{"title":"Equity, Inclusion, and Justice: An Opportunity for Action for AMS Publications Stakeholders","authors":"","doi":"10.1175/waf-d-23-0133.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0133.1","url":null,"abstract":"","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":"47606949","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}
Maria Czarina M. Tierra, Tzu‐Ting Lo, Hsiao-Chung Tsai, M. Villafuerte
In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional timescales covered by weather forecasting in the Philippines, this study has examined the multi-week (i.e., from Week-1 to Week-4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely: NCEP Climate Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 06 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multi-week TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p<0.05) positional bias toward the right of observed tracks beyond Week-1, and that they tend to propagate slower than observations especially in Week-1 and Week-2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multi-week TC forecasts in the Philippines.
{"title":"Evaluation of Multi-Week Tropical Cyclone Forecasts in the Philippines","authors":"Maria Czarina M. Tierra, Tzu‐Ting Lo, Hsiao-Chung Tsai, M. Villafuerte","doi":"10.1175/waf-d-22-0173.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0173.1","url":null,"abstract":"\u0000In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional timescales covered by weather forecasting in the Philippines, this study has examined the multi-week (i.e., from Week-1 to Week-4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely: NCEP Climate Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 06 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multi-week TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p<0.05) positional bias toward the right of observed tracks beyond Week-1, and that they tend to propagate slower than observations especially in Week-1 and Week-2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multi-week TC forecasts in the Philippines.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45793164","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}
G. Chirokova, J. Knaff, M. Brennan, Robert T. Demaria, M. Bozeman, S. N. Stevenson, J. Beven, E. Blake, Alan Brammer, James W. E. Darlow, M. DeMaria, S. Miller, C. Slocum, Debra A. Molenar, D. Hillger
Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS Day/Night Band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES- 16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center.
{"title":"ProxyVis – a Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations","authors":"G. Chirokova, J. Knaff, M. Brennan, Robert T. Demaria, M. Bozeman, S. N. Stevenson, J. Beven, E. Blake, Alan Brammer, James W. E. Darlow, M. DeMaria, S. Miller, C. Slocum, Debra A. Molenar, D. Hillger","doi":"10.1175/waf-d-23-0038.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0038.1","url":null,"abstract":"\u0000Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options.\u0000ProxyVis was trained on the VIIRS Day/Night Band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery.\u0000ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training.\u0000ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES- 16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738096","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}
This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multi-storm configuration of the Hurricane Weather Research and Forecasting model (HWRF) and covered active periods of the 2017–2020 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve’s sampling strategy in 2018 that added dropsondes outside of the inner core.
{"title":"A Comparison of the Impacts of Inner-Core, In-Vortex, and Environmental Dropsondes on Tropical Cyclone Forecasts during the 2017-2020 Hurricane Seasons","authors":"Sarah D. Ditchek, J. Sippel","doi":"10.1175/waf-d-23-0055.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0055.1","url":null,"abstract":"\u0000This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multi-storm configuration of the Hurricane Weather Research and Forecasting model (HWRF) and covered active periods of the 2017–2020 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve’s sampling strategy in 2018 that added dropsondes outside of the inner core.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46469929","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}
Shakira D. Stackhouse, Stephanie E. Zick, C. Matyas, Kimberly M. Wood, A. Hazelton, G. Alaka
Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-hour precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the Basin-scale Hurricane Weather Research and Forecasting (HWRF-B) model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first six hours after initialization. No such spin-up issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain rate thresholds. Future work will investigate spin-up issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.
{"title":"Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation using Object-Based Metrics","authors":"Shakira D. Stackhouse, Stephanie E. Zick, C. Matyas, Kimberly M. Wood, A. Hazelton, G. Alaka","doi":"10.1175/waf-d-22-0223.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0223.1","url":null,"abstract":"\u0000Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-hour precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the Basin-scale Hurricane Weather Research and Forecasting (HWRF-B) model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first six hours after initialization. No such spin-up issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain rate thresholds. Future work will investigate spin-up issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48292362","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}
Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. In order to obtain a best possible initial state for the lower boundary of the NWP system, the land surface model is re-run between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2 meter temperature root mean square errors and mean errors of 6% and 12% respectively, and 4.5% and 11% for 2 meter specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts (above 30 mm per 12 hour) down to a 100 km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and further the forecasts of screen level parameters. The experiments also indicate diurnal and weather dependent variation of the forecast errors that give valuable insight on the role of initial land surface conditions and the land-atmosphere interactions in southern Scandinavia.
{"title":"Using a reanalysis driven land surface model for initialization of a numerical weather prediction system","authors":"Å. Bakketun, Jostein Blyverket, Malte Müller","doi":"10.1175/waf-d-22-0184.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0184.1","url":null,"abstract":"\u0000Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. In order to obtain a best possible initial state for the lower boundary of the NWP system, the land surface model is re-run between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2 meter temperature root mean square errors and mean errors of 6% and 12% respectively, and 4.5% and 11% for 2 meter specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts (above 30 mm per 12 hour) down to a 100 km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and further the forecasts of screen level parameters. The experiments also indicate diurnal and weather dependent variation of the forecast errors that give valuable insight on the role of initial land surface conditions and the land-atmosphere interactions in southern Scandinavia.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41922811","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}
Deep-layer vertical wind shear and mid-tropospheric relative humidity (RH) are explored in and around environments of all intensifying North Atlantic tropical cyclones (TCs) between 1980–2021 using reanalysis data. Shear and RH are averaged within the standard environmental annulus of 200–800-km, along with a 100–600-km annulus, and a 0–250-km radius to represent the inner core and TC itself. Distributions of shear and RH at onset along with a time series of evolution from 48 h prior to and after onset of three different intensification rates: slight (5–10 kt 24 h−1), moderate (15–25 kt 24 h−1), and rapid (≥ 30 kt 24 h−1), are analyzed. RH is also investigated within different shear environments and in shear-relative quadrants around the storm. While low shear and high RH are found to be most favorable for rapid intensification (RI), there is still a significant probability that RI will occur within less favorable environments. RI cases decrease in 850–200-hPa shear in the 24 h leading up to RI, whereas slight intensification cases increase, which is evident in both the standard shear and a shallower layer at 48 h prior to onset. The inner-core RH for RI increases prior to onset whereas it decreases in the environments. RH analysis by shear-relative quadrants demonstrates the importance of moistening in the upshear-right quadrant before onset of RI. Results indicate the potential value of multiple annuli and shear-relative analysis for moisture and a shallower, 925–400-hPa layer for shear in RI forecasting.
{"title":"Revisiting Environmental Wind and Moisture Calculations in the Context of Tropical Cyclone Intensification","authors":"Samantha Nebylitsa, S. Majumdar, D. Nolan","doi":"10.1175/waf-d-23-0045.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0045.1","url":null,"abstract":"\u0000Deep-layer vertical wind shear and mid-tropospheric relative humidity (RH) are explored in and around environments of all intensifying North Atlantic tropical cyclones (TCs) between 1980–2021 using reanalysis data. Shear and RH are averaged within the standard environmental annulus of 200–800-km, along with a 100–600-km annulus, and a 0–250-km radius to represent the inner core and TC itself. Distributions of shear and RH at onset along with a time series of evolution from 48 h prior to and after onset of three different intensification rates: slight (5–10 kt 24 h−1), moderate (15–25 kt 24 h−1), and rapid (≥ 30 kt 24 h−1), are analyzed. RH is also investigated within different shear environments and in shear-relative quadrants around the storm. While low shear and high RH are found to be most favorable for rapid intensification (RI), there is still a significant probability that RI will occur within less favorable environments. RI cases decrease in 850–200-hPa shear in the 24 h leading up to RI, whereas slight intensification cases increase, which is evident in both the standard shear and a shallower layer at 48 h prior to onset. The inner-core RH for RI increases prior to onset whereas it decreases in the environments. RH analysis by shear-relative quadrants demonstrates the importance of moistening in the upshear-right quadrant before onset of RI. Results indicate the potential value of multiple annuli and shear-relative analysis for moisture and a shallower, 925–400-hPa layer for shear in RI forecasting.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43785010","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}
The prediction of snow accumulation remains a forecasting challenge. While the adoption of ensemble numerical weather prediction has enabled the development of probabilistic guidance, the challenges associated with snow accumulation, particularly snow-to-liquid ratio (SLR), still remain when building snow-accumulation tools. In operations, SLR is generally assumed to either fit a simple mathematical relationship or conform to a historic average. In this paper, the impacts of the choice of SLR on ensemble snow forecasts are tested. Ensemble forecasts from the nine-member High Resolution Rapid Refresh Ensemble (HRRRE) were used to create 24-hour snowfall forecasts for five snowfall events associated with winter cyclones. These snowfall forecasts were derived from model liquid precipitation forecasts using five SLR relationships. These forecasts were evaluated against daily new snowfall observations from the Community Collaborative Rain Hail and Snow network. The results of this analysis show that the forecast error associated with individual members is similar to the error associated with choice of SLR. The SLR with the lowest forecast error showed regional agreement across nearby observations. This suggests that, while there is no one SLR that works best everywhere, it may be possible to improve ensemble snow forecasts if regions where SLRs perform best can be determined ahead of time. The implications of these findings for future ensemble snowfall tools will be discussed.
{"title":"Evaluation of Ensemble Snowfall Forecasts using Operationally-used Snow-to-Liquid Ratios in Five Winter Storms","authors":"Andrew A. Rosenow, H. Reeves, Daniel D. Tripp","doi":"10.1175/waf-d-22-0202.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0202.1","url":null,"abstract":"\u0000The prediction of snow accumulation remains a forecasting challenge. While the adoption of ensemble numerical weather prediction has enabled the development of probabilistic guidance, the challenges associated with snow accumulation, particularly snow-to-liquid ratio (SLR), still remain when building snow-accumulation tools. In operations, SLR is generally assumed to either fit a simple mathematical relationship or conform to a historic average. In this paper, the impacts of the choice of SLR on ensemble snow forecasts are tested.\u0000Ensemble forecasts from the nine-member High Resolution Rapid Refresh Ensemble (HRRRE) were used to create 24-hour snowfall forecasts for five snowfall events associated with winter cyclones. These snowfall forecasts were derived from model liquid precipitation forecasts using five SLR relationships. These forecasts were evaluated against daily new snowfall observations from the Community Collaborative Rain Hail and Snow network. The results of this analysis show that the forecast error associated with individual members is similar to the error associated with choice of SLR. The SLR with the lowest forecast error showed regional agreement across nearby observations. This suggests that, while there is no one SLR that works best everywhere, it may be possible to improve ensemble snow forecasts if regions where SLRs perform best can be determined ahead of time. The implications of these findings for future ensemble snowfall tools will be discussed.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46686448","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 proof-of-concept systematic evaluation of convective hazards is applied to short-term (1-6 h) forecasts using the Morrison, National Severe Storms Laboratory (NSSL), Predicted Particle Properties (P3), and Thompson two-moment microphysics schemes for the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (HWT SFE) period (hereafter “MORR”, “NSSL”, “P3”, and “THOM” experiments, respectively). Four convective line cases are highlighted to elaborate on relative experiment biases/skill. Composite reflectivity and 1-h accumulated precipitation are examined to determine storm coverage/precipitation biases/skill utilizing point-based verification with a neighborhood. Simulated 1-6 km updraft helicity and observed 3-6 km azimuthal shear, and MESH are examined to consider simulated rotation and hail core prediction with object-based scores. Over the full season, MORR displays little overall storm coverage bias relative to NSSL, P3, and THOM underprediction. The equitable threat score (ETS) and fractions skill score (FSS) of P3 are lower than the other experiments. P3 and THOM underpredict convective regions with intense reflectivity relative to MORR and NSSL overprediction. All experiments underpredict precipitation amounts. P3 light precipitation FSS is lower than other experiments. Rotation object verification exhibits sensitivity to microphysics experiments, as microphysics has an indirect influence on storm dynamics. While P3 has the largest hail object underprediction, all experiments grossly overpredict the number of hail objects in convective line cases despite forecast objects defined with the same product (MESH) and threshold as observations. The importance of microphysics ice parameterization and ongoing scheme updates highlight the need to apply this verification framework to optimal/updated schemes before optimizing ensemble design.
{"title":"Biases and Skill of Four Two-Moment Bulk Microphysics Schemes in Convection-Allowing Forecasts for the 2018 Hazardous Weather Testbed Spring Forecasting Experiment Period","authors":"M. Johnson, M. Xue, Youngsun Jung","doi":"10.1175/waf-d-22-0171.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0171.1","url":null,"abstract":"\u0000A proof-of-concept systematic evaluation of convective hazards is applied to short-term (1-6 h) forecasts using the Morrison, National Severe Storms Laboratory (NSSL), Predicted Particle Properties (P3), and Thompson two-moment microphysics schemes for the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (HWT SFE) period (hereafter “MORR”, “NSSL”, “P3”, and “THOM” experiments, respectively). Four convective line cases are highlighted to elaborate on relative experiment biases/skill. Composite reflectivity and 1-h accumulated precipitation are examined to determine storm coverage/precipitation biases/skill utilizing point-based verification with a neighborhood. Simulated 1-6 km updraft helicity and observed 3-6 km azimuthal shear, and MESH are examined to consider simulated rotation and hail core prediction with object-based scores.\u0000Over the full season, MORR displays little overall storm coverage bias relative to NSSL, P3, and THOM underprediction. The equitable threat score (ETS) and fractions skill score (FSS) of P3 are lower than the other experiments. P3 and THOM underpredict convective regions with intense reflectivity relative to MORR and NSSL overprediction. All experiments underpredict precipitation amounts. P3 light precipitation FSS is lower than other experiments. Rotation object verification exhibits sensitivity to microphysics experiments, as microphysics has an indirect influence on storm dynamics. While P3 has the largest hail object underprediction, all experiments grossly overpredict the number of hail objects in convective line cases despite forecast objects defined with the same product (MESH) and threshold as observations. The importance of microphysics ice parameterization and ongoing scheme updates highlight the need to apply this verification framework to optimal/updated schemes before optimizing ensemble design.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42316332","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}
While prior research has shown that characteristics of the supercell environment can indicate the likelihood of tornadogenesis, it is common for tornadic and non-tornadic supercells to coexist in seemingly similar environments. Thus, some small-scale factors must support tornadogenesis in some supercells and not in others. In this study we examined polarimetric radar signatures of proximate pretornadic and non-tornadic supercells in seemingly similar environments to determine if these radar signatures can indicate which proximate supercells are pretornadic and which are non-tornadic. We gathered a collection of proximity supercell groups and developed a method to quantify environmental similarity between storms. Using this method, we selected pretornadic – non-tornadic supercell pairs in close proximity in space and time having the most similar environments. These pairs were run through an automated tracking algorithm which quantifies polarimetric signatures in each supercell. Supercells with larger differential reflectivity (ZDR) column areas were more likely to become tornadic within the next 30 minutes compared to neighboring supercells with smaller ZDR column areas. In about two-thirds of pairs, the pretornadic supercell had a larger ZDR column area than the non-tornadic supercell prior to its maximum low-level rotation, which is consistent with much prior work. ZDR arcs could not discriminate between pretornadic and non-tornadic supercells, and hailfall area was larger in pretornadic supercells. The separation distance between the specific differential phase (KDP) foot and the ZDR arc was larger in pretornadic supercells yet was a limited result due to the small sample size used for comparison.
{"title":"Comparing Polarimetric Signatures of Proximate Pretornadic and Non-Tornadic Supercells in Similar Environments","authors":"Devon J. Healey, Matthew S. Van Den Broeke","doi":"10.1175/waf-d-23-0013.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0013.1","url":null,"abstract":"\u0000While prior research has shown that characteristics of the supercell environment can indicate the likelihood of tornadogenesis, it is common for tornadic and non-tornadic supercells to coexist in seemingly similar environments. Thus, some small-scale factors must support tornadogenesis in some supercells and not in others. In this study we examined polarimetric radar signatures of proximate pretornadic and non-tornadic supercells in seemingly similar environments to determine if these radar signatures can indicate which proximate supercells are pretornadic and which are non-tornadic. We gathered a collection of proximity supercell groups and developed a method to quantify environmental similarity between storms. Using this method, we selected pretornadic – non-tornadic supercell pairs in close proximity in space and time having the most similar environments. These pairs were run through an automated tracking algorithm which quantifies polarimetric signatures in each supercell. Supercells with larger differential reflectivity (ZDR) column areas were more likely to become tornadic within the next 30 minutes compared to neighboring supercells with smaller ZDR column areas. In about two-thirds of pairs, the pretornadic supercell had a larger ZDR column area than the non-tornadic supercell prior to its maximum low-level rotation, which is consistent with much prior work. ZDR arcs could not discriminate between pretornadic and non-tornadic supercells, and hailfall area was larger in pretornadic supercells. The separation distance between the specific differential phase (KDP) foot and the ZDR arc was larger in pretornadic supercells yet was a limited result due to the small sample size used for comparison.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46544406","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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