Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time-consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecast—in between production intervals — using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data is available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 versus 0.55 at a 1-month lead time and 0.51 versus 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea-ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.
{"title":"Enhancing seasonal forecast skills by optimally weighting the ensemble from fresh data","authors":"J. Brajard, F. Counillon, Yiguo Wang, M. Kimmritz","doi":"10.1175/waf-d-22-0166.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0166.1","url":null,"abstract":"\u0000Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time-consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecast—in between production intervals — using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data is available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 versus 0.55 at a 1-month lead time and 0.51 versus 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea-ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42216724","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}
Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope-wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope-windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state-critical-level regime in comparison to other downslope-windstorm-generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the US and annually averaged for each year between 2008 and 2019.
{"title":"Downslope windstorm forecasting: Easier with a critical level, but still challenging for high-resolution ensembles","authors":"J. Metz, D. Durran","doi":"10.1175/waf-d-22-0135.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0135.1","url":null,"abstract":"\u0000Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope-wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope-windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state-critical-level regime in comparison to other downslope-windstorm-generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the US and annually averaged for each year between 2008 and 2019.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45745838","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 average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes owing to their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the U.S. were exploited, while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). Eighty-eight high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea-surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden-Julian Oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific Decadal Oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 over forecasted the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters.
{"title":"GEFSv12 high- and low-skill day-10 tornado forecasts","authors":"Douglas E. Miller, Vittorio A. Gensini","doi":"10.1175/waf-d-22-0122.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0122.1","url":null,"abstract":"\u0000On average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes owing to their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the U.S. were exploited, while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). Eighty-eight high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea-surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden-Julian Oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific Decadal Oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 over forecasted the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43085443","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}
J. Kossin, D. Herndon, A. Wimmers, Xi Guo, E. Blake
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind-field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind-intensity and passive satellite microwave imagery, and is named “M-PERC” for Microwave-based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin, but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
{"title":"M-PERC: A New Satellite Microwave-Based Model to Diagnose the Onset of Tropical Cyclone Eyewall Replacement Cycles","authors":"J. Kossin, D. Herndon, A. Wimmers, Xi Guo, E. Blake","doi":"10.1175/waf-d-22-0178.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0178.1","url":null,"abstract":"\u0000Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind-field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind-intensity and passive satellite microwave imagery, and is named “M-PERC” for Microwave-based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin, but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43286891","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}
Dana M. Uden, M. S. Wandishin, P. Schlatter, Michael Kraus
This work set out to assess the performance of four forecast systems (the Short-Range Ensemble Forecast (SREF), High-Resolution Rapid Refresh Ensemble (HRRRE), the National Blend of Models (NBM), and the Probabilistic Snow Accumulation product (PSA) from the National Weather Service (NWS) Boulder, CO Weather Forecast Office) when predicting snowfall events around the Intermountain West to advise winter weather decision-making processes at Denver International Airport. The goal was to provide airport personnel and the Boulder NWS Forecast Office with operationally-relevant verification results on the timing and severity of these events so they are able to make better-informed decisions to minimize negative impacts of storms. Forecasts of snow events using various probability thresholds and a climatological snow-to-liquid ratio of 15:1 were evaluated against Meteorological Aerodrome Reports (METARs) for 24-hour periods following four decision-making times spaced equally throughout the day. For the ensembles, a frequentist approach was used: the forecast probability equaled the percentage of ensemble members that predicted a snow event. The results show that the NBM had the best timing of snow events out of the products while all the products tended to over-forecast snow amount. Additionally, NBM had fewer snow events and rarely had high probabilities of snow, unlike the other forecast products.
{"title":"Evaluation of Probabilistic Snow Forecasts for Winter Weather Operations at Intermountain West Airports","authors":"Dana M. Uden, M. S. Wandishin, P. Schlatter, Michael Kraus","doi":"10.1175/waf-d-22-0170.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0170.1","url":null,"abstract":"\u0000This work set out to assess the performance of four forecast systems (the Short-Range Ensemble Forecast (SREF), High-Resolution Rapid Refresh Ensemble (HRRRE), the National Blend of Models (NBM), and the Probabilistic Snow Accumulation product (PSA) from the National Weather Service (NWS) Boulder, CO Weather Forecast Office) when predicting snowfall events around the Intermountain West to advise winter weather decision-making processes at Denver International Airport. The goal was to provide airport personnel and the Boulder NWS Forecast Office with operationally-relevant verification results on the timing and severity of these events so they are able to make better-informed decisions to minimize negative impacts of storms. Forecasts of snow events using various probability thresholds and a climatological snow-to-liquid ratio of 15:1 were evaluated against Meteorological Aerodrome Reports (METARs) for 24-hour periods following four decision-making times spaced equally throughout the day. For the ensembles, a frequentist approach was used: the forecast probability equaled the percentage of ensemble members that predicted a snow event. The results show that the NBM had the best timing of snow events out of the products while all the products tended to over-forecast snow amount. Additionally, NBM had fewer snow events and rarely had high probabilities of snow, unlike the other forecast products.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42553770","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}
Mu-Chieh Ko, Xiaomin Chen, M. Kubát, S. Gopalakrishnan
This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CMLmodelwas built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the HurricaneWeather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized that the importance of performing resampling on an imbalanced input dataset. Edited Nearest Neighbor and Synthetic Minority Oversampling Technique improved the Probability of Detection (POD) by ∼10% for the RI class. This paper also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% False Alarm Ratio (FAR), while the operational models had only 10 to 30% POD but 50 to 60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.
{"title":"The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data","authors":"Mu-Chieh Ko, Xiaomin Chen, M. Kubát, S. Gopalakrishnan","doi":"10.1175/waf-d-22-0217.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0217.1","url":null,"abstract":"\u0000This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CMLmodelwas built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the HurricaneWeather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized that the importance of performing resampling on an imbalanced input dataset. Edited Nearest Neighbor and Synthetic Minority Oversampling Technique improved the Probability of Detection (POD) by ∼10% for the RI class. This paper also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% False Alarm Ratio (FAR), while the operational models had only 10 to 30% POD but 50 to 60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48687027","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}
T. Sandmæl, Brandon R. Smith, Jonathan G. Madden, Justin W. Monroe, P. Hyland, B. Schenkel, T. Meyer
Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D radar network, the Tornado Probability algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advances in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA NWS and the United States Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm’s evaluation during the testbed.
{"title":"The 2021 Hazardous Weather Testbed Experimental Warning Program Radar Convective Applications Experiment: A Forecaster Evaluation of the Tornado Probability Algorithm and the New Mesocyclone Detection Algorithm","authors":"T. Sandmæl, Brandon R. Smith, Jonathan G. Madden, Justin W. Monroe, P. Hyland, B. Schenkel, T. Meyer","doi":"10.1175/waf-d-23-0042.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0042.1","url":null,"abstract":"\u0000Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D radar network, the Tornado Probability algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advances in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA NWS and the United States Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm’s evaluation during the testbed.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47828084","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}
Analyses of cloud top temperature and lightning characteristics of 48 Weather Research and Forecasting (WRF) model simulated ocean-based wind events, with 1 min temporal and 0.5 km horizontal resolution, revealed signatures similar to the corresponding 13 observed events detected by buoys and Coastal-Marine Automated Network (C-MAN) stations as shown in prior research on ocean-based wind events by the first author. These events occurred in the eastern Gulf of Mexico and in the Atlantic Ocean from Florida northward through South Carolina. The coldest WRF cloud top temperature (WCTT) and peak WRF-estimated lightning flash rate values of the model simulated events, where each event was required to have a negative vertical velocity of at least 10 m s-1 in the lowest 2 km associated with a convective storm, occurred at an average of 4.2 and 1.1 min prior to the events, respectively. With 36 of the events, the peak estimated flash rate occurred within 5 min of the coldest WCTT. Cloud depth typically increased as the WCTT decreased, and the maximum depth occurred at an average of 2.9 min prior to the events. Thermal cooling and precipitation loading provided negative buoyancy needed to help drive the wind events. Environmental characteristics of the model simulated ocean-based wind events also resembled those associated with land-based wet downbursts, including moist air near the surface, lapse rates near moist adiabatic, and low cloud bases.
对天气研究与预报(WRF)模式模拟的48个1分钟时间和0.5 km水平分辨率的海洋风事件的云顶温度和闪电特征进行分析，揭示了与浮标和海岸-海洋自动网络(C-MAN)站探测到的相应13个观测事件相似的特征，这与第一作者先前对海洋风事件的研究结果相似。这些事件发生在墨西哥湾东部和从佛罗里达州向北到南卡罗来纳州的大西洋。模式模拟事件的最冷WRF云顶温度(WCTT)和WRF估计的闪电闪速峰值值(每个事件要求在与对流风暴相关的最低2 km处具有至少10 m s-1的负垂直速度)平均分别在事件发生前4.2和1.1 min出现。在其中36个事件中，估计闪速峰值发生在最冷WCTT的5分钟内。云深随WCTT减小而增大，最大云深平均出现在事件发生前2.9 min。热冷却和降水负荷提供了帮助驱动风事件所需的负浮力。该模式模拟的海洋风事件的环境特征也类似于与陆基湿降暴相关的环境特征，包括地表附近的潮湿空气、潮湿绝热附近的递减率和低云基底。
{"title":"Signatures of Oceanic Wind Events in Convection Resolving WRF Model Simulations","authors":"Kelsey B. Thompson, J. Mecikalski, M. Bateman","doi":"10.1175/waf-d-22-0020.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0020.1","url":null,"abstract":"\u0000Analyses of cloud top temperature and lightning characteristics of 48 Weather Research and Forecasting (WRF) model simulated ocean-based wind events, with 1 min temporal and 0.5 km horizontal resolution, revealed signatures similar to the corresponding 13 observed events detected by buoys and Coastal-Marine Automated Network (C-MAN) stations as shown in prior research on ocean-based wind events by the first author. These events occurred in the eastern Gulf of Mexico and in the Atlantic Ocean from Florida northward through South Carolina. The coldest WRF cloud top temperature (WCTT) and peak WRF-estimated lightning flash rate values of the model simulated events, where each event was required to have a negative vertical velocity of at least 10 m s-1 in the lowest 2 km associated with a convective storm, occurred at an average of 4.2 and 1.1 min prior to the events, respectively. With 36 of the events, the peak estimated flash rate occurred within 5 min of the coldest WCTT. Cloud depth typically increased as the WCTT decreased, and the maximum depth occurred at an average of 2.9 min prior to the events. Thermal cooling and precipitation loading provided negative buoyancy needed to help drive the wind events. Environmental characteristics of the model simulated ocean-based wind events also resembled those associated with land-based wet downbursts, including moist air near the surface, lapse rates near moist adiabatic, and low cloud bases.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45295566","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}
B. Kahn, E. Berndt, J. Case, P. Kalmus, M. Richardson
Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to six hours to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the Contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.
{"title":"A nowcasting approach for low Earth orbit hyperspectral infrared soundings within the convective environment","authors":"B. Kahn, E. Berndt, J. Case, P. Kalmus, M. Richardson","doi":"10.1175/waf-d-22-0204.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0204.1","url":null,"abstract":"\u0000Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to six hours to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the Contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48057148","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}
Dillon V. Blount, C. Evans, I. Jirak, A. Dean, S. Kravtsov
This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31,153 soundings taken at 0000 UTC and 32,308 soundings taken at 1200 UTC between May 2019 – March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above-ground-level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint-temperature differences resulting from altitude, latitude, or seasonality. The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary-layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary-layer features such turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.
{"title":"An Objective Method for Clustering Observed Vertical Thermodynamic Profiles by Their Boundary-Layer Structure","authors":"Dillon V. Blount, C. Evans, I. Jirak, A. Dean, S. Kravtsov","doi":"10.1175/waf-d-22-0195.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0195.1","url":null,"abstract":"\u0000This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31,153 soundings taken at 0000 UTC and 32,308 soundings taken at 1200 UTC between May 2019 – March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above-ground-level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint-temperature differences resulting from altitude, latitude, or seasonality.\u0000The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary-layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary-layer features such turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42917504","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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