Heather Dawn Reeves, Daniel D. Tripp, Michael E. Baldwin, Andrew A. Rosenow
Abstract Several new precipitation-type algorithms have been developed to improve NWP predictions of surface precipitation type during winter storms. In this study, we evaluate whether it is possible to objectively declare one algorithm as superior to another through comparison of three precipitation-type algorithms when validated using different techniques. The apparent skill of the algorithms is dependent on the choice of performance metric – algorithms can have high scores for some metrics and poor scores for others. It is also possible for an algorithm to have high skill at diagnosing some precipitation types and poor skill with others. Algorithm skill is also highly dependent on the choice of verification data/methodology. Just by changing what data is considered “truth,” we were able to substantially change the apparent skill of all algorithms evaluated herein. These findings suggest an objective declaration of algorithm “goodness” is not possible. Moreover, they indicate the unambiguous declaration of superiority is difficult, if not impossible. A contributing factor to algorithm performance is uncertainty of the microphysical processes that lead to phase changes of falling hydrometeors, which are treated differently by each algorithm thus resulting in different biases in near-0°C environments. These biases are evident even when algorithms are applied to ensemble forecasts. Hence, a multi-algorithm approach is advocated to account for this source of uncertainty. Though the apparent performance of this approach is still dependent on the choice of performance metric and precipitation type, a case-study analysis shows it has the potential to provide better decision support than the single-algorithm approach.
{"title":"Statistical evaluation of different surface precipitation-type algorithms and its implications for NWP prediction and operational decision making","authors":"Heather Dawn Reeves, Daniel D. Tripp, Michael E. Baldwin, Andrew A. Rosenow","doi":"10.1175/waf-d-23-0081.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0081.1","url":null,"abstract":"Abstract Several new precipitation-type algorithms have been developed to improve NWP predictions of surface precipitation type during winter storms. In this study, we evaluate whether it is possible to objectively declare one algorithm as superior to another through comparison of three precipitation-type algorithms when validated using different techniques. The apparent skill of the algorithms is dependent on the choice of performance metric – algorithms can have high scores for some metrics and poor scores for others. It is also possible for an algorithm to have high skill at diagnosing some precipitation types and poor skill with others. Algorithm skill is also highly dependent on the choice of verification data/methodology. Just by changing what data is considered “truth,” we were able to substantially change the apparent skill of all algorithms evaluated herein. These findings suggest an objective declaration of algorithm “goodness” is not possible. Moreover, they indicate the unambiguous declaration of superiority is difficult, if not impossible. A contributing factor to algorithm performance is uncertainty of the microphysical processes that lead to phase changes of falling hydrometeors, which are treated differently by each algorithm thus resulting in different biases in near-0°C environments. These biases are evident even when algorithms are applied to ensemble forecasts. Hence, a multi-algorithm approach is advocated to account for this source of uncertainty. Though the apparent performance of this approach is still dependent on the choice of performance metric and precipitation type, a case-study analysis shows it has the potential to provide better decision support than the single-algorithm approach.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246910","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}
Stanley G. Benjamin, Eric P. James, Edward J. Szoke, Paul T. Schlatter, John M. Brown
Abstract The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
{"title":"The 30 December 2021 Colorado Front Range windstorm and Marshall Fire: Evolution of surface and 3-d structure, NWP guidance, NWS forecasts and decision support","authors":"Stanley G. Benjamin, Eric P. James, Edward J. Szoke, Paul T. Schlatter, John M. Brown","doi":"10.1175/waf-d-23-0086.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0086.1","url":null,"abstract":"Abstract The Marshall Fire on 30 December 2021 became the most destructive wildfire cost-wise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly eleven hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the mid-level inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a high-wind warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a county-wide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts – important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816282","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}
Nicholas A. Gasperoni, Xuguang Wang, Yongming Wang, Tsung-Han Li
Abstract Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS), and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best performing individual component VTS. Ten-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1-6 while Both/ConVTS had better skill at later hours 7-15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits.
{"title":"Evaluating the multiscale implementation of valid time shifting within a real-time EnVar data assimilation and forecast system for the 2022 HWT Spring Forecasting Experiment","authors":"Nicholas A. Gasperoni, Xuguang Wang, Yongming Wang, Tsung-Han Li","doi":"10.1175/waf-d-23-0096.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0096.1","url":null,"abstract":"Abstract Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS), and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best performing individual component VTS. Ten-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1-6 while Both/ConVTS had better skill at later hours 7-15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136136399","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 We evaluated the ability of the fvGFS with a 13-km resolution in simulating tropical cyclone genesis (TCG) by conducting hindcast experiments for 42 TCG events over 2018–2019 in the Western North Pacific (WNP). We observed an improved hit rate with a lead time of between 5 and 4 days; however, from 4 to 3 days lead time, no consistent improvement in the temporal and spatial errors of TCG was obtained. More Fail cases occurred when and where a low-level easterly background flow prevailed: from mid-August to September 2018 and after October 2019 and mainly in the eastern WNP. In Hit cases, 850-hPa stream function and divergence, 200-hPa divergence, and genesis potential index (GPI) provided favorable TCG conditions. However, the Hit–Fail case differences in other suggested factors (vertical wind shear, 700-hPa moisture, and SST) were nonsignificant. By contrast, the reanalysis used for validation showed only significant difference in 850-hPa stream function. We stratified the background flow of TCG into four types. The monsoon trough type (82%) provided the most favorable environmental conditions for successful hindcasts, followed by the subtropical high (45%), easterly (17%), and others (0%) types. These results indicated that fvGFS is more capable of enhancing monsoon trough circulation and provides a much better environment for TCG development but is less skillful in other types of background flow that provides weaker large-scale forcing. The results suggest that the most advanced high-resolution weather forecast models such as the fvGFS warrants further improvement to properly simulate the subtle circulation features (e.g., mesoscale convection system) that might provide seeds for TCG.
{"title":"Assessment of an Experimental Version of fvGFS for TC Genesis Forecasting Ability in the Western North Pacific","authors":"Shu-Jeng Lin, Huang-Hsiung Hsu, Chia-Ying Tu, Cheng-Hsiang Chih","doi":"10.1175/waf-d-23-0056.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0056.1","url":null,"abstract":"Abstract We evaluated the ability of the fvGFS with a 13-km resolution in simulating tropical cyclone genesis (TCG) by conducting hindcast experiments for 42 TCG events over 2018–2019 in the Western North Pacific (WNP). We observed an improved hit rate with a lead time of between 5 and 4 days; however, from 4 to 3 days lead time, no consistent improvement in the temporal and spatial errors of TCG was obtained. More Fail cases occurred when and where a low-level easterly background flow prevailed: from mid-August to September 2018 and after October 2019 and mainly in the eastern WNP. In Hit cases, 850-hPa stream function and divergence, 200-hPa divergence, and genesis potential index (GPI) provided favorable TCG conditions. However, the Hit–Fail case differences in other suggested factors (vertical wind shear, 700-hPa moisture, and SST) were nonsignificant. By contrast, the reanalysis used for validation showed only significant difference in 850-hPa stream function. We stratified the background flow of TCG into four types. The monsoon trough type (82%) provided the most favorable environmental conditions for successful hindcasts, followed by the subtropical high (45%), easterly (17%), and others (0%) types. These results indicated that fvGFS is more capable of enhancing monsoon trough circulation and provides a much better environment for TCG development but is less skillful in other types of background flow that provides weaker large-scale forcing. The results suggest that the most advanced high-resolution weather forecast models such as the fvGFS warrants further improvement to properly simulate the subtle circulation features (e.g., mesoscale convection system) that might provide seeds for TCG.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135880740","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}
Kevin C. Thiel, Kristin M. Calhoun, Anthony E. Reinhart
The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly-updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including Flash Extent Density (FED), Minimum Flash Area (MFA), and Total Optical Energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April through 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210 to 230 K, and isothermal reflectivity at −10 °C of 20 to 30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts.
{"title":"Forecast Applications of GLM Gridded Products: A Data Fusion Perspective","authors":"Kevin C. Thiel, Kristin M. Calhoun, Anthony E. Reinhart","doi":"10.1175/waf-d-23-0078.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0078.1","url":null,"abstract":"\u0000The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly-updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including Flash Extent Density (FED), Minimum Flash Area (MFA), and Total Optical Energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April through 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210 to 230 K, and isothermal reflectivity at −10 °C of 20 to 30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47524595","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}
Extreme fire weather and fire behavior occurred during the New Year’s Eve period 30-31 December 2019 in southeast New South Wales, Australia. Fire progressed rapidly during the late evening and early morning periods, and significant extreme pyrocumulonimbus behavior developed, sometimes repeatedly in the same area. This occurred within a broader context of an unprecedented fire season in eastern Australia. Several aspects of the synoptic and mesoscale meteorology are examined, to identify contributions to fire behavior during this period. The passage of a cold front through the region was a key factor in the event, but other processes contributed to the severity of fire weather. Additional important features during this period included the movement of a negatively tilted upper tropospheric trough, the interaction of the front with topography and the occurrence of low-level overnight jets and of horizontal boundary layer rolls in the vicinity of the fireground.
{"title":"Synoptic and mesoscale aspects of exceptional fire weather during the New Year period 2019-20 in southeastern New South Wales, Australia","authors":"Paul Fox‐Hughes","doi":"10.1175/waf-d-23-0007.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0007.1","url":null,"abstract":"\u0000Extreme fire weather and fire behavior occurred during the New Year’s Eve period 30-31 December 2019 in southeast New South Wales, Australia. Fire progressed rapidly during the late evening and early morning periods, and significant extreme pyrocumulonimbus behavior developed, sometimes repeatedly in the same area. This occurred within a broader context of an unprecedented fire season in eastern Australia. Several aspects of the synoptic and mesoscale meteorology are examined, to identify contributions to fire behavior during this period. The passage of a cold front through the region was a key factor in the event, but other processes contributed to the severity of fire weather. Additional important features during this period included the movement of a negatively tilted upper tropospheric trough, the interaction of the front with topography and the occurrence of low-level overnight jets and of horizontal boundary layer rolls in the vicinity of the fireground.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47991585","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}
Barbara Brown, Louisa Nance, Christopher Williams, Kathryn Newman, James Franklin, Edward Rappaport, Paul Kucera, Robert Gall
The Hurricane Forecast Improvement Project1 (HFIP) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010–2014 in the period preceding the hurricane season (defined as August through October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.
{"title":"User-responsive diagnostic forecast evaluation approaches: Application to tropical cyclone predictions","authors":"Barbara Brown, Louisa Nance, Christopher Williams, Kathryn Newman, James Franklin, Edward Rappaport, Paul Kucera, Robert Gall","doi":"10.1175/waf-d-23-0072.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0072.1","url":null,"abstract":"\u0000The Hurricane Forecast Improvement Project1 (HFIP) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010–2014 in the period preceding the hurricane season (defined as August through October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43331002","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}
Kyle K. Hugeback, W. Gallus, Hugo N. Villegas Pico
The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.
{"title":"Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations","authors":"Kyle K. Hugeback, W. Gallus, Hugo N. Villegas Pico","doi":"10.1175/waf-d-23-0023.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0023.1","url":null,"abstract":"\u0000The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41636664","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}
Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts. Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.
{"title":"Winter Precipitation Type from Microwave Radiometers in New York State Mesonet Profiler Network","authors":"B. Shrestha, June Wang, J. Brotzge, N. Bain","doi":"10.1175/waf-d-23-0035.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0035.1","url":null,"abstract":"\u0000Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts.\u0000\u0000\u0000Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43928406","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}
Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts. Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation.
{"title":"Conditional Ensemble Model Output Statistics for Postprocessing of Ensemble Precipitation Forecasting","authors":"Yan Ji, Xiefei Zhi, Lu-ying Ji, Tingbo Peng","doi":"10.1175/waf-d-22-0190.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0190.1","url":null,"abstract":"\u0000Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts.\u0000\u0000\u0000Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49179026","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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