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dc.contributor.authorPu, Bing
dc.contributor.authorGinoux, Paul
dc.contributor.authorKapnick, Sarah B.
dc.contributor.authorYang, Xiaosong
dc.date.accessioned2019-08-22T16:20:53Z
dc.date.available2019-08-22T16:20:53Z
dc.date.issued2019-07-19
dc.identifier.citationPu, B., Ginoux, P., Kapnick, S., & Yang,X. (2019). Seasonal prediction potential for springtime dustiness in the United States. Geophysical Research Letters,46. https://doi.org/10.1029/2019GL083703en_US
dc.identifier.urihttp://hdl.handle.net/1808/29445
dc.description.abstractMost dust forecast models focus on short, subseasonal lead times, that is, 3 to 6 days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the United States using an observation‐constrained regression model and key variables predicted by a seasonal prediction model developed at National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Forecast‐Oriented Low Ocean Resolution (FLOR) model. Our method shows skillful predictions of spring dustiness 3 to 6 months in advance. It is found that the regression model explains about 71% of the variances of dust event frequency over the Great Plains and 63% over the southwestern United States in March‐May from 2004 to 2016 using predictors from FLOR initialized on 1 December. Variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors. Findings here will help development of a seasonal dust prediction system and hazard prevention.en_US
dc.description.sponsorshipNASA (NNH14ZDA001N-ACMAP, NNH16ZDA001N-MAP)en_US
dc.description.sponsorshipPrinceton University's Cooperative Institute for Climate Scienceen_US
dc.description.sponsorshipNOAAen_US
dc.publisherAmerican Geophysical Unionen_US
dc.subjectDusten_US
dc.subjectSeasonal predictionen_US
dc.subjectUnited Statesen_US
dc.subjectFLOR statistical modelen_US
dc.titleSeasonal Prediction Potential for Springtime Dustiness in the United Statesen_US
dc.typeArticleen_US
kusw.kuauthorPu, Bing
kusw.kudepartmentGeography and Atmospheric Scienceen_US
dc.identifier.doi10.1029/2019GL083703en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-7620-8460en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3642-2988en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-0979-3070en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5601-9710en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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