TY - GEN
T1 - Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence
AU - Pineda, Israel
AU - Piispa, Elisa J.
AU - Williams, Scott L.
AU - Solis-Aulestia, Maria
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023/7/16
Y1 - 2023/7/16
N2 - The Weather, Research and Forecasting (WRF) model and Self-Organizing Maps (SOM) artificial neural network (ANN) have been used to classify regional evapotranspiration (ETo) 'weather'. Here, this concept is expanded to develop a pilot ETo 'climate' classification system in a regional subset of the Andes and the Amazon. ETo 'climate' is defined as the frequency of ETo 'weather' classes passing through a geographical location (i.e., a pixel in the WRF model), allowing the construction of a histogram for each pixel. The histogram is then used as a one-dimensional signal for another SOM classification, offering a regional perspective on how ETo behaves and specifically what a geographic location can expect in terms of ETo variables, similar to the Köppen climate classification system. This research presents the results of several classification comparisons to produce a repeatable pilot system for Eto 'climate' classification and a map that offers the potential to improve irrigation decision-making.
AB - The Weather, Research and Forecasting (WRF) model and Self-Organizing Maps (SOM) artificial neural network (ANN) have been used to classify regional evapotranspiration (ETo) 'weather'. Here, this concept is expanded to develop a pilot ETo 'climate' classification system in a regional subset of the Andes and the Amazon. ETo 'climate' is defined as the frequency of ETo 'weather' classes passing through a geographical location (i.e., a pixel in the WRF model), allowing the construction of a histogram for each pixel. The histogram is then used as a one-dimensional signal for another SOM classification, offering a regional perspective on how ETo behaves and specifically what a geographic location can expect in terms of ETo variables, similar to the Köppen climate classification system. This research presents the results of several classification comparisons to produce a repeatable pilot system for Eto 'climate' classification and a map that offers the potential to improve irrigation decision-making.
KW - ETo classification
KW - Evapotranspiration
KW - Self-Organizing Maps
KW - Weather Research and Forecast model
KW - neural network
UR - https://www.scopus.com/pages/publications/85178380807
U2 - 10.1109/IGARSS52108.2023.10282877
DO - 10.1109/IGARSS52108.2023.10282877
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3842
EP - 3845
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -