Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence

Israel Pineda; Elisa J. Piispa; Scott L. Williams; Maria Solis-Aulestia

doi:10.1109/IGARSS52108.2023.10282877

Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence

Israel Pineda^*
, Elisa J. Piispa
, Scott L. Williams
, Maria Solis-Aulestia

^*Corresponding author for this work

Matemáticas

University of Iceland
New Mexico State University
Canegrowers Innisfail District

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3842-3845
Number of pages	4
ISBN (Electronic)	9798350320107
DOIs	https://doi.org/10.1109/IGARSS52108.2023.10282877
State	Published - 2023
Event	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2023-July

Conference

Conference	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/Territory	United States
City	Pasadena
Period	16/07/23 → 21/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

ETo classification
Evapotranspiration
Self-Organizing Maps
Weather Research and Forecast model
neural network

Access to Document

10.1109/IGARSS52108.2023.10282877

Cite this

Pineda, I., Piispa, E. J., Williams, S. L., & Solis-Aulestia, M. (2023). Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 3842-3845). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS52108.2023.10282877

Pineda, Israel ; Piispa, Elisa J. ; Williams, Scott L. et al. / Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3842-3845 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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abstract = "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 K{\"o}ppen 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.",

keywords = "ETo classification, Evapotranspiration, Self-Organizing Maps, Weather Research and Forecast model, neural network",

author = "Israel Pineda and Piispa, \{Elisa J.\} and Williams, \{Scott L.\} and Maria Solis-Aulestia",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",

year = "2023",

doi = "10.1109/IGARSS52108.2023.10282877",

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series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Pineda, I, Piispa, EJ, Williams, SL & Solis-Aulestia, M 2023, Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence. in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2023-July, Institute of Electrical and Electronics Engineers Inc., pp. 3842-3845, 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, United States, 16/07/23. https://doi.org/10.1109/IGARSS52108.2023.10282877

Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence. / Pineda, Israel; Piispa, Elisa J.; Williams, Scott L. et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3842-3845 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

PY - 2023

Y1 - 2023

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 - Contribución a la conferencia

AN - SCOPUS:85178380807

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 -

Pineda I, Piispa EJ, Williams SL, Solis-Aulestia M. Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3842-3845. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS52108.2023.10282877

Meso-scale Standard Evapotranspiration 'Climate' Classification Derived from Numerical Weather Prediction Models and Artificial Intelligence

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this