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 »
- Authors:
-
- 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); National Aeronautics 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
- Additional Journal Information:
- 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. https://doi.org/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. https://doi.org/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},
number = 1,
volume = 124,
place = {United States},
year = {Sun Feb 15 00:00:00 EST 2015},
month = {Sun Feb 15 00:00:00 EST 2015}
}
Web of Science
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