An Empirical Model for Rainfall Maximums Conditioned to Tropospheric Water Vapor Over the Eastern Pacific Ocean

Sheila Serrano-Vincenti; Thomas Condom; Lenin Campozano; Jessica Guamán; Marcos Villacís

doi:10.3389/feart.2020.00198

An Empirical Model for Rainfall Maximums Conditioned to Tropospheric Water Vapor Over the Eastern Pacific Ocean

Sheila Serrano-Vincenti, Thomas Condom, Lenin Campozano, Jessica Guamán, Marcos Villacís

Environmental Sciences Research Group (GRICAM)

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

One of the most difficult weather variables to predict is rain, particularly intense rain. The main limitation is the complexity of the fluid dynamic equations used by predictive models with increasing uncertainties over time, especially in the description of brief, local, and high intensity precipitation events. Although computational, instrumental and theoretical improvements have been developed for models, it is still a challenge to estimate high intensity rainfall events, especially in terms of determining the maximum rainfall rates and the location of the event. Within this context, this research presents a statistical and relationship analysis of rainfall intensity rates, total precipitable water (TPW), and sea surface temperature (SST) over the ocean. An empirical model to estimate the maximum rainfall rates conditioned to TPW values is developed. The performance of the maximum rainfall rate model is spatially evaluated for a case study. High-resolution TRMM 2A12 satellite data with a resolution of 5.1 × 5.1 km and 1.67 s was used from January 2009 to December 2012, over the Eastern Pacific Niño area in the tropical Pacific Ocean (0–5°S; 90–81°W), comprising 326,092 rain pixels. After applying the model selection methodology, i.e., the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), an empirical exponential model between the maximum possible rain rates conditioned to TPW was found with R² = 0.96, indicating that the amount of TPW determines the maximum amount of rain that the atmosphere can precipitate exponentially. Spatially, this model unequivocally locates the rain event; however, the rainfall intensity is underestimated in the convective nucleus of the cloud. Thus, these results provide an additional constraint for maximum rain intensity values that should be adopted in dynamic models, improving the quantification of heavy rainfall event intensities and the correct location of these events.

Original language	English
Article number	198
Journal	Frontiers in Earth Science
Volume	8
DOIs	https://doi.org/10.3389/feart.2020.00198
State	Published - 9 Jul 2020

Bibliographical note

Funding Information:
This research was carried out using the research computing facilities and advisory services offered by the Scientific Computing Laboratory at the Research Center on Mathematical Modeling: MODEMAT, Escuela Polit?cnica Nacional, Quito, Ecuador. The satellite data was provided by the Goddard Earth Sciences Data and Information Services Center of NASA Goddard Space Flight Center. Additionally, the authors are thankful for the technical support provided by Leandro Robaina. And appreciate the comments made by the reviewers which have greatly enriched this research. Funding. Financial support was given by Universidad Polit?cnica Salesiana doctoral scholarships. The authors also thank the IRD and EPN for the LMI GREATICE grant.

Publisher Copyright:
© Copyright © 2020 Serrano-Vincenti, Condom, Campozano, Guamán and Villacís.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

high resolution precipitation models
integrated water vapor
intense rain
model selection
TRMM 2A12

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10.3389/feart.2020.00198

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title = "An Empirical Model for Rainfall Maximums Conditioned to Tropospheric Water Vapor Over the Eastern Pacific Ocean",

abstract = "One of the most difficult weather variables to predict is rain, particularly intense rain. The main limitation is the complexity of the fluid dynamic equations used by predictive models with increasing uncertainties over time, especially in the description of brief, local, and high intensity precipitation events. Although computational, instrumental and theoretical improvements have been developed for models, it is still a challenge to estimate high intensity rainfall events, especially in terms of determining the maximum rainfall rates and the location of the event. Within this context, this research presents a statistical and relationship analysis of rainfall intensity rates, total precipitable water (TPW), and sea surface temperature (SST) over the ocean. An empirical model to estimate the maximum rainfall rates conditioned to TPW values is developed. The performance of the maximum rainfall rate model is spatially evaluated for a case study. High-resolution TRMM 2A12 satellite data with a resolution of 5.1 × 5.1 km and 1.67 s was used from January 2009 to December 2012, over the Eastern Pacific Ni{\~n}o area in the tropical Pacific Ocean (0–5°S; 90–81°W), comprising 326,092 rain pixels. After applying the model selection methodology, i.e., the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), an empirical exponential model between the maximum possible rain rates conditioned to TPW was found with R2 = 0.96, indicating that the amount of TPW determines the maximum amount of rain that the atmosphere can precipitate exponentially. Spatially, this model unequivocally locates the rain event; however, the rainfall intensity is underestimated in the convective nucleus of the cloud. Thus, these results provide an additional constraint for maximum rain intensity values that should be adopted in dynamic models, improving the quantification of heavy rainfall event intensities and the correct location of these events.",

keywords = "high resolution precipitation models, integrated water vapor, intense rain, model selection, TRMM 2A12",

author = "Sheila Serrano-Vincenti and Thomas Condom and Lenin Campozano and Jessica Guam{\'a}n and Marcos Villac{\'i}s",

note = "Funding Information: This research was carried out using the research computing facilities and advisory services offered by the Scientific Computing Laboratory at the Research Center on Mathematical Modeling: MODEMAT, Escuela Polit?cnica Nacional, Quito, Ecuador. The satellite data was provided by the Goddard Earth Sciences Data and Information Services Center of NASA Goddard Space Flight Center. Additionally, the authors are thankful for the technical support provided by Leandro Robaina. And appreciate the comments made by the reviewers which have greatly enriched this research. Funding. Financial support was given by Universidad Polit?cnica Salesiana doctoral scholarships. The authors also thank the IRD and EPN for the LMI GREATICE grant. Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2020 Serrano-Vincenti, Condom, Campozano, Guam{\'a}n and Villac{\'i}s. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

month = jul,

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doi = "10.3389/feart.2020.00198",

language = "English",

volume = "8",

journal = "Frontiers in Earth Science",

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AU - Serrano-Vincenti, Sheila

AU - Condom, Thomas

AU - Campozano, Lenin

AU - Guamán, Jessica

AU - Villacís, Marcos

N1 - Funding Information: This research was carried out using the research computing facilities and advisory services offered by the Scientific Computing Laboratory at the Research Center on Mathematical Modeling: MODEMAT, Escuela Polit?cnica Nacional, Quito, Ecuador. The satellite data was provided by the Goddard Earth Sciences Data and Information Services Center of NASA Goddard Space Flight Center. Additionally, the authors are thankful for the technical support provided by Leandro Robaina. And appreciate the comments made by the reviewers which have greatly enriched this research. Funding. Financial support was given by Universidad Polit?cnica Salesiana doctoral scholarships. The authors also thank the IRD and EPN for the LMI GREATICE grant. Publisher Copyright: © Copyright © 2020 Serrano-Vincenti, Condom, Campozano, Guamán and Villacís. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/7/9

Y1 - 2020/7/9

N2 - One of the most difficult weather variables to predict is rain, particularly intense rain. The main limitation is the complexity of the fluid dynamic equations used by predictive models with increasing uncertainties over time, especially in the description of brief, local, and high intensity precipitation events. Although computational, instrumental and theoretical improvements have been developed for models, it is still a challenge to estimate high intensity rainfall events, especially in terms of determining the maximum rainfall rates and the location of the event. Within this context, this research presents a statistical and relationship analysis of rainfall intensity rates, total precipitable water (TPW), and sea surface temperature (SST) over the ocean. An empirical model to estimate the maximum rainfall rates conditioned to TPW values is developed. The performance of the maximum rainfall rate model is spatially evaluated for a case study. High-resolution TRMM 2A12 satellite data with a resolution of 5.1 × 5.1 km and 1.67 s was used from January 2009 to December 2012, over the Eastern Pacific Niño area in the tropical Pacific Ocean (0–5°S; 90–81°W), comprising 326,092 rain pixels. After applying the model selection methodology, i.e., the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), an empirical exponential model between the maximum possible rain rates conditioned to TPW was found with R2 = 0.96, indicating that the amount of TPW determines the maximum amount of rain that the atmosphere can precipitate exponentially. Spatially, this model unequivocally locates the rain event; however, the rainfall intensity is underestimated in the convective nucleus of the cloud. Thus, these results provide an additional constraint for maximum rain intensity values that should be adopted in dynamic models, improving the quantification of heavy rainfall event intensities and the correct location of these events.

AB - One of the most difficult weather variables to predict is rain, particularly intense rain. The main limitation is the complexity of the fluid dynamic equations used by predictive models with increasing uncertainties over time, especially in the description of brief, local, and high intensity precipitation events. Although computational, instrumental and theoretical improvements have been developed for models, it is still a challenge to estimate high intensity rainfall events, especially in terms of determining the maximum rainfall rates and the location of the event. Within this context, this research presents a statistical and relationship analysis of rainfall intensity rates, total precipitable water (TPW), and sea surface temperature (SST) over the ocean. An empirical model to estimate the maximum rainfall rates conditioned to TPW values is developed. The performance of the maximum rainfall rate model is spatially evaluated for a case study. High-resolution TRMM 2A12 satellite data with a resolution of 5.1 × 5.1 km and 1.67 s was used from January 2009 to December 2012, over the Eastern Pacific Niño area in the tropical Pacific Ocean (0–5°S; 90–81°W), comprising 326,092 rain pixels. After applying the model selection methodology, i.e., the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), an empirical exponential model between the maximum possible rain rates conditioned to TPW was found with R2 = 0.96, indicating that the amount of TPW determines the maximum amount of rain that the atmosphere can precipitate exponentially. Spatially, this model unequivocally locates the rain event; however, the rainfall intensity is underestimated in the convective nucleus of the cloud. Thus, these results provide an additional constraint for maximum rain intensity values that should be adopted in dynamic models, improving the quantification of heavy rainfall event intensities and the correct location of these events.

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KW - integrated water vapor

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An Empirical Model for Rainfall Maximums Conditioned to Tropospheric Water Vapor Over the Eastern Pacific Ocean

Abstract

Bibliographical note

Keywords

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