Seasonal prediction potential for springtime dustiness in the United States
Most 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.
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http://n2t.net/ark:/85065/d7ms3wvr
eng
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2016-01-01T00:00:00Z
publication
2019-08-16T00:00:00Z
Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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