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Accuracy and Variability Analysis of the Global Forecast System's Near-Surface Temperatures throughout the Contiguous United States.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Accuracy and Variability Analysis of the Global Forecast System's Near-Surface Temperatures throughout the Contiguous United States./
作者:	Forbis, Dakota.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	95 p.
附註:	Source: Masters Abstracts International, Volume: 82-12.
Contained By:	Masters Abstracts International82-12.
標題:	Meteorology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28491014
ISBN:	9798738649905

Accuracy and Variability Analysis of the Global Forecast System's Near-Surface Temperatures throughout the Contiguous United States.
Forbis, Dakota.

Accuracy and Variability Analysis of the Global Forecast System's Near-Surface Temperatures throughout the Contiguous United States. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 95 p.

Source: Masters Abstracts International, Volume: 82-12.

Thesis (M.A.)--Western Illinois University, 2021.

This item must not be sold to any third party vendors.

The problem of how effective the Global Forecast System numerical weather prediction model was at predicting two-meter temperatures throughout the contiguous United States compared to model analysis fields in 2017 is investigated within this study. Accuracy of medium and long-term forecasted two-meter temperatures were expected to increase as forecast lead time decreased, while accuracy was also expected to decrease as forecast variability increased. Forecast variability was also predicted to increase as forecast lead time increased, while forecast bias was anticipated to be the greatest in the winter and summer months for the contiguous United States. Python scripts were created to compute error and absolute error, as well as statistics such as mean and standard deviation, from a large quantity of data, while Pearson's Product-Moment Correlation Coefficient was also calculated to determine the direction and strength of the relationship between model accuracy and forecast lead time as well as for the relationship between model accuracy and model variability. This study shows that a strong, positive correlation exists between mean absolute error of two-meter temperatures and forecast lead time throughout the study area, while a variable, positive correlation exists between mean absolute error and the standard deviation of temperature across lead times. This study also shows that forecast variability of two-meter temperatures increased slightly for the study area as forecast lead time increased, while the largest biases in forecasted two-meter temperature occurred throughout the study area around the spring and autumn months in 2017.

ISBN: 9798738649905Subjects--Topical Terms:

542822
Meteorology.
Subjects--Index Terms:

ArcGIS

Accuracy and Variability Analysis of the Global Forecast System's Near-Surface Temperatures throughout the Contiguous United States.
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300 $a 95 p.
500 $a Source: Masters Abstracts International, Volume: 82-12.
500 $a Advisor: Finch, Redina.
502 $a Thesis (M.A.)--Western Illinois University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The problem of how effective the Global Forecast System numerical weather prediction model was at predicting two-meter temperatures throughout the contiguous United States compared to model analysis fields in 2017 is investigated within this study. Accuracy of medium and long-term forecasted two-meter temperatures were expected to increase as forecast lead time decreased, while accuracy was also expected to decrease as forecast variability increased. Forecast variability was also predicted to increase as forecast lead time increased, while forecast bias was anticipated to be the greatest in the winter and summer months for the contiguous United States. Python scripts were created to compute error and absolute error, as well as statistics such as mean and standard deviation, from a large quantity of data, while Pearson's Product-Moment Correlation Coefficient was also calculated to determine the direction and strength of the relationship between model accuracy and forecast lead time as well as for the relationship between model accuracy and model variability. This study shows that a strong, positive correlation exists between mean absolute error of two-meter temperatures and forecast lead time throughout the study area, while a variable, positive correlation exists between mean absolute error and the standard deviation of temperature across lead times. This study also shows that forecast variability of two-meter temperatures increased slightly for the study area as forecast lead time increased, while the largest biases in forecasted two-meter temperature occurred throughout the study area around the spring and autumn months in 2017.
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653 $a Python
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