LABS - DATA, TOOLS AND METHODS FOR EARTH SCIENCES - INTRODUCTION

Anno accademico

2022/2023 Programmi anni precedenti

Titolo corso in inglese

LABS - DATA, TOOLS AND METHODS FOR EARTH SCIENCES - INTRODUCTION

Codice insegnamento

PHD165 (AF:401878 AR:222254)

Lingua di insegnamento

Inglese

Modalità

In presenza

Crediti formativi universitari

3

Livello laurea

Master di Secondo Livello (DM270)

Settore scientifico disciplinare

GEO/12

Periodo

II Semestre

Anno corso

1

Sede

VENEZIA

Spazio Moodle

Link allo spazio del corso

Inquadramento dell'insegnamento nel percorso del corso di studio

Big data and cloud computing are rapidly evolving topics in Earth Sciences. Understanding the data types and limitations, in addition to the methods and tools, are basic fundamental building blocks for analysis and applications of earth observations data. The aim of this LABS is to make students familiar with state-of-the-art tools and data repositories that can prepare them to address various challenges when handling big data in earth sciences.

Risultati di apprendimento attesi

The course will introduce students to recent tools, methodologies, data repositories and advancements in computing infrastructures applicable in Earth Sciences, with special emphasis on climate impacts and risk assessment. Students will develop an understanding of various observational/model simulated data sources, scope and limitations of usage, and tools to access and process Earth Sciences' data on cloud computing infrastructures.

Prerequisiti

Basic knowledge of R and Python, IPCC/climate change scenarios and impacts assessments.

Contenuti

Overview of climate data variables, repositories, cloud computing and earth system grid infrastructures, such as: Earth System Grid Framework -ESGF, Copernicus Climate Data Store, Microsoft Planetary Computer, Google Earth Engine and Google Colab.

Overview of commonly used data types and software environments used in earth science (e.g. Vector and Raster file types, NetCDF fIle format, NetCDF command line operators, R, Python).

Understanding limitations of observations, reanalysis and model simulated climate data products.

Testi di riferimento

In addition to the material provided in each lecture (which includes slides, data and scripts), additional information on below weblink are useful:

climate.copernicus.eu
copernicus.eu
ecmwf.int
https://colab.research.google.com
https://jupyter.org/

Modalità di verifica dell'apprendimento

During the course, the students will be asked to participate in interactive sessions (coding skills) and graded on their active engagement and a general understanding of programming concepts. These will count towards 100% for the final grade.

Modalità di esame

orale

Metodi didattici

Each lecture will combine a frontal lecture and in-class activities (hands-on sessions using sample data and analysis/scripts prepared in R, Python and cloud computing). Activities will allow students to advance their knowledge of the commonly used methods and tools for analysing earth observations data. Students are expected to bring their laptops in each session.

Altre informazioni

Further details about readings, required data and software installation including practical exercises will be communicated at the beginning of the course and published on Moodle.

Obiettivi Agenda 2030 per lo sviluppo sostenibile

Questo insegnamento tratta argomenti connessi alla macroarea "Cambiamento climatico e energia" e concorre alla realizzazione dei relativi obiettivi ONU dell'Agenda 2030 per lo Sviluppo Sostenibile

Programma definitivo.

Data ultima modifica programma: 09/05/2023