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Seasonal Prediction Potential for Springtime Dustiness in the United States
Pu, Bing Ginoux, Paul Kapnick, Sarah B. Yang, Xiaosong
Pu, Bing
Ginoux, Paul
Kapnick, Sarah B.
Yang, Xiaosong
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Abstract
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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Date
2019-07-19
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Publisher
American Geophysical Union
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Keywords
Dust, Seasonal prediction, United States, FLOR statistical model
Citation
Pu, 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/10.1029/2019GL083703