Bias correction of global circulation model outputs using artificial neural networks

Moghim, Sanaz

Title:

Bias correction of global circulation model outputs using artificial neural networks

dc.contributor.advisor	Bras, Rafael L.
dc.contributor.advisor	Georgakakos, Aristidis P.
dc.contributor.advisor	Wang, Jingfeng
dc.contributor.advisor	Hsu, Kuo-lin
dc.contributor.advisor	Deng, Yi
dc.contributor.author	Moghim, Sanaz
dc.contributor.department	Civil and Environmental Engineering
dc.date.accessioned	2016-08-22T12:19:35Z
dc.date.available	2016-08-22T12:19:35Z
dc.date.created	2015-08
dc.date.issued	2015-05-01
dc.date.submitted	August 2015
dc.date.updated	2016-08-22T12:19:35Z
dc.description.abstract	Climate studies and effective environmental management plans require unbiased climate datasets. This study develops a new bias correction approach using a three layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, net longwave and shortwave radiation are used as inputs for the bias correction of temperature. Precipitation at lag zero, one, two, and three, and the standard deviation from 3 by 3 neighbors around the pixel of interest are the inputs into the ANN bias correction of precipitation. The data are provided by the Community Climate System Model (CCSM3). Results show that the trained ANN can markedly reduce the estimation error and improve the correlation and probabilistic structure of the bias-corrected variables for calibration and validation periods. The ANN outperforms linear regression (LR), which is used for comparison purposes. The ability of the regression models (linear and ANN) to regionalize the study domain is investigated by defining the minimum number of training pixels necessary to achieve a good level of bias correction performance over the entire domain. Results confirm that it is possible to identify regions in terms of physical features such as land cover, topography, and climatology over which the trained models at a few pixels can do well. The new approach saves computational demands, time, and memory usage and it can be used for other climate models efficiently.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/55487
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Bias correction
dc.subject	Regression models
dc.subject	Artificial neural networks
dc.subject	Generalization
dc.subject	Regionalization
dc.subject	Community climate system model
dc.subject	Climate data sets
dc.title	Bias correction of global circulation model outputs using artificial neural networks
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Wang, Jingfeng
local.contributor.advisor	Bras, Rafael L.
local.contributor.advisor	Deng, Yi
local.contributor.advisor	Georgakakos, Aristidis P.
local.contributor.corporatename	School of Civil and Environmental Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	46a4e691-2214-4ba6-bff3-7f1f0bf292d8
relation.isAdvisorOfPublication	64e45b8f-3df1-4e41-84d2-0cd91b9a0d61
relation.isAdvisorOfPublication	70fb52a8-0735-4585-b69b-4ef5b24f4f5a
relation.isAdvisorOfPublication	ec69ec36-b20e-4231-be3e-cfe0dd51ea86
relation.isOrgUnitOfPublication	88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral