Predicting Weather with Computers

Lesson Overview:

This 45-minute lesson is designed for Year 11 students as part of the GCSE Computer Science curriculum, focusing on how computers are used in weather prediction. The lesson will cover key computational concepts, including data collection, algorithms, and modelling, while exploring real-world applications of technology in meteorology.

Curriculum Focus:
This lesson aligns with the UK GCSE Computer Science standards for Key Stage 4, specifically:

• CS1.1 Algorithms: Understanding and applying computational thinking techniques such as decomposition and abstraction.
• CS2.5 Impacts of Digital Technology: Exploring how computers are used to solve real-world problems and their implications.
• CS3.2 Data Representation and Analysis: Understanding how data is collected, processed, and visualised.

The session aims to promote computational thinking, data literacy, and cross-curricular links with geography and science.

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Learning Objectives:

By the end of the lesson, students will:

1. Understand how meteorological data is collected and utilised.
2. Comprehend the role of algorithms and simulations in weather forecasting.
3. Analyse the advantages and limitations of computer-based weather models.
4. Experience hands-on problem-solving related to meteorological data through coding or logic puzzles.

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Lesson Structure:

0-5 Minutes: Introduction

1. Settling and Starter Activity

   • Begin with a "weather trivia" quick-fire quiz to immediately engage the students (e.g., “What is the UK’s average annual rainfall?”).
   • Brief class discussion on why predicting the weather in the UK is particularly challenging (e.g., maritime climate).

2. Hook

   • Elicit curiosity by sharing an exciting fact: "Did you know that the UK Met Office uses one of the world’s fastest supercomputers, capable of 14,000 trillion calculations per second, to predict weather?”

   • Show a short, impactful video clip or animated visualisation (e.g., how weather data is turned into a forecast).

Teacher Prompt: “How do you think computers can turn raw data into a prediction for tomorrow's weather?”

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5-15 Minutes: The Science of Weather Prediction

Teacher-Led Explanation

1. How Data is Collected

   • Discuss the inputs for weather prediction: temperature, humidity, pressure, wind patterns.
   • Explain sources of data: satellites, weather stations, aircraft, maritime buoys, and radar systems.

   Key Question for Engagement: “If every weather station in the UK collected one data point per second, how much data would we collect in 24 hours?”

2. Processing Data with Computers

   • Simplify how supercomputers process the data:
     • Decomposition: Breaking weather data into smaller patterns.
     • Abstraction: Ignoring irrelevant details (e.g. an individual raindrop).
     • Using algorithms to simulate the atmosphere.

3. The Role of Models

   • Explain Numerical Weather Prediction (NWP) models that simulate future states of the atmosphere.
   • Highlight UK-specific challenges like rapid changes due to proximity to the Atlantic Ocean.

Teacher Tip: Use simple graphics on the board to visualise the relationships between data, algorithms, and forecasting.

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15-30 Minutes: Student Activity

Hands-On Learning: Predicting with Algorithms

Option A: Algorithm Design (low tech)

• Provide each student group with simplified datasets (e.g., temperature and pressure patterns for two days).

• Task each group with designing a basic algorithm on paper to predict the next day’s weather.

• Groups should think about how to weigh certain factors (e.g., rapid drop in pressure may indicate a storm).

• Teacher Pivotal Question: “How accurate do you think your algorithm would be, and why?”

Option B: Micro:bit Coding Challenge (interactive tech)

• Students use BBC Micro:bits (or an online Python editor) to write basic commands that compare pressure readings and predict simple weather changes (e.g., falling = bad weather, rising = good weather).
• Offer pre-prepared code snippets they can tweak to make it student-friendly.

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30-40 Minutes: Reflection and Discussion

Class Discussion

• Summarise findings from the activity and discuss potential improvements to their algorithms or code.
• Reflect on how complex real-world data requires powerful technology to provide accurate predictions.

Ethical Debate (Stretch Activity)

• Discuss the impacts of inaccurate forecasts. For example:
  • Overestimating storms can cause unnecessary panic.
  • Underestimating bad weather can result in harm.
• Connect to the broader societal impacts of technology in decision-making.

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40-45 Minutes: Plenary and Homework

Plenary

• Recap key points:

  • How is data collected for weather forecasts?
  • Why do we rely on computers and algorithms?
  • What are the limitations of computer predictions?

• Use a ‘Traffic Light’ system on exit slips to assess understanding:

  • Green: Fully understand the use of computers in weather prediction.
  • Amber: Understand most of it, but still unsure about algorithms.
  • Red: Need more clarity about data collection or modelling.

Homework Activity

• Assign students to research a famous weather prediction failure (such as the “Great Storm of 1987” in the UK). Ask for a one-paragraph reflection on what went wrong and any lessons for meteorologists in the age of computers.

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Resources Needed:

1. Pre-made weather trivia questions and data sheets for group activities.
2. BBC Micro:bits or access to Python editors for coding exercises.
3. Visual aids (e.g., algorithms flowcharts, UK Met Office models, or capabilities of supercomputers).
4. Exit slips for the plenary.

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Assessment:

• Informal assessment through class discussion and participation in group activities.
• Exit slips to gauge understanding.
• Homework submission for further insight into individual comprehension levels.

Differentiation Strategy:
Ensure accessibility by offering coding scaffolds, pairing mixed-ability students, and supporting visual learners with diagrams. Stretch high-achieving students by challenging them to think about global weather prediction challenges.

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This lesson plan provides an engaging and interactive way to teach about the intersection of technology and real-world problem-solving, ensuring relevance to the GCSE curriculum and beyond.