Version 1
: Received: 19 July 2023 / Approved: 20 July 2023 / Online: 20 July 2023 (12:56:23 CEST)
How to cite:
Gonzalez, M.A.; Suastegui, A.C. Monthly Precipitation Outlooks for Mexico Using ENSO Indices Approach. Preprints2023, 2023071416. https://doi.org/10.20944/preprints202307.1416.v1
Gonzalez, M.A.; Suastegui, A.C. Monthly Precipitation Outlooks for Mexico Using ENSO Indices Approach. Preprints 2023, 2023071416. https://doi.org/10.20944/preprints202307.1416.v1
Gonzalez, M.A.; Suastegui, A.C. Monthly Precipitation Outlooks for Mexico Using ENSO Indices Approach. Preprints2023, 2023071416. https://doi.org/10.20944/preprints202307.1416.v1
APA Style
Gonzalez, M.A., & Suastegui, A.C. (2023). Monthly Precipitation Outlooks for Mexico Using ENSO Indices Approach. Preprints. https://doi.org/10.20944/preprints202307.1416.v1
Chicago/Turabian Style
Gonzalez, M.A. and Arturo Corrales Suastegui. 2023 "Monthly Precipitation Outlooks for Mexico Using ENSO Indices Approach" Preprints. https://doi.org/10.20944/preprints202307.1416.v1
Abstract
The socioeconomic sectors increasingly rely on prompt and affordable tools to predict climatic conditions. This study uses a decision tree classifier model to identify similar monthly ENSO 3.4 indices from March 2001 to February 2011 through monthly historical ENSO3.4 indices (December 1950 – February 2001). Historical monthly ENSO3.4 indices were used to extract available high-resolution monthly historical precipitation grids (1950-2001) for Mexico and constructed monthly ensembles (March 2001 – February 2011 hindcasts). Formerly, a precipitation categorization of monthly observed and simulated precipitation (2001-2011 hindcasts) was employed. Thereafter, global vector data and spatial map assessments were performed seasonally (winter, spring, summer, and fall). Global KSS and HSS metrics indicated the model approach has skill, especially in the spring and fall. Spatial metrics exposed large areas with hit rates > 0.60 in winter and spring, though in winter and spring (dry seasons), it was due to the precipitation categorization hindcast and observed were within the first classification. Lower hit rates, but still acceptable (> 0.40), were detected in summer and fall (during the rainy season and at the end, respectively) because more precipitation classifications were tested and prone to miss. Spatial Spearman correlations provided specific areas of significant model performances and found that high hit rates during the winter and spring are spurious. Meanwhile, it was also found that during the fall the model approach is reliable in most of Mexico, followed by the spring, and in constraint areas in summer and winter across the country. It also appears that dominant ENSO neutral events followed by balanced ENSO cooling and warming events do not prefer the model skill. Overall, the empirical method shows promise (low-cost human and computational resources if ENSO indices expose certainty in its Lead months to forecast) and has the potential to be used in other developing countries influenced by El Niño phenomena.
Keywords
Monthly hindcasts; ENSO indices; Mexico; decision tree model classifier
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
