東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Statistical Models for Extreme Event...

Zorzetto, Enrico.

FindBook

Google Book

Amazon

博客來

Statistical Models for Extreme Events in Atmospheric Processes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Models for Extreme Events in Atmospheric Processes./
作者:	Zorzetto, Enrico.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	314 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Contained By:	Dissertations Abstracts International82-03B.
標題:	Hydrologic sciences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997585
ISBN:	9798672146713

Statistical Models for Extreme Events in Atmospheric Processes.
Zorzetto, Enrico.

Statistical Models for Extreme Events in Atmospheric Processes. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 314 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: B.

Thesis (Ph.D.)--Duke University, 2020.

This item must not be sold to any third party vendors.

Extreme events - that is, intense events so rare to be poorly represented in historical observational records - play a fundamental role in atmospheric processes, and can have far reaching consequences ranging from impacts on society, economy, the environment, as well as on the global water and energy budgets. However, characterizing the statistical properties of such events is a challenging task, as (i) by definition extremes are poorly sampled, and thus studying them often requires extrapolation beyond the range of available observations, and (ii) extremes are often the result of nonlinear and intermittent processes, which determine significant difficulties both in predicting them and in studying their frequency of occurrence beyond the range of observations. This dissertation focuses on new statistical methods specifically aimed at characterizing extremes events in rainfall and boundary layer turbulence, including contributions along three main lines of inquiry:1) Developing extreme value models able to reduce estimation uncertainty in the case of short rainfall time series. To this end, a non-asymptotic approach is developed which, deviating from traditional extreme value theory, models the entire distribution of rainfall magnitudes and frequency of occurrence. Using compound distributions and the structure of latent-level Bayesian models, this framework accounts for the effects of low-frequency variability of rainfall statistics on the tail decay of their probability distribution. 2) Characterizing the frequency of extreme values from remotely-sensed rainfall estimates. This objective is approached by developing a downscaling technique that allows comparing rainfall statistics across different spatial averaging scales, and by constructing a model of the error so as to permit their validation over poorly gauged locations. The framework developed here now allows for the production of large-scale estimates of the frequency of extreme rainfall based on satellite-derived rainfall datasets and their validation even in data-scarce regions.3) Investigating the dynamics of scalar quantities transported in the atmospheric boundary layer, with a focus on fluxes of sensible heat and methane. In the case of sensible heat, I studied to what extent the extreme values properties of temperature fluctuations retain information on the turbulence generation mechanism. In the case of methane, I focused instead on an inverse problem: given the observed statistical properties of methane concentration fluctuations, is it possible to infer the spatial intermittency of its source at the ground? In both cases, I found that statistical properties of the scalar, including its extreme value statistics, can be used to improve characterization of the turbulent flow and of its boundary conditions.

ISBN: 9798672146713Subjects--Topical Terms:

3168407
Hydrologic sciences.
Subjects--Index Terms:

Boundary Layer Turbulence

Statistical Models for Extreme Events in Atmospheric Processes.
LDR:03902nmm a2200349 4500 001 2277331
005 20210521101659.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798672146713
035 $a (MiAaPQ)AAI27997585
035 $a AAI27997585
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zorzetto, Enrico. $3 3555646
245 1 0 $a Statistical Models for Extreme Events in Atmospheric Processes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 314 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
500 $a Advisor: Marani, Marco.
502 $a Thesis (Ph.D.)--Duke University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Extreme events - that is, intense events so rare to be poorly represented in historical observational records - play a fundamental role in atmospheric processes, and can have far reaching consequences ranging from impacts on society, economy, the environment, as well as on the global water and energy budgets. However, characterizing the statistical properties of such events is a challenging task, as (i) by definition extremes are poorly sampled, and thus studying them often requires extrapolation beyond the range of available observations, and (ii) extremes are often the result of nonlinear and intermittent processes, which determine significant difficulties both in predicting them and in studying their frequency of occurrence beyond the range of observations. This dissertation focuses on new statistical methods specifically aimed at characterizing extremes events in rainfall and boundary layer turbulence, including contributions along three main lines of inquiry:1) Developing extreme value models able to reduce estimation uncertainty in the case of short rainfall time series. To this end, a non-asymptotic approach is developed which, deviating from traditional extreme value theory, models the entire distribution of rainfall magnitudes and frequency of occurrence. Using compound distributions and the structure of latent-level Bayesian models, this framework accounts for the effects of low-frequency variability of rainfall statistics on the tail decay of their probability distribution. 2) Characterizing the frequency of extreme values from remotely-sensed rainfall estimates. This objective is approached by developing a downscaling technique that allows comparing rainfall statistics across different spatial averaging scales, and by constructing a model of the error so as to permit their validation over poorly gauged locations. The framework developed here now allows for the production of large-scale estimates of the frequency of extreme rainfall based on satellite-derived rainfall datasets and their validation even in data-scarce regions.3) Investigating the dynamics of scalar quantities transported in the atmospheric boundary layer, with a focus on fluxes of sensible heat and methane. In the case of sensible heat, I studied to what extent the extreme values properties of temperature fluctuations retain information on the turbulence generation mechanism. In the case of methane, I focused instead on an inverse problem: given the observed statistical properties of methane concentration fluctuations, is it possible to infer the spatial intermittency of its source at the ground? In both cases, I found that statistical properties of the scalar, including its extreme value statistics, can be used to improve characterization of the turbulent flow and of its boundary conditions.
590 $a School code: 0066.
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Statistics. $3 517247
653 $a Boundary Layer Turbulence
653 $a Extreme Rainfall
653 $a Extreme Values
690 $a 0388
690 $a 0725
690 $a 0463
710 2 $a Duke University. $b Earth and Ocean Sciences. $3 3194646
773 0 $t Dissertations Abstracts International $g 82-03B.
790 $a 0066
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997585