澳门六合彩开奖记录
MTMDCS: Data Science and Climate Services
Module code: MTMDCS
Module provider: Meteorology; School of Mathematical, Physical and Computational Sciences
Credits: 20
Level: 7
When you'll be taught: Semester 2
Module convenor: Professor David Brayshaw, email: d.j.brayshaw@reading.ac.uk
Module co-convenor: Dr Reinhard Schiemann, email: r.k.schiemann@reading.ac.uk
Additional teaching staff 1: Dr Ben Harvey, email: b.j.harvey@reading.ac.uk
Pre-requisite module(s):
Co-requisite module(s): IN THE SAME YEAR AS TAKING THIS MODULE YOU MUST TAKE MTMDFS OR TAKE CSMAD (Compulsory)
Pre-requisite or Co-requisite module(s):
Module(s) excluded:
Placement information: NA
Academic year: 2024/5
Available to visiting students: Yes
Talis reading list: Yes
Last updated: 20 December 2024
Overview
Module aims and purpose
This module aims to introduce how quantitative climate datasets can be used to estimate climate impacts and model their consequences for human- and environmental- systems (such as food, water, insurance & energy) on timescales from weeks to decades ahead.聽 Students will learn how raw climate data can be 鈥渃onverted鈥 into information relevant to decision-making applying appropriate statistical methods for analysis and interpretation, and understanding the limitations of the methods and data used. Students will also develop their ability to communicate this information effectively to both expert and non-expert audiences.聽
Specific aims include:聽
- To deepen students鈥 understanding of basic statistical concepts and reasoning relevant to environmental science, providing experience in the proper use of statistical methods for the analysis of weather and climate data.聽
- To introduce students to the scientific principles and techniques associated with climate service provision.聽
- To introduce how weather and climate information is used in key end-user sectors. Examples may include disaster risk reduction, energy, insurance, water and transport.聽
- To enable students to put into practice the application of real-world global model data to climate service problems through the use of statistics and computer programming.聽
- To enable the student to independently evaluate, tailor, and communicate climate information to address specific end-user needs.聽
Module learning outcomes
By the end of the module, it is expected that students will be able to:
- Describe the strengths and limitations of major climate data sources (observations, reanalyses, forecasts and projections) and how climate impact models can 鈥渃onvert鈥 this data into decision-relevant information, including demonstrating awareness of the uncertainties inherent in this process.
- Describe the main concepts in statistical science to critically analyse climate data and draw statistical inferences.
- Construct, apply and interpret statistical analyses of geospatial data and 鈥渃limate impact models鈥 using Python.
- Communicate climate information appropriately, efficiently, and effectively.
Module content
Taught (lecture) content will include:
- Statistical foundations:
- Exploratory data analysis
- Probability distributions
- Statistical inference and hypothesis testing
- Linear regression
- Multivariate statistics and principal component analysis
- Time series analysis
- Climate service fundamentals:
- What is a 'climate service'?
- The nature of weather and climate predictability
- Introducing climate impact modelling, model
- Understanding errors and uncertainty
- Example: Climate risk assessment using historic data
- Example: Climate projections
- Example: Subseasonal-to-seasonal forecasting
These will be supported by hands-on experience of climate data analysis (computer labs):
- Applying statistics foundations: Modelling rainfall, the statistics of climate model agreement, a statistical seasonal forecast model
- Identifying regional climate impacts in complex environmental data
- A practical climate-service project, creating an end-user relevant application driven by weather and climate data, including communication of results to a non-expert audience
Additionally, a series of specific industry/sector case studies will be presented by invited guest speakers (representing up to four distinct sectors and industries, e.g., energy, insurance, transport, water, and disaster risk reduction).
Structure
Teaching and learning methods
Lectures (technical and scientific material); seminars (sector-specific case studies by external speakers); computer-based analysis using the Python programming language.聽
Study hours
At least 48 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.
聽Scheduled teaching and learning activities | 聽Semester 1 | 聽Semester 2 | 听厂耻尘尘别谤 |
---|---|---|---|
Lectures | 24 | ||
Seminars | 8 | ||
Tutorials | |||
Project Supervision | |||
Demonstrations | |||
Practical classes and workshops | 16 | ||
Supervised time in studio / workshop | |||
Scheduled revision sessions | |||
Feedback meetings with staff | |||
Fieldwork | |||
External visits | |||
Work-based learning | |||
聽Self-scheduled teaching and learning activities | 聽Semester 1 | 聽Semester 2 | 听厂耻尘尘别谤 |
---|---|---|---|
Directed viewing of video materials/screencasts | |||
Participation in discussion boards/other discussions | |||
Feedback meetings with staff | |||
Other | |||
Other (details) | |||
聽Placement and study abroad | 聽Semester 1 | 聽Semester 2 | 听厂耻尘尘别谤 |
---|---|---|---|
Placement | |||
Study abroad | |||
聽Independent study hours | 聽Semester 1 | 聽Semester 2 | 听厂耻尘尘别谤 |
---|---|---|---|
Independent study hours | 152 |
Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.
Semester 1 The hours in this column may include hours during the Christmas holiday period.
Semester 2 The hours in this column may include hours during the Easter holiday period.
Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.
Assessment
Requirements for a pass
Students need to achieve an overall module mark of 50% to pass this module.
Summative assessment
Type of assessment | Detail of assessment | % contribution towards module mark | Size of assessment | Submission date | Additional information |
---|---|---|---|---|---|
Set exercise | Statistical analysis assignment | 40 | 8 pages | Semester 2, Teaching Week 6 | Submission dates are estimates, subject to confirmation once module materials are developed |
Written coursework assignment | Climate service assignment | 60 | 8 pages | Semester 2, Teaching Week 12 | Submission dates are estimates, subject to confirmation once module materials are developed |
Penalties for late submission of summative assessment
The Support Centres will apply the following penalties for work submitted late:
Assessments with numerical marks
- where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
- the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
- where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
- where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
Assessments marked Pass/Fail
- where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
- where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.
The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.
Formative assessment
Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.
- Interim 鈥淪tatistical Analysis鈥 computer lab report feedback (3 pages)聽
- Interim 鈥淐limate Services鈥 computer lab report feedback (3 pages)聽
Reassessment
Type of reassessment | Detail of reassessment | % contribution towards module mark | Size of reassessment | Submission date | Additional information |
---|---|---|---|---|---|
Set exercise | Statistical analysis assignment | 40 | 8 pages | Dissertation Week 12 | |
Written coursework assignment | Climate service assignment | 60 | 8 pages | Dissertation Week 12 |
Additional costs
Item | Additional information | Cost |
---|---|---|
Computers and devices with a particular specification | ||
Required textbooks | ||
Specialist equipment or materials | ||
Specialist clothing, footwear, or headgear | ||
Printing and binding | ||
Travel, accommodation, and subsistence |
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.