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‎chapters/homework/homework2.ipynb‎

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"name": "#%% md\n"
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}
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},
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"source": [
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"# HW#2: Visualizing Historical Temperature Changes in Singapore\n",
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"\n",
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"```{admonition} Objectives\n",
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":class: tip\n",
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"\n",
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"This homework will provide you a real-world example in terms of:\n",
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"* how to visualize the GEV distribution\n",
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"* how to visualize time series of temperature anomalies and the trend\n",
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"* how to make beautiful, accessible, and understandable data visualizations\n",
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"\n",
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"Happy coding!\n",
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"```"
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]
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},
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{
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"metadata": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"```{admonition} Submission Guide\n",
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"\n",
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"Deadline: **Sunday 11:59 pm, 16th November 2025** \n",
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"(Note: Late submissions will not be accepted). \n",
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"\n",
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"Please upload your solutions to [Canvas](https://canvas.nus.edu.sg/courses/77993/assignments) in a Jupyter Notebook format with the name \"Homework2_StudentID.ipynb\". Make sure to write down your student ID and full name in the cell below. \n",
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"\n",
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"For any questions, feel free to contact Prof. Xiaogang HE ([hexg@nus.edu.sg](mailto:hexg@nus.edu.sg)), Kewei ZHANG ([kewei_zhang@u.nus.edu](mailto:kewei_zhang@u.nus.edu)) or Yifan LU ([yifan_lu@u.nus.edu](mailto:yifan_lu@u.nus.edu)).\n",
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"\n",
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"```"
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]
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{
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"source": [
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"## Fill your student ID and full name below.\n",
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"\n",
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"# Student ID:\n",
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"# Full name:"
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]
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},
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{
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},
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"source": [
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"**Data**:\n",
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"You will need to use the historical (1982-2020) daily mean air temperature data measured at Singapore's Changi station for this homework.\n",
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"You can create a DataFrame using Pandas by reading the file \"../../assets/data/Changi_daily_temperature.csv\"."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b60eb96",
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"source": [
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"## Task 1: Visualize the GEV distribution (30 marks)\n",
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"To visualize the GEV distribution, you can:\n",
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"1. Fit the Generalized Extreme Value (GEV) distribution to annual maximum daily temperature data and estimate the GEV parameters using the **L-Moments method**.\n",
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"2. Plot the probability density function (PDF) curve to represent the distribution directly.\n",
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"3. Calculate the return level for a 10-year event and mark it on the plot.\n",
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"4. Shade the plot with different colors to distinguish areas above and below the calculated return level.\n",
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"5. Ensure that the necessary non-data elements are included, such as title, x/y axis labels, legend, etc. (you can check the [Matplotlib tutorial](https://xiaoganghe.github.io/python-climate-visuals/chapters/data-visuals/matplotlib-basic.html) for details)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f7a646f",
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"metadata": {},
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"source": [
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"```{admonition} Bonus: 10 marks\n",
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":class: tip\n",
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"\n",
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"Illustrate how return levels vary across different return periods (e.g., 2, 5, 10, 20, 50, 100 years) using interactive sliders or dashboards. Resources such as the [Plotly documentation](https://plotly.com/python/sliders/) and [this tutorial video](https://www.youtube.com/watch?v=gs4d0_AKQi8) may be helpful for this task.\n",
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"\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"source": [
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"# Your solutions go here.\n",
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"# Use the + icon in the toolbar to add a cell."
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{
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"cell_type": "markdown",
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"source": [
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"## Task 2: Visualize the trend of temperature anomaly (70 marks)\n",
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"### Q1: Visualize the time series and local trend (slope) of historical temperature anomalies for Singapore (35 marks)\n",
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"- Calculate the annual mean temperature from the daily data. This will result in a dataset of 39 values — one per year. (5 marks)\n",
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"- Calculate the annual temperature anomalies using the first 10-year period as the baseline. (Hint: subtract the mean temperature over 1982 to 1991 from the annual mean temperature for each year.) (5 marks)\n",
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"- Visualize the change in these annual temperature anomalies over time, ensuring that you include essential non-data elements. (10 marks)\n",
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"- Compute a rolling linear regression (10-year moving window) of annual mean temperature anomalies to estimate how the local warming rate evolves through time. (10 marks)\n",
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"- Plot the resulting slope (°C per decade) against the central year. (5 marks)"
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]
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"# Your solutions go here.\n",
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}
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},
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"source": [
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"### Q2: Add climate stripes for Singapore (35 marks)\n",
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"\n",
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"- Reproduce the [climate stripes](https://showyourstripes.info/s/asia/singapore) for Singapore using `Matplotlib`. (20 marks)\n",
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"- Overlay the annual temperature anomaly time series and its local trend (from Q1) on top of your generated climate stripes. (15 marks)\n",
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"\n",
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"Tips: \n",
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"- Refer to [this GitHub repository](https://github.com/josephshea/ClimateStripes/blob/master/ClimateStripes-Canada.ipynb) for guidance on creating climate stripes.\n",
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"- Fine-tune the aesthetics of your chart (e.g., color palette of the diverging colorbar) to ensure it is visually appealing and accessible (colorblind safe)."
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]
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{
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"cell_type": "markdown",
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"source": [
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"```{admonition} Bonus: 10 marks\n",
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":class: tip\n",
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"\n",
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"Create an interactive plot where users can hover over climate stripes and trend lines to view detailed information. For inspiration, you can refer to [this example](https://ourworldindata.org/un-population-2024-revision).\n",
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"\n",
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"```"
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