Abstract The optical flow technique has advantages in motion tracking and has long been employed in precipitation nowcasting to track the motion of precipitation fields using ground radar datasets. However, the performance and forecast timescale of models based on optical flow are limited. Here, we present the results of the application of the deep learning method to optical flow estimation to extend its forecast timescale and enhance the performance of nowcasting. It is shown that deep learning model can better capture both multi-spatial and multi-temporal motions of precipitation events compared with traditional optical flow estimation methods. The model comprises two components: (1) a regression process based on multiple optical flow algorithms, which more accurately captures multi-spatial features compared with a single optical flow algorithm, and (2) a U-Net-based network that trains multi-temporal features of precipitation movement. We evaluated the model performance with cases of precipitation in South Korea. In particular, the regression process minimizes errors by combining multiple optical flow algorithms with a gradient descent method and outperforms other models using only a single optical flow algorithm up to a 3-h lead time. Additionally, the U-Net plays a crucial role in capturing nonlinear motion that cannot be captured by a simple advection model through traditional optical flow estimation. Consequently, we suggest that the proposed optical flow estimation method with deep learning could play a significant role in improving the performance of current operational nowcasting models, which are based on traditional optical flow methods.
{"title":"A Deep Learning Model for Precipitation Nowcasting Using Multiple Optical Flow Algorithms","authors":"Ji-Hoon Ha, Hyesook Lee","doi":"10.1175/waf-d-23-0104.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0104.1","url":null,"abstract":"Abstract The optical flow technique has advantages in motion tracking and has long been employed in precipitation nowcasting to track the motion of precipitation fields using ground radar datasets. However, the performance and forecast timescale of models based on optical flow are limited. Here, we present the results of the application of the deep learning method to optical flow estimation to extend its forecast timescale and enhance the performance of nowcasting. It is shown that deep learning model can better capture both multi-spatial and multi-temporal motions of precipitation events compared with traditional optical flow estimation methods. The model comprises two components: (1) a regression process based on multiple optical flow algorithms, which more accurately captures multi-spatial features compared with a single optical flow algorithm, and (2) a U-Net-based network that trains multi-temporal features of precipitation movement. We evaluated the model performance with cases of precipitation in South Korea. In particular, the regression process minimizes errors by combining multiple optical flow algorithms with a gradient descent method and outperforms other models using only a single optical flow algorithm up to a 3-h lead time. Additionally, the U-Net plays a crucial role in capturing nonlinear motion that cannot be captured by a simple advection model through traditional optical flow estimation. Consequently, we suggest that the proposed optical flow estimation method with deep learning could play a significant role in improving the performance of current operational nowcasting models, which are based on traditional optical flow methods.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"8 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992002","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}
Xia Sun, Dominikus Heinzeller, Ligia Bernardet, Linlin Pan, Weiwei Li, David Turner, John Brown
Abstract Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental U.S. than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS which comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings.
摘要对流有效势能(CAPE)是风暴预报的重要指标。全球预报系统(GFS)的最新版本(v15.2和v16)预测美国大陆夏季的CAPE值低于分析和观测值。我们利用统一预报系统案例研究收集的一个例子，对GFS模拟夏季CAPE进行了评估，以探讨导致GFS低CAPE偏差的因素。具体而言，我们研究了地表能量收支、土壤性质以及近地表和高层气象场。结果表明，GFS模拟的地表潜热通量较小，地表感热通量较大。这可归因于来自离线全球土地数据同化系统的GFS中土壤湿度比观测到的略干。GFS v16中较低的模拟CAPE与中午后边界层云过多的地表净辐射较GFS v15.2提前下降有关。水分收支分析表明，大尺度水汽平流的误差不会导致低空GFS的干偏。Common Community Physics Package单列模型(SCM)实验表明，在真实的初始垂直剖面下，SCM模拟产生的CAPE比使用GFS IC模拟产生的CAPE大，而使用主动LSM的SCM模拟产生的CAPE往往比使用规定表面通量的SCM模拟产生的CAPE小。请注意，这些发现仅适用于本案例研究。包括更多温暖季节的病例将增强我们研究结果的普遍性。
{"title":"A Case Study Investigating the Low Summertime CAPE Behavior in the Global Forecast System","authors":"Xia Sun, Dominikus Heinzeller, Ligia Bernardet, Linlin Pan, Weiwei Li, David Turner, John Brown","doi":"10.1175/waf-d-22-0208.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0208.1","url":null,"abstract":"Abstract Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental U.S. than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS which comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"73 S7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342623","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}
Alex Alvin Cheung, Christopher J. Slocum, John A. Knaff, Muhammad Naufal Razin
Abstract Intense tropical cyclones can form secondary eyewalls (SEs) that contract towards the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016–19, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following a SE formation. The results show two primary SE pathways exist, No Replacement, known as Path 1, and Replacement, known as the Classic Path. Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold air advection. This study highlights a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions regarding the physical processes driving SE formations. Most importantly, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.
{"title":"Documenting the Progressions of Secondary Eyewall Formations","authors":"Alex Alvin Cheung, Christopher J. Slocum, John A. Knaff, Muhammad Naufal Razin","doi":"10.1175/waf-d-23-0047.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0047.1","url":null,"abstract":"Abstract Intense tropical cyclones can form secondary eyewalls (SEs) that contract towards the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016–19, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following a SE formation. The results show two primary SE pathways exist, No Replacement, known as Path 1, and Replacement, known as the Classic Path. Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold air advection. This study highlights a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions regarding the physical processes driving SE formations. Most importantly, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391330","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}
Michael E. Baldwin, Heather D. Reeves, Andrew A. Rosenow
Abstract Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning-based system for nowcasting the probability of sub-freezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real-time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of sub-freezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
{"title":"Evaluation of a Probabilistic Subfreezing Road Temperature Nowcast System Based on Machine Learning","authors":"Michael E. Baldwin, Heather D. Reeves, Andrew A. Rosenow","doi":"10.1175/waf-d-23-0137.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0137.1","url":null,"abstract":"Abstract Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning-based system for nowcasting the probability of sub-freezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real-time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of sub-freezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"65 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934985","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}
Abstract Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself.
{"title":"Hodographs and Skew Ts of Hail-Producing Storms","authors":"Cameron J. Nixon, John T. Allen, Mateusz Taszarek","doi":"10.1175/waf-d-23-0031.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0031.1","url":null,"abstract":"Abstract Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"194 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957115","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}
Platon Patlakas, Christos Stathopoulos, Christina Kalogeri, Vassilios Vervatis, John Karagiorgos, Ioannis Chaniotis, Andreas Kallos, Ayman S. Ghulam, Mohammed A. Al-omary, Ioannis Papageorgiou, Dimitrios Diamantis, Zaphiris Christidis, John Snook, Sarantis Sofianos, George Kallos
Abstract The weather and climate greatly affect socioeconomic activities on multiple temporal and spatial scales. From a climate perspective, atmospheric and ocean characteristics have determined the life, evolution, and prosperity of humans and other species in different areas of the world. On smaller scales, the atmospheric and sea conditions affect various sectors such as civil protection, food security, communications, transportation, and insurance. It becomes evident that weather and ocean forecasting is high-value information highlighting the need for state-of-the-art forecasting systems to be adopted. This importance has been acknowledged by the authorities of Saudi Arabia entrusting the National Center for Meteorology (NCM) to provide high-quality weather and climate analytics. This led to the development of a numerical weather prediction (NWP) system. The new system includes weather, wave, and ocean circulation components and has been operational since 2020 enhancing the national capabilities in NWP. Within this article, a description of the system and its performance is discussed alongside future goals.
{"title":"The development and operational use of an integrated Numerical Weather Prediction System in the National Center of Meteorology of the Kingdom of Saudi Arabia","authors":"Platon Patlakas, Christos Stathopoulos, Christina Kalogeri, Vassilios Vervatis, John Karagiorgos, Ioannis Chaniotis, Andreas Kallos, Ayman S. Ghulam, Mohammed A. Al-omary, Ioannis Papageorgiou, Dimitrios Diamantis, Zaphiris Christidis, John Snook, Sarantis Sofianos, George Kallos","doi":"10.1175/waf-d-23-0034.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0034.1","url":null,"abstract":"Abstract The weather and climate greatly affect socioeconomic activities on multiple temporal and spatial scales. From a climate perspective, atmospheric and ocean characteristics have determined the life, evolution, and prosperity of humans and other species in different areas of the world. On smaller scales, the atmospheric and sea conditions affect various sectors such as civil protection, food security, communications, transportation, and insurance. It becomes evident that weather and ocean forecasting is high-value information highlighting the need for state-of-the-art forecasting systems to be adopted. This importance has been acknowledged by the authorities of Saudi Arabia entrusting the National Center for Meteorology (NCM) to provide high-quality weather and climate analytics. This led to the development of a numerical weather prediction (NWP) system. The new system includes weather, wave, and ocean circulation components and has been operational since 2020 enhancing the national capabilities in NWP. Within this article, a description of the system and its performance is discussed alongside future goals.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"36 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011623","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}
Andrew B. Penny, Laura Alaka, Arthur A. Taylor, William Booth, Mark DeMaria, Cody Fritz, Jamie Rhome
Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008-2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
{"title":"Operational storm surge forecasting at the National Hur ricane Center: The case for probabilistic guidance and the evaluation of improved storm size forecasts used to define the wind forcing","authors":"Andrew B. Penny, Laura Alaka, Arthur A. Taylor, William Booth, Mark DeMaria, Cody Fritz, Jamie Rhome","doi":"10.1175/waf-d-22-0209.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0209.1","url":null,"abstract":"Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008-2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617833","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}
Juhee Kwon, Kyo-Sun Sunny Lim, Sun-Young Park, Kwonil Kim, Gyuwon Lee
Abstract A new version of the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula.
{"title":"Effects of Prognostic Number Concentrations of Snow and Graupel on the Simulated Precipitation over the Korean Peninsula","authors":"Juhee Kwon, Kyo-Sun Sunny Lim, Sun-Young Park, Kwonil Kim, Gyuwon Lee","doi":"10.1175/waf-d-23-0057.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0057.1","url":null,"abstract":"Abstract A new version of the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778481","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}
Abstract Throughout the summer months in the Southeast United States, the initiation of isolated convection can occur abundantly during the daytime with weak synoptic support (e.g., weak wind shear). Centered around this premise, a dual-summer, limited area case study of CI events concerning both geographical and meteorological features was conducted. The goal of this study was to help explain SEUS summertime CI in weak synoptic environments, which can enhance CI predictability . Results show that spatial CI non-randomness event patterns arise, with greater CI event density appearing over high elevation by midday. Later in the day, overall CI event counts subside with other mechanisms/factors emerging (e.g., urban heat island). Antecedent rainfall, instability, and moisture features are also higher on average where CI occurred. In a random forest feature importance analysis, elevation was the most important variable in dictating CI events in the early to mid-afternoon while antecedent rainfall and wind direction consistently rank highest in permutation importance. The results cumulatively allude to, albeit in a muted, non-significant statistical signal, and a degree of spatial clustering of CI event occurrences cross the study domain as a function of daytime heating and contributions of features to enhancing CI probabilities (e.g., differential heating, mesoscale thermal circulations).
{"title":"Mesoscale influences of land use, topography, antecedent rainfall, and atmospheric conditions on summertime convective storm initiation under weak synoptic scale forcing","authors":"Christopher Tracy, John R. Mecikalski","doi":"10.1175/waf-d-22-0216.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0216.1","url":null,"abstract":"Abstract Throughout the summer months in the Southeast United States, the initiation of isolated convection can occur abundantly during the daytime with weak synoptic support (e.g., weak wind shear). Centered around this premise, a dual-summer, limited area case study of CI events concerning both geographical and meteorological features was conducted. The goal of this study was to help explain SEUS summertime CI in weak synoptic environments, which can enhance CI predictability . Results show that spatial CI non-randomness event patterns arise, with greater CI event density appearing over high elevation by midday. Later in the day, overall CI event counts subside with other mechanisms/factors emerging (e.g., urban heat island). Antecedent rainfall, instability, and moisture features are also higher on average where CI occurred. In a random forest feature importance analysis, elevation was the most important variable in dictating CI events in the early to mid-afternoon while antecedent rainfall and wind direction consistently rank highest in permutation importance. The results cumulatively allude to, albeit in a muted, non-significant statistical signal, and a degree of spatial clustering of CI event occurrences cross the study domain as a function of daytime heating and contributions of features to enhancing CI probabilities (e.g., differential heating, mesoscale thermal circulations).","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778788","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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