A. Fujisaki‐Manome, D. Wright, G. Mann, Eric J. Anderson, P. Chu, C. Jablonowski, S. Benjamin
{"title":"预报湖/海效应暴风雪,进展和挑战","authors":"A. Fujisaki‐Manome, D. Wright, G. Mann, Eric J. Anderson, P. Chu, C. Jablonowski, S. Benjamin","doi":"10.1002/wat2.1594","DOIUrl":null,"url":null,"abstract":"Lake‐/sea‐effect snow forms typically from late fall to winter when a cold air mass moves over the warmer, large water surface. The resulting intense snowfall has many societal impacts on communities living in downwind areas; hence, accurate forecasts of lake‐/sea‐effect snow are essential for safety and preparedness. Forecasting lake‐/sea‐effect snow is extremely challenging, but over the past decades the advancement of numerical forecast models and the expansion of observational networks have incrementally improved the forecasting capability. The recent advancement includes numerical forecast models with high spatiotemporal resolutions that allow simulating vigorous snowstorms at the kilometer‐scale and the frequent inclusion of radar observations in the model. This combination of more accurate weather prediction models as well as ground‐based and remotely sensed observations has aided operational forecasters to make better lake‐/sea‐effect snow forecasts. A remaining challenge is that many observations of precipitation, surface meteorology, evaporation, and heat supply from the water surface are still limited to being land‐based and the information over the water, particularly offshore, remains a gap. This primer overviews the basic mechanisms for lake‐/sea‐effect snow formation, evolution of forecast techniques, and challenges to be addressed in the future.","PeriodicalId":23774,"journal":{"name":"Wiley Interdisciplinary Reviews: Water","volume":"59 1","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Forecasting lake‐/sea‐effect snowstorms, advancement, and challenges\",\"authors\":\"A. Fujisaki‐Manome, D. Wright, G. Mann, Eric J. Anderson, P. Chu, C. Jablonowski, S. Benjamin\",\"doi\":\"10.1002/wat2.1594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lake‐/sea‐effect snow forms typically from late fall to winter when a cold air mass moves over the warmer, large water surface. The resulting intense snowfall has many societal impacts on communities living in downwind areas; hence, accurate forecasts of lake‐/sea‐effect snow are essential for safety and preparedness. Forecasting lake‐/sea‐effect snow is extremely challenging, but over the past decades the advancement of numerical forecast models and the expansion of observational networks have incrementally improved the forecasting capability. The recent advancement includes numerical forecast models with high spatiotemporal resolutions that allow simulating vigorous snowstorms at the kilometer‐scale and the frequent inclusion of radar observations in the model. This combination of more accurate weather prediction models as well as ground‐based and remotely sensed observations has aided operational forecasters to make better lake‐/sea‐effect snow forecasts. A remaining challenge is that many observations of precipitation, surface meteorology, evaporation, and heat supply from the water surface are still limited to being land‐based and the information over the water, particularly offshore, remains a gap. This primer overviews the basic mechanisms for lake‐/sea‐effect snow formation, evolution of forecast techniques, and challenges to be addressed in the future.\",\"PeriodicalId\":23774,\"journal\":{\"name\":\"Wiley Interdisciplinary Reviews: Water\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2022-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wiley Interdisciplinary Reviews: Water\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1002/wat2.1594\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews: Water","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1002/wat2.1594","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Forecasting lake‐/sea‐effect snowstorms, advancement, and challenges
Lake‐/sea‐effect snow forms typically from late fall to winter when a cold air mass moves over the warmer, large water surface. The resulting intense snowfall has many societal impacts on communities living in downwind areas; hence, accurate forecasts of lake‐/sea‐effect snow are essential for safety and preparedness. Forecasting lake‐/sea‐effect snow is extremely challenging, but over the past decades the advancement of numerical forecast models and the expansion of observational networks have incrementally improved the forecasting capability. The recent advancement includes numerical forecast models with high spatiotemporal resolutions that allow simulating vigorous snowstorms at the kilometer‐scale and the frequent inclusion of radar observations in the model. This combination of more accurate weather prediction models as well as ground‐based and remotely sensed observations has aided operational forecasters to make better lake‐/sea‐effect snow forecasts. A remaining challenge is that many observations of precipitation, surface meteorology, evaporation, and heat supply from the water surface are still limited to being land‐based and the information over the water, particularly offshore, remains a gap. This primer overviews the basic mechanisms for lake‐/sea‐effect snow formation, evolution of forecast techniques, and challenges to be addressed in the future.
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