Servizi per la didattica
PORTALE DELLA DIDATTICA

Statistical Methods with application to Climate Variability and Change Assessments (didattica di eccellenza)

01VPORW

A.A. 2020/21

Course Language

Italian

Course degree

Doctorate Research in Ingegneria Civile E Ambientale - Torino

Course structure
Teaching Hours
Lezioni 20
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Claps Pierluigi Professore Ordinario ICAR/02 2 0 0 0 2
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
This course presents a comprehensive review of statistical approaches and methods that are essential for the analysis of hydroclimatic data and for evaluating climate variability and change influences but can also be of widespread interest for a general audience. The course emphasizes three main issues: (1) data analysis, (2) statistical methodologies, and (3) application of techniques. The course provides an exhaustive background and in-depth review of exploratory data analysis, statistical (both parametric and nonparametric), and data mining methods to evaluate changes, trends, anomalies, and extremes detection and appropriate treatment. Applications of statistical methods and practical laboratory work with real-world data for climate change and variability assessments will be part of this course.
This course presents a comprehensive review of statistical approaches and methods that are essential for the analysis of hydroclimatic data and for evaluating climate variability and change influences but can also be of widespread interest for a general audience. The course emphasizes three main issues: (1) data analysis, (2) statistical methodologies, and (3) application of techniques. The course provides an exhaustive background and in-depth review of exploratory data analysis, statistical (both parametric and nonparametric), and data mining methods to evaluate changes, trends, anomalies, and extremes detection and appropriate treatment. Applications of statistical methods and practical laboratory work with real-world data for climate change and variability assessments will be part of this course.
-
-
Understanding Hydroclimatic Data: Climate Change and Variability Data: Observations and estimates; understanding climate variability and change especially from a data perspective, data scarcity, exploratory data analysis, visual evaluation of time series data, evaluation of spatial data and outlies, handling of data anomalies, summary statistics, univariate and multivariate analysis: basics, smoothing methods and introduction to data mining and assimilation, and time series analysis, development of univariate and multivariate forecasting approaches. Parametric Statistical Methods Introduction; Paired and unpaired tests, assessment of changes in statistical moments, changes in distributions, Data transformations (e.g., Box-Cox transformation and others), Trends, and Changepoint assessments. Detection of homogeneity. Non-Parametric Statistical Methods Introduction to rank-based methods, measures of associations, trends, and changes, changes in data: moments, rank-based methods, assessment of homogeneity, evaluation of persistence, smoothing methods, resampling methods (e.g., bootstrap). Climate Variability Evaluation Introduction to climate variability, manifestations of climate variability, coupled oceanic and atmospheric oscillations, changes in essential climatic variables, assessment of changes in precipitation, temperature, and streamflow, use of parametric and nonparametric methods, resampling methods. Climate Change and Stationarity Assessment Introduction to climate change, downscaling, bias corrections, statistical methods for assessment of climate change, stationarity and its assessment, frequency analysis, hydrologic design, and water resources management under climate change and climate change adaptation.
Understanding Hydroclimatic Data: Climate Change and Variability Data: Observations and estimates; understanding climate variability and change especially from a data perspective, data scarcity, exploratory data analysis, visual evaluation of time series data, evaluation of spatial data and outlies, handling of data anomalies, summary statistics, univariate and multivariate analysis: basics, smoothing methods and introduction to data mining and assimilation, and time series analysis, development of univariate and multivariate forecasting approaches. Parametric Statistical Methods Introduction; Paired and unpaired tests, assessment of changes in statistical moments, changes in distributions, Data transformations (e.g., Box-Cox transformation and others), Trends, and Changepoint assessments. Detection of homogeneity. Non-Parametric Statistical Methods Introduction to rank-based methods, measures of associations, trends, and changes, changes in data: moments, rank-based methods, assessment of homogeneity, evaluation of persistence, smoothing methods, resampling methods (e.g., bootstrap). Climate Variability Evaluation Introduction to climate variability, manifestations of climate variability, coupled oceanic and atmospheric oscillations, changes in essential climatic variables, assessment of changes in precipitation, temperature, and streamflow, use of parametric and nonparametric methods, resampling methods. Climate Change and Stationarity Assessment Introduction to climate change, downscaling, bias corrections, statistical methods for assessment of climate change, stationarity and its assessment, frequency analysis, hydrologic design, and water resources management under climate change and climate change adaptation.
A distanza in modalitÓ sincrona
On line synchronous mode
Presentazione report scritto
Written report presentation
P.D.2-2 - Luglio
P.D.2-2 - July


© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti