Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) includes that no ensemble members have non-zero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the non-zero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with non-zero lightning flashes is smaller than a prescribed threshold (Nmin). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have non-zero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of Nmin.
{"title":"Regression-Based Ensemble Perturbations for the Zero-Gradient Issue Posed in Lightning-Flash Data Assimilation with an Ensemble Kalman Filter","authors":"T. Honda, Yousuke Sato, T. Miyoshi","doi":"10.1175/mwr-d-22-0334.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0334.1","url":null,"abstract":"\u0000Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) includes that no ensemble members have non-zero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the non-zero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with non-zero lightning flashes is smaller than a prescribed threshold (Nmin). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have non-zero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of Nmin.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46333954","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 Walsworth, J. Poterjoy, Elizabeth A. Satterfield
In order for data assimilation to provide faithful state estimates for dynamical models, specifications of observation uncertainty need to be as accurate as possible. Innovation-based methods based on Desroziers diagnostics, are commonly used to estimate observation uncertainty, but such methods can depend greatly on the prescribed background uncertainty. For ensemble data assimilation, this uncertainty comes from statistics calculated from ensemble forecasts, which require inflation and localization to address under sampling. In this work, we use an Ensemble Kalman Filter (EnKF) with a low-dimensional Lorenz model to investigate the interplay between the Desroziers method and inflation. Two inflation techniques are used for this purpose: 1) a rigorously-tuned fixed multiplicative scheme and 2) an adaptive state-space scheme. We document how inaccuracies in observation uncertainty affect errors in EnKF posteriors and study the combined impacts of misspecified initial observation uncertainty, sampling error, and model error on Desroziers estimates. We find that whether observation uncertainty is over- or underestimated greatly affects the stability of data assimilation and the accuracy of Desroziers estimates and that preference should be given to initial overestimates. Inline estimates of Desroziers tend to remove the dependence between ensemble spread-skill and the initially prescribed observation error. Additionally, we find that the inclusion of model error introduces spurious correlations in observation uncertainty estimates. Further, we note that the adaptive inflation scheme is less robust than fixed inflation at mitigating multiple sources of error. Finally, sampling error strongly exacerbates existing sources of error and greatly degrades EnKF estimates, which translates into biased Desroziers estimates of observation error covariance.
{"title":"Challenges for Inline Observation Error Estimation in the Presence of Misspecified Background Uncertainty","authors":"Andrew Walsworth, J. Poterjoy, Elizabeth A. Satterfield","doi":"10.1175/mwr-d-22-0298.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0298.1","url":null,"abstract":"\u0000In order for data assimilation to provide faithful state estimates for dynamical models, specifications of observation uncertainty need to be as accurate as possible. Innovation-based methods based on Desroziers diagnostics, are commonly used to estimate observation uncertainty, but such methods can depend greatly on the prescribed background uncertainty. For ensemble data assimilation, this uncertainty comes from statistics calculated from ensemble forecasts, which require inflation and localization to address under sampling. In this work, we use an Ensemble Kalman Filter (EnKF) with a low-dimensional Lorenz model to investigate the interplay between the Desroziers method and inflation. Two inflation techniques are used for this purpose: 1) a rigorously-tuned fixed multiplicative scheme and 2) an adaptive state-space scheme. We document how inaccuracies in observation uncertainty affect errors in EnKF posteriors and study the combined impacts of misspecified initial observation uncertainty, sampling error, and model error on Desroziers estimates. We find that whether observation uncertainty is over- or underestimated greatly affects the stability of data assimilation and the accuracy of Desroziers estimates and that preference should be given to initial overestimates. Inline estimates of Desroziers tend to remove the dependence between ensemble spread-skill and the initially prescribed observation error. Additionally, we find that the inclusion of model error introduces spurious correlations in observation uncertainty estimates. Further, we note that the adaptive inflation scheme is less robust than fixed inflation at mitigating multiple sources of error. Finally, sampling error strongly exacerbates existing sources of error and greatly degrades EnKF estimates, which translates into biased Desroziers estimates of observation error covariance.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64696242","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}
Liangyi Wang, Xihui Gu, L. Slater, Yangchen Lai, Xiang Zhang, D. Kong, Jianyu Liu, Jianfeng Li
In July 2021, Typhoon In-Fa (TIF) triggered a significant indirect heavy precipitation event (HPE) in central China and a direct HPE in eastern China. Both these events led to severe disasters. However, the synoptic-scale conditions and the impacts of these HPEs on future estimations of return periods remain poorly understood. Here, we find that the remote HPE that occurred ~2200 km ahead of TIF over central China was a predecessor rain event (PRE). The PRE unfolded under the equatorward entrance of the upper-level westerly jet. This event, which encouraged divergent and adiabatic outflow in the upper level, subsequently intensified the strength of the upper-level westerly jet. In contrast, the direct HPE in eastern China was due primarily to the long duration and slow movement of TIF. The direct HPE occurred in areas situated less than 200 km from TIF’s center and to the left of TIF’s propagation trajectory. Anomaly analyses reveal favorable thermodynamic and dynamic conditions and abundant atmospheric moisture that sustained TIF’s intensity. A saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded TIF’s movement northward. Hydrologically, the inclusion of these two HPEs in the historical record leads to a decrease in the estimated return periods of similar HPEs. Our findings highlight the potential difficulties that HPEs could introduce for the design of hydraulic engineering infrastructure as well as for the disaster mitigation measures required to mitigate future risk, particularly in central China.
{"title":"Indirect and direct impacts of Typhoon In-Fa (2021) on heavy precipitation in inland and coastal areas of China: Synoptic-scale environments and return period analysis","authors":"Liangyi Wang, Xihui Gu, L. Slater, Yangchen Lai, Xiang Zhang, D. Kong, Jianyu Liu, Jianfeng Li","doi":"10.1175/mwr-d-22-0241.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0241.1","url":null,"abstract":"\u0000In July 2021, Typhoon In-Fa (TIF) triggered a significant indirect heavy precipitation event (HPE) in central China and a direct HPE in eastern China. Both these events led to severe disasters. However, the synoptic-scale conditions and the impacts of these HPEs on future estimations of return periods remain poorly understood. Here, we find that the remote HPE that occurred ~2200 km ahead of TIF over central China was a predecessor rain event (PRE). The PRE unfolded under the equatorward entrance of the upper-level westerly jet. This event, which encouraged divergent and adiabatic outflow in the upper level, subsequently intensified the strength of the upper-level westerly jet. In contrast, the direct HPE in eastern China was due primarily to the long duration and slow movement of TIF. The direct HPE occurred in areas situated less than 200 km from TIF’s center and to the left of TIF’s propagation trajectory. Anomaly analyses reveal favorable thermodynamic and dynamic conditions and abundant atmospheric moisture that sustained TIF’s intensity. A saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded TIF’s movement northward. Hydrologically, the inclusion of these two HPEs in the historical record leads to a decrease in the estimated return periods of similar HPEs. Our findings highlight the potential difficulties that HPEs could introduce for the design of hydraulic engineering infrastructure as well as for the disaster mitigation measures required to mitigate future risk, particularly in central China.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48870150","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}
Multiple parallel rain bands (MPRBs) involve the organization of mesoscale convective systems characterized by multiple parallel convective rain bands, which may produce high rainfall accumulation. A total of 178 MPRBs were identified from 2016–2020 in China, which were classified into the initiation type (~40%), where rain bands initiate individually, and differentiation type (~60%) where rain bands form through the splitting of larger rain band or merging of smaller cells. Results showed that the occurrence frequency of MPRBs peaks in July with a midnight major peak and a morning minor peak. The highest occurrence frequency is observed in the northern Beibu Gulf and its coastal areas, with minor high frequencies in Guangdong, northern Jiangxi, and southern Shandong provinces, typically in a southwesterly low-level jet to the west of the subtropical high. MPRBs mainly contain 3–4 rain bands with a spacing distance of 30–50 km and an orientation generally consistent with the direction of 850 hPa winds and 0–1 km vertical wind shear. MPRBs generally move slower than that of squall lines in East China ranging from 4 to 8 m s−1 with 16% being quasi-stationary, which is mainly due to the occurrence of band back building mainly associated with cold pool. Most MPRBs have training effects with band training as the dominant mode. Because of the band training effect and slower movement of MPRBs mainly due to band back building, 71% of MPRBs are associated with enhanced maximum hourly precipitation. Rainfall severity may be alleviated somewhat by the generally short duration of MPRBs with 78% being shorter than 2 h.
多平行雨带(MPRBs)涉及以多个平行对流雨带为特征的中尺度对流系统的组织，可能产生高的降水积累。2016-2020年，中国共鉴定出178个MPRBs，其中雨带单独形成的萌发型(~40%)和雨带通过大雨带分裂或小雨带合并形成的分化型(~60%)。结果表明:MPRBs的发生频率在7月达到高峰，表现为午夜高峰和早晨次要高峰;发生频率最高的是北部湾北部及其沿海地区，其次是广东、江西北部和山东南部，通常发生在副热带高压以西的西南低空急流中。MPRBs主要包含3-4个雨带，雨带间距为30-50 km，方向与850 hPa风方向和0-1 km垂直风切变方向基本一致。在4 ~ 8 m s−1范围内，MPRBs的移动速度普遍低于东部飑线的移动速度，其中有16%为准静止，这主要是由于出现了以冷池为主的回带建设。大多数MPRBs具有训练效果，以条带训练为主导模式。由于带状训练效应和带状回建导致的MPRBs移动速度较慢，71%的MPRBs与最大逐时降水增强有关。MPRBs持续时间普遍较短，78%的MPRBs持续时间短于2小时，可能在一定程度上减轻降雨的严重程度。
{"title":"General Features of MCSs with the Organization of Multiple Parallel Rain Bands in China","authors":"Peiyu Wang, Z. Meng","doi":"10.1175/mwr-d-22-0304.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0304.1","url":null,"abstract":"\u0000Multiple parallel rain bands (MPRBs) involve the organization of mesoscale convective systems characterized by multiple parallel convective rain bands, which may produce high rainfall accumulation. A total of 178 MPRBs were identified from 2016–2020 in China, which were classified into the initiation type (~40%), where rain bands initiate individually, and differentiation type (~60%) where rain bands form through the splitting of larger rain band or merging of smaller cells. Results showed that the occurrence frequency of MPRBs peaks in July with a midnight major peak and a morning minor peak. The highest occurrence frequency is observed in the northern Beibu Gulf and its coastal areas, with minor high frequencies in Guangdong, northern Jiangxi, and southern Shandong provinces, typically in a southwesterly low-level jet to the west of the subtropical high. MPRBs mainly contain 3–4 rain bands with a spacing distance of 30–50 km and an orientation generally consistent with the direction of 850 hPa winds and 0–1 km vertical wind shear. MPRBs generally move slower than that of squall lines in East China ranging from 4 to 8 m s−1 with 16% being quasi-stationary, which is mainly due to the occurrence of band back building mainly associated with cold pool. Most MPRBs have training effects with band training as the dominant mode. Because of the band training effect and slower movement of MPRBs mainly due to band back building, 71% of MPRBs are associated with enhanced maximum hourly precipitation. Rainfall severity may be alleviated somewhat by the generally short duration of MPRBs with 78% being shorter than 2 h.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64696253","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}
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA and, surprisingly, we find that it depends directly on the number of independent observations, but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
{"title":"How Sampling Errors in Covariance Estimates Cause Bias in the Kalman Gain and Impact Ensemble Data Assimilation","authors":"D. Hodyss, M. Morzfeld","doi":"10.1175/mwr-d-23-0029.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0029.1","url":null,"abstract":"\u0000Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA and, surprisingly, we find that it depends directly on the number of independent observations, but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43873862","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. Colle, Phillip Yeh, Joseph A. Finlon, L. McMurdie, V. McDonald, A. DeLaFrance
On 7 February 2020 a relatively deep cyclone (~980 hPa) with mid-level frontogenesis produced heavy snow (20-30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) model simulations at 2-km and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24 h runs realistically produced a broad sloping region of frontogenesis at mid-levels typically; however, there were relatively large (20–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (< 10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20–30% have too little ice aloft and at low-levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5–10% more precipitation than the 2-km grid.
{"title":"An Investigation of a Northeast U.S. Cyclone Event Without Well-Defined Snow Banding During IMPACTS","authors":"B. Colle, Phillip Yeh, Joseph A. Finlon, L. McMurdie, V. McDonald, A. DeLaFrance","doi":"10.1175/mwr-d-22-0296.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0296.1","url":null,"abstract":"\u0000On 7 February 2020 a relatively deep cyclone (~980 hPa) with mid-level frontogenesis produced heavy snow (20-30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) model simulations at 2-km and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24 h runs realistically produced a broad sloping region of frontogenesis at mid-levels typically; however, there were relatively large (20–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (< 10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20–30% have too little ice aloft and at low-levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5–10% more precipitation than the 2-km grid.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43415201","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}
Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ∼100 severe weather days per year and upwards of 1.3 million objectively tracked storms from 2010-2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports as well as the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single cell, multicell, or mesoscale convective system) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies.
{"title":"Development and Investigation of GridRad-Severe, a Multi-Year Severe Event Radar Dataset","authors":"A. Murphy, C. Homeyer, Kiley Q. Allen","doi":"10.1175/mwr-d-23-0017.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0017.1","url":null,"abstract":"\u0000Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ∼100 severe weather days per year and upwards of 1.3 million objectively tracked storms from 2010-2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports as well as the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single cell, multicell, or mesoscale convective system) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43187775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A multiscale alignment (MSA) ensemble filtering method was introduced by Ying to reduce nonlinear position errors effectively during data assimilation. The MSA method extends the traditional ensemble Kalman filter (EnKF) to update states from large to small scales sequentially, during which it leverages the displacement vectors derived from the large-scale analysis increments to reduce position errors at smaller scales through warping of the model grid. This study stress tests the MSA method in various scenarios using an idealized vortex model. We show that the MSA improves filter performance as number of scales (Ns) increases in the presence of nonlinear position errors. We tuned localization parameters for the cross-scale EnKF updates to find the best performance when assimilating an observation network. To further reduce the scale mismatch between observations and states, a new option called MSA-O is introduced to decompose observations into scale components during assimilation. Cycling DA experiments show that the MSA-O consistently outperforms the traditional EnKF at equal computational cost. A more challenging scenario for the MSA is identified when the large-scale background flow and the small-scale vortex are incoherent in terms of their errors, making the displacement vectors not effective in reducing vortex position errors. Observation availability for the small scales also limits the use of large Ns for the MSA. Potential remedies for these issues are discussed.
{"title":"Improving Vortex Position Accuracy with a New Multiscale Alignment Ensemble Filter","authors":"Y. Ying, Jeffrey L. Anderson, Laurent Bertino","doi":"10.1175/mwr-d-22-0140.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0140.1","url":null,"abstract":"\u0000A multiscale alignment (MSA) ensemble filtering method was introduced by Ying to reduce nonlinear position errors effectively during data assimilation. The MSA method extends the traditional ensemble Kalman filter (EnKF) to update states from large to small scales sequentially, during which it leverages the displacement vectors derived from the large-scale analysis increments to reduce position errors at smaller scales through warping of the model grid. This study stress tests the MSA method in various scenarios using an idealized vortex model. We show that the MSA improves filter performance as number of scales (Ns) increases in the presence of nonlinear position errors. We tuned localization parameters for the cross-scale EnKF updates to find the best performance when assimilating an observation network. To further reduce the scale mismatch between observations and states, a new option called MSA-O is introduced to decompose observations into scale components during assimilation. Cycling DA experiments show that the MSA-O consistently outperforms the traditional EnKF at equal computational cost. A more challenging scenario for the MSA is identified when the large-scale background flow and the small-scale vortex are incoherent in terms of their errors, making the displacement vectors not effective in reducing vortex position errors. Observation availability for the small scales also limits the use of large Ns for the MSA. Potential remedies for these issues are discussed.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42095918","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}
K. Ali, David M. Schultz, A. Revell, T. Stallard, P. Ouro
To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated turbulence and wind-speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind-speed differences in power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study.
{"title":"Assessment of five wind-farm parameterizations in the Weather Research and Forecasting model: A case study of wind farms in the North Sea","authors":"K. Ali, David M. Schultz, A. Revell, T. Stallard, P. Ouro","doi":"10.1175/mwr-d-23-0006.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0006.1","url":null,"abstract":"\u0000To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated turbulence and wind-speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind-speed differences in power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42365078","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}
Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar and two-dimensional vector data. The proposed method uses a fixed radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the Continuous Ranked Probability Score (CRPS) and the proposed score with a relatively small ensemble size (< 25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden-Julian Oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields.
{"title":"Information-based Probabilistic Verification Scores for Two-dimensional Ensemble Forecast Data: A Madden-Julian Oscillation Index Example","authors":"Y. Takaya, K. K. Komatsu, H. Hino, F. Vitart","doi":"10.1175/mwr-d-23-0003.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0003.1","url":null,"abstract":"\u0000Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar and two-dimensional vector data. The proposed method uses a fixed radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the Continuous Ranked Probability Score (CRPS) and the proposed score with a relatively small ensemble size (< 25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden-Julian Oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49450453","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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