MT4DCS: Data Science and Climate Services

Module code: MT4DCS

Module provider: Meteorology; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Level 4 (Undergraduate Masters)

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

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE MT2NSM OR TAKE MT2SWC (Compulsory)

Co-requisite module(s):

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: 16 September 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.  Students will learn how raw climate data can be “converted” 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.

Specific aims include:

• To deepen students’ 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.
• To introduce students to the scientific principles and techniques associated with climate service provision.
• 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.
• 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.
• To enable the student to independently evaluate, tailor, and communicate climate information to address specific end-user needs.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Describe the strengths and limitations of major climate data sources (observations, reanalyses, forecasts and projections) and how climate impact models can “convert” this data into decision-relevant information, including demonstrating awareness of the uncertainties inherent in this process.
2. Describe the main concepts in statistical science to critically analyse climate data and draw statistical inferences.
3. Be able to construct, apply and interpret statistical analyses of geospatial data and “climate impact models” using Python.
4. 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.

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.

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

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: https://www.reading.ac.uk/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 “Statistical Analysis” computer lab report feedback (3 pages)
• Interim “Climate Services” computer lab report feedback (3 pages)

Reassessment

Additional costs