MTMDFS: Data Analysis and Forecasting Systems for Weather and Climate

Module code: MTMDFS

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

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 1

Module convenor: Professor Bob Plant, email: r.s.plant@reading.ac.uk

Module co-convenor: Dr Peter Inness, email: p.m.inness@reading.ac.uk

Pre-requisite module(s):

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: 21 May 2024

Overview

Module aims and purpose

This module introduces students to the use of scientific computing for performing statistical data analyses suitable for common meteorological applications. A key application is in operational weather forecasting, and students will learn about the end-to-end process through which operational forecasts are produced.  

´¡¾±³¾²õ:Ìý

The module aims to familiarise students with different methods of numerical and statistical weather and climate forecasting. It also aims to provide students with programming skills in the python language, up to a level at which they can implement their own analyses and carry out independent dissertation research. 

Module learning outcomes

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

• Describe the formulation and design of a modern operational Numerical Weather Prediction (NWP) system and its implementation within a large forecasting organisation. 
• Describe the various uncertainties associated with NWP-generated forecasts on a range of different timescales and how NWP systems are designed to address those uncertainties. 
• Describe some statistical approaches suitable for meteorological data analysis and be able to select an appropriate analysis method for a range of applications 
• Carry out such statistical analyses by writing flexible and reuseable codes in the python programming language 

Module content

• The basic formulation of numerical models in terms of a set of dynamical equations, parametrized sub-gridscale physical processes and a model domain with appropriate resolution 
• The other elements of an operational forecasting system, including the input and assimilation of observations, generation of model output fields and the post-processing of model output to provide useful information for the production of weather forecasts 
• The different applications for which numerical models are used, together with consideration of how these applications affect the design of the forecasting system. Examples of the systems used by the UK Met Office will be given, together with some comparison with systems used at the European Centre for Medium-range Weather Forecasts where appropriate 
• An introduction to the various types of observational data used in numerical models, together with some consideration of how these observations introduce uncertainty into weather prediction 
• An introduction to the use and interpretation of ensemble forecasts  
• Introduction to fundamental concepts in statistics and probability: e.g. statistical distributions, Bayes theorem 
• Linear and multiple regression 
• Correlations, including the analysis of auto-correlation 
• Parameter estimation and hypothesis testing 
• The evaluation of forecast model performance using skill scores 
• Introduction to fundamental elements of programming: e.g. variable types, assign statements, arrays 
• Performing calculations using loops and conditional statements 
• Writing functions and using them effectively 
• Reading large datasets from files in NetCDF format and more advanced manipulation of data 
• Key elements of good practice in writing and designing programs to tackle meteorological applications  

Structure

Teaching and learning methods

Lectures will cover operational forecasting systems and methods of statistical analysis. The former will include structured discussions of operational model output. 

Computing laboratory sessions will introduce some typical meteorological data types and techniques, and students will perform analyses using the python language. Students will also be taught to run the ECMWF Open-IFS forecasting system and make statistical analyses of the output. 

Scheduled feedback sessions will provide students opportunities to discuss their progress on the assessed project. 

Study hours

At least 50 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: /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.

Two programming exercises, with feedback provided on both accuracy and good style 

Reassessment

Additional costs