{"title":"2018年危险天气试验台春季预报试验期对流容许预报中四种二矩体微物理方案的偏误与技巧","authors":"M. Johnson, M. Xue, Youngsun Jung","doi":"10.1175/waf-d-22-0171.1","DOIUrl":null,"url":null,"abstract":"\nA 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.\nOver 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":3.0000,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":null,\"url\":null,\"abstract\":\"\\nA 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.\\nOver 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\":3.0000,\"publicationDate\":\"2023-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Weather and Forecasting\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1175/waf-d-22-0171.1\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Weather and Forecasting","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/waf-d-22-0171.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Biases and Skill of Four Two-Moment Bulk Microphysics Schemes in Convection-Allowing Forecasts for the 2018 Hazardous Weather Testbed Spring Forecasting Experiment Period
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.
期刊介绍:
Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.