Statistical guidance on seasonal forecast of Korean dust days over South Korea in the springtime

Sohn, Keon Tae

doi:10.1007/s00376-012-2112-x

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Lang [] built a statistical prediction model through linear regression for spring dust storm frequency in north China using antecedent temperature, precipitation, and large‐scale climatic indices. Sohn [] developed a ternary logistic regression model to predict springtime dust storm frequency over South Korea, using the previous season's temperature, rainfall, snowfall, wind speed, and nine large‐scale climatic indices during the previous season as predictors. These studies illustrate the potential for seasonal dust prediction using antecedent climatic variables.…”

Section: Introductionmentioning

confidence: 99%

Climatic controls on the interannual to decadal variability in Saudi Arabian dust activity: Toward the development of a seasonal dust prediction model

et al. 2015

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The observed climatic controls on springtime and summertime Saudi Arabian dust activities during 1975-2012 are analyzed, leading to development of a seasonal dust prediction model. According to empirical orthogonal function analysis, dust storm frequency exhibits a dominantly homogeneous pattern across Saudi Arabia, with distinct interannual and decadal variability. The previously identified positive trend in remotely sensed aerosol optical depth since 2000 is shown to be a segment of the decadal oscillation in dust activity, according to long-duration station record. Regression and correlation analyses reveal that the interannual variability in Saudi Arabian dust storm frequency is regulated by springtime rainfall across the Arabian Peninsula and summertime Shamal wind intensity. The key drivers of Saudi Arabian dust storm variability are identified. Winter-to-spring La Niña enhances subsequent spring dust activity by decreasing rainfall across the country's primary dust source region, the Rub' al Khali Desert. A relatively cool tropical Indian Ocean favors frequent summer dust storms by producing an anomalously anticyclonic circulation over the central Arabian Peninsula, which enhances the Shamal wind. Decadal variability in Saudi Arabian dust storm frequency is associated with North African rainfall and Sahel vegetation, which regulate African dust emissions and transport to Saudi Arabia. Mediterranean sea surface temperatures (SSTs) also regulate decadal dust variability, likely through their influence on Sahel rainfall and Shamal intensity. Using antecedent-accumulated rainfall over the Arabian Peninsula and North Africa, and Mediterranean SSTs, as low-frequency predictors, and tropical eastern Pacific and tropical Indian Ocean SSTs as high-frequency predictors, Saudi Arabia's seasonal dust activity is well predicted.

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Section: Introductionmentioning

confidence: 99%

Climatic controls on the interannual to decadal variability in Saudi Arabian dust activity: Toward the development of a seasonal dust prediction model

et al. 2015

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“…There are a few studies of seasonal dust prediction in Asia through statistical methods (Gao et al, 2010;Sohn, 2013). For instance, Gao et al (2009) examined the potential of using previous annual mean precipitation to predict subsequent springtime dust storm frequency in the Inner Mongolia region as precipitation can influence soil moisture and vegetation growth.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Prediction Potential for Springtime Dustiness in the United States

Ginoux

Kapnick

et al. 2019

Geophysical Research Letters

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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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Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions

Ebrahimi-Khusfi

Taghizadeh–Mehrjardi

Nafarzadegan

2020

Environ Sci Pollut Res

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Statistical guidance on seasonal forecast of Korean dust days over South Korea in the springtime

Cited by 6 publications

References 15 publications

Climatic controls on the interannual to decadal variability in Saudi Arabian dust activity: Toward the development of a seasonal dust prediction model

Climatic controls on the interannual to decadal variability in Saudi Arabian dust activity: Toward the development of a seasonal dust prediction model

Seasonal Prediction Potential for Springtime Dustiness in the United States

Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions

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