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Title: Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps

Abstract

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. Lastly,more » this result may be due to a manifestation of seasonally dependent biases in NARR.« less

Authors:
 [1];  [1];  [1]
  1. Univ. of North Dakota, Grand Forks, ND (United States). Dept. of Atmospheric Sciences
Publication Date:
Research Org.:
Univ. of North Dakota, Grand Forks, ND (United States)
Sponsoring Org.:
USDOE Office of Energy Research (ER). Office of Health and Environmental Research; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Aeronautic and Space Administration (NASA); National Science Foundation (NSF)
OSTI Identifier:
1343438
Grant/Contract Number:  
SC0008468; NNX11AM15A; EPS-814442
Resource Type:
Accepted Manuscript
Journal Name:
Theoretical and Applied Climatology (Austria)
Additional Journal Information:
Journal Name: Theoretical and Applied Climatology (Austria); Journal Volume: 124; Journal Issue: 1; Journal ID: ISSN 0177-798X
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Kennedy, Aaron D., Dong, Xiquan, and Xi, Baike. Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps. United States: N. p., 2015. Web. doi:10.1007/s00704-015-1384-3.
Kennedy, Aaron D., Dong, Xiquan, & Xi, Baike. Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps. United States. doi:10.1007/s00704-015-1384-3.
Kennedy, Aaron D., Dong, Xiquan, and Xi, Baike. Sun . "Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps". United States. doi:10.1007/s00704-015-1384-3. https://www.osti.gov/servlets/purl/1343438.
@article{osti_1343438,
title = {Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps},
author = {Kennedy, Aaron D. and Dong, Xiquan and Xi, Baike},
abstractNote = {Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. Lastly, this result may be due to a manifestation of seasonally dependent biases in NARR.},
doi = {10.1007/s00704-015-1384-3},
journal = {Theoretical and Applied Climatology (Austria)},
number = 1,
volume = 124,
place = {United States},
year = {2015},
month = {2}
}

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