.
{"title":"A combined scheme based on the multi-scale stochastic perturbed parameterization tendencies and perturbed boundary layer parameterization for a global ensemble prediction system","authors":"Fei Peng, Xiaoli Li, Jing Chen","doi":"10.1175/waf-d-24-0022.1","DOIUrl":"https://doi.org/10.1175/waf-d-24-0022.1","url":null,"abstract":"\u0000Stochastic representations of model uncertainties are of great importance for the performance of ensemble prediction systems (EPSs). The stochastically perturbed parametrization tendencies (SPPT) scheme with a single-scale random pattern has been used in the operational global EPS of China Meteorological Administration (CMA-GEPS) since 2018. To deal with deficiencies in this operational single-scale SPPT scheme, a combined scheme based on the multi-scale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is developed. In the combined scheme, the mSPPT component aims to expand model uncertainties characterized by SPPT at mesoscale, synoptic scale, and planetary scale. The SPP-PBL component with six vital parameters is used to capture uncertainties in PBL processes, which is under-represented by SPPT for the tapering treatment within PBL. Comparisons between the operational SPPT scheme and the mSPPT scheme reveal that the mSPPT scheme can generate more improvements in both ensemble reliability and forecast skills mainly in tropics. Besides, additional benefits from SPP-PBL on top of mSPPT are shown to be primarily distributed in tropics at the lower layers below 850 hPa and surface. Furthermore, the combined scheme of mSPPT and SPP-PBL is suggested to yield better spread-error relationships and forecast skills than the operational SPPT scheme in terms of objective verification scores for standard upper-air variables and surface parameters. A case study for the extreme precipitation event on 20 July 2021 in Henan Province of China also demonstrates the better ability of the combined scheme in forecasting the precipitation intensity and location.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mariana G. Cains, Christopher D. Wirz, Julie L. Demuth, Ann Bostrom, David John Gagne, Amy McGovern, R. Sobash, Deianna Madlambayan
As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (a) how they make guidance use decisions, and (b) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting probability of severe hail or a 2D-convolutional neural net model predicting probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful and used tools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts.
{"title":"Exploring NWS Forecasters’ Assessment of AI Guidance Trustworthiness","authors":"Mariana G. Cains, Christopher D. Wirz, Julie L. Demuth, Ann Bostrom, David John Gagne, Amy McGovern, R. Sobash, Deianna Madlambayan","doi":"10.1175/waf-d-23-0180.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0180.1","url":null,"abstract":"\u0000As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (a) how they make guidance use decisions, and (b) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting probability of severe hail or a 2D-convolutional neural net model predicting probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful and used tools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael D. Pletcher, Peter G. Veals, Michael E. Wessler, David Church, Kirstin Harnos, James Correia, Randy J. Chase, W. J. Steenburgh
Producing a quantitative snowfall forecast (QSF) typically requires a model quantitative precipitation forecast (QPF) and snow-to-liquid ratio (SLR) estimate. QPF and SLR can vary significantly in space and time over complex terrain, necessitating fine-scale or point-specific forecasts of each component. Little Cottonwood Canyon (LCC) in Utah’s Wasatch Range frequently experiences high-impact winter storms and avalanche closures that result in substantial transportation and economic disruptions, making it an excellent testbed for evaluating snowfall forecasts. In this study, we validate QPFs, SLR forecasts, and QSFs produced by or derived from the Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) using liquid precipitation equivalent (LPE) and snowfall observations collected during the 2019/20 – 2022/23 cool seasons at the Alta–Collins snow-study site (2945 m MSL) in upper LCC. The 12-h QPFs produced by the GFS and HRRR underpredict the total LPE during the four cool seasons by 33% and 29%, respectively, and underpredict 50th, 75th, and 90th percentile event frequencies. Current operational SLR methods exhibit mean absolute errors of 4.5 – 7.7. In contrast, a locally trained random forest algorithm reduces SLR mean absolute errors to 3.7. Despite the random forest producing more accurate SLR forecasts, QSFs derived from operational SLR methods produce higher critical success indices since they exhibit positive SLR biases that offset negative QPF biases. These results indicate an overall underprediction of LPE by operational models in upper LCC and illustrate the need to identify sources of QSF bias to enhance QSF performance.
{"title":"Validation of cool-season snowfall forecasts at a high-elevation site in Utah’s Little Cottonwood Canyon","authors":"Michael D. Pletcher, Peter G. Veals, Michael E. Wessler, David Church, Kirstin Harnos, James Correia, Randy J. Chase, W. J. Steenburgh","doi":"10.1175/waf-d-23-0176.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0176.1","url":null,"abstract":"\u0000Producing a quantitative snowfall forecast (QSF) typically requires a model quantitative precipitation forecast (QPF) and snow-to-liquid ratio (SLR) estimate. QPF and SLR can vary significantly in space and time over complex terrain, necessitating fine-scale or point-specific forecasts of each component. Little Cottonwood Canyon (LCC) in Utah’s Wasatch Range frequently experiences high-impact winter storms and avalanche closures that result in substantial transportation and economic disruptions, making it an excellent testbed for evaluating snowfall forecasts. In this study, we validate QPFs, SLR forecasts, and QSFs produced by or derived from the Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) using liquid precipitation equivalent (LPE) and snowfall observations collected during the 2019/20 – 2022/23 cool seasons at the Alta–Collins snow-study site (2945 m MSL) in upper LCC. The 12-h QPFs produced by the GFS and HRRR underpredict the total LPE during the four cool seasons by 33% and 29%, respectively, and underpredict 50th, 75th, and 90th percentile event frequencies. Current operational SLR methods exhibit mean absolute errors of 4.5 – 7.7. In contrast, a locally trained random forest algorithm reduces SLR mean absolute errors to 3.7. Despite the random forest producing more accurate SLR forecasts, QSFs derived from operational SLR methods produce higher critical success indices since they exhibit positive SLR biases that offset negative QPF biases. These results indicate an overall underprediction of LPE by operational models in upper LCC and illustrate the need to identify sources of QSF bias to enhance QSF performance.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate tropical cyclogenesis (TCG) prediction is important because it allows national operational forecasting agencies to issue timely warnings and implement effective disaster prevention measures. In 2020, the Korea Meteorological Administration employed a self-developed operational model called the Korean Integrated Model (KIM). In this study, we verified KIM’s TCG forecast skill over the western North Pacific. Based on 9-day forecasts, TCG in the model was objectively detected and classified as well-predicted, early formation, late formation, miss, or false alarm by comparing their formation times and locations with those of 46 tropical cyclones (TCs) from June to November in 2020–2021 documented by the Joint Typhoon Warning Center. The prediction of large-scale environmental conditions relevant to TCG was also evaluated. The results showed that the probability of KIM detection was comparable to or better than that of previously reported statistics of other numerical weather prediction models. The intra-basin comparison revealed that the probability of detection in the Philippine Sea was the highest, followed by the South China Sea and Central Pacific. The best TCG prediction performance in the Philippine Sea was supported by unbiased forecasts in large-scale environments. The missed and false alarm cases in all three regions had the largest prediction biases in the large-scale lower-tropospheric relative vorticity. Excessive false alarms may be associated with prediction biases in the vertical gradient of equivalent potential temperature within the boundary layer. This study serves as a primary guide for national forecasters and is useful to model developers for further refinement of KIM.
{"title":"Verification of tropical cyclogenesis forecasts of the Korean Integrated Model for 2020–2021","authors":"Jiyoung Jung, Minhee Chang, Eun-Hee Lee, Mi-Kyung Sung","doi":"10.1175/waf-d-23-0175.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0175.1","url":null,"abstract":"\u0000Accurate tropical cyclogenesis (TCG) prediction is important because it allows national operational forecasting agencies to issue timely warnings and implement effective disaster prevention measures. In 2020, the Korea Meteorological Administration employed a self-developed operational model called the Korean Integrated Model (KIM). In this study, we verified KIM’s TCG forecast skill over the western North Pacific. Based on 9-day forecasts, TCG in the model was objectively detected and classified as well-predicted, early formation, late formation, miss, or false alarm by comparing their formation times and locations with those of 46 tropical cyclones (TCs) from June to November in 2020–2021 documented by the Joint Typhoon Warning Center. The prediction of large-scale environmental conditions relevant to TCG was also evaluated. The results showed that the probability of KIM detection was comparable to or better than that of previously reported statistics of other numerical weather prediction models. The intra-basin comparison revealed that the probability of detection in the Philippine Sea was the highest, followed by the South China Sea and Central Pacific. The best TCG prediction performance in the Philippine Sea was supported by unbiased forecasts in large-scale environments. The missed and false alarm cases in all three regions had the largest prediction biases in the large-scale lower-tropospheric relative vorticity. Excessive false alarms may be associated with prediction biases in the vertical gradient of equivalent potential temperature within the boundary layer. This study serves as a primary guide for national forecasters and is useful to model developers for further refinement of KIM.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles M. Kuster, Keith D. Sherburn, V. Mahale, Terry J. Schuur, Olivia F. McCauley, Jason S. Schaumann
Recent operationally driven research has generated a framework, known as the three-ingredients method and mesovortex warning system, that can help forecasters anticipate mesovortex development and issue warnings within quasi-linear convective systems (QLCSs). However, dual-polarization radar data has not yet been incorporated into this framework. Therefore, several dual- and single-polarization radar signatures associated with QLCS mesovortices were analyzed to determine if they could provide additional information about mesovortex development and intensity. An analysis of 167 mesovortices showed that 1) KDP drops precede ~95% of mesovortices and provide an initial indication of where a mesovortex may develop, 2) midlevel KDP cores are a potentially useful precursor signature because they precede a majority of mesovortices and have higher magnitudes for mesovortices that produce wind damage or tornadoes, 3) low-level KDP cores and areas of enhanced spectrum width have higher magnitudes for mesovortices that produce wind damage or tornadoes, but tend to develop at about the same time as the mesovortex, which makes them more useful as diagnostic than as predictive signatures, and 4) as range from the radar increases, the radar signatures become less useful in anticipating mesovortex intensity but can still be used to anticipate mesovortex development or build confidence in mesovortex existence.
{"title":"Radar Signatures Associated with Quasi-Linear Convective System Mesovortices","authors":"Charles M. Kuster, Keith D. Sherburn, V. Mahale, Terry J. Schuur, Olivia F. McCauley, Jason S. Schaumann","doi":"10.1175/waf-d-23-0144.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0144.1","url":null,"abstract":"\u0000Recent operationally driven research has generated a framework, known as the three-ingredients method and mesovortex warning system, that can help forecasters anticipate mesovortex development and issue warnings within quasi-linear convective systems (QLCSs). However, dual-polarization radar data has not yet been incorporated into this framework. Therefore, several dual- and single-polarization radar signatures associated with QLCS mesovortices were analyzed to determine if they could provide additional information about mesovortex development and intensity. An analysis of 167 mesovortices showed that 1) KDP drops precede ~95% of mesovortices and provide an initial indication of where a mesovortex may develop, 2) midlevel KDP cores are a potentially useful precursor signature because they precede a majority of mesovortices and have higher magnitudes for mesovortices that produce wind damage or tornadoes, 3) low-level KDP cores and areas of enhanced spectrum width have higher magnitudes for mesovortices that produce wind damage or tornadoes, but tend to develop at about the same time as the mesovortex, which makes them more useful as diagnostic than as predictive signatures, and 4) as range from the radar increases, the radar signatures become less useful in anticipating mesovortex intensity but can still be used to anticipate mesovortex development or build confidence in mesovortex existence.","PeriodicalId":509742,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141345175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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