edu.ucar.opensky::articles:22807 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:27:16.049327 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7ms3wvr Seasonal prediction potential for springtime dustiness in the United States 2019-08-16T00:00:00Z publication https://n2t.org/ark:/85065/d7ms3wvr https://doi.org/10.1029/2019GL083703 Pu, B. author Ginoux, P. author Kapnick, S. B. author Yang, Xiaosong author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation