Welcome to CEVE 543!!

Lecture

2023-08-21

Introductions

Today

  1. Introductions

  2. Climate Hazards

  3. What makes environmental data unique?

  4. Course Organziaton

  5. Should you take this class?

About me

  • Dr. James Doss-Gollin
  • Assistant professor in CEVE
  • Interested in bridging Earth science, data science, and decision science to improve climate risk management and long-term infrastructure planning
  • Hometown: New Haven, CT (❤️ for Houston, NYC, and Luque, Paraguay)
  • Doss-Gollin lab

James Doss-Gollin

TA

Yuchen Lu
  • Yuchen Lu
  • Third year Ph.D. student in CEVE
  • Currently researching statistical methods to estimate the probability of extreme precipitation
  • Hometown: Wuhan, China (via Pittsburgh)

Your turn!

  1. Your name
  2. Your field and program of study
  3. Your hometown(s)

What is one thing you hope to learn this semester?

Take a moment to think, write it down, and then we’ll share.

Climate Hazards

Today

  1. Introductions

  2. Climate Hazards

  3. What makes environmental data unique?

  4. Course Organziaton

  5. Should you take this class?

Floods in Paraguay, 2015

Figure 1: Municipalidad de Asunción

TX Cold Snap, 2021

Figure 2: Go Nakamura for Getty Images

What other climate hazards do you know about?

Take a moment to think, write it down, and then we’ll share.

How do we manage climate risks?

  • Reduce emissions to prevent future climate change (“mitigation”)
  • Real-time monitoring and forecasting
  • Building codes and design standards
  • Insurance
  • and much more!

Bayesian Decision Theory

Expected reward \(R\) (equivalently utility, loss, etc.) for taking some decision \(a \in \mathcal{A}\): \[ \mathbb{E}(R(a)) = \int_{\mathcal{S}} R(a, {\bf{s}}) p({\bf{s}}) d{\bf{s}} \] Crucial insight: \[ \mathbb{E}(R(a)) \neq R(a, \mathbb{E}(\bf{s})) \]

Implications

  • We often care about extremes
  • Uncertainty (especially of extremes) matters
  • What makes a “good” estimates of \(p(\bf{s})\)?
    • Physically accurate / realistic
    • High spatial and temporal resolution to quantify impacts on people and infrastructure
    • Large ensemble sizes to quantify uncertainty
    • Multiple scenarios (of “deep” uncertainties)

What makes environmental data unique?

Today

  1. Introductions

  2. Climate Hazards

  3. What makes environmental data unique?

  4. Course Organziaton

  5. Should you take this class?

Fat tails

Bonnafous et al. (2017)

Quasi-periodic oscillations

Doss-Gollin et al. (2019)

Nonstationarity

Fagnant et al. (2020)

Vary on multiple temporal scales

Doss-Gollin et al. (2019)

Spatial structure

Farnham et al. (2018)

Emphasis on extremes

Doss-Gollin & Keller (2023)

Deep Uncertainty

Walker et al. (2013)

Course Organziaton

Today

  1. Introductions

  2. Climate Hazards

  3. What makes environmental data unique?

  4. Course Organziaton

  5. Should you take this class?

Syllabus

Syllabus

Lectures

  • Tests will cover material from lectures and labs
    • Slides will be posted ahead of time on course website (see instructions for printing to PDF)
  • Occasional readings (assigned ahead of time on Canvas)
  • I am not a mind reader! Ask questions.

Labs (10%)

  • Build your hands-on computational skills
  • Most weeks, generally Friday
  • Apply conecpts from lectures to simple problems
  • Graded on a 3 point scale
  • Due one week after the in-class lab; solutions will be posted and discussed so no late submissions (turn in what you have)

Tests (40%)

  • Material from lecture, assigned readings, and labs
  • Always a review session

Projects (40%)

  • Apply concepts from class and lab to a more complex and open-ended problem
  • Each module (except intro) centers on a project

Three rainfall-focused projects planned:

  1. Downscaling
  2. Frequency analysis
  3. Weather typing

Participation (10%)

Some ways to participate include:

  • Attending every class
  • Asking questions in class
  • Answering questions on Canvas
  • Coming to office hours

We will co-grade your participation for every module

Should you take this class?

Today

  1. Introductions

  2. Climate Hazards

  3. What makes environmental data unique?

  4. Course Organziaton

  5. Should you take this class?

Job opportunities

Growing climate analytics opportunities in:

  • Insurance
  • Finance
  • Agriculture
  • Engineering

And more! These require an understanding of climate, probability, statistics, coding, and communication.

Pre-requistes: Linear Algebra

You need basic matrix notation and multiplication, but not much more. Let \[ A = \left[ \begin{matrix} a & b \\ c & d \end{matrix} \right], \quad B = \left[ \begin{matrix} e & f \\ g & h \end{matrix} \right], \quad x = \left[ \begin{matrix} k \\ \ell \end{matrix} \right], \quad \]

You should be able to (with note-checking as needed!) figure out:

  • \(A_{2,1}\)
  • \(A + B\)
  • \(AB\)
  • \(A x\)
  • \(x^T x\)
  • \(x x^T\)

Pre-requisites: Probability and Statistics

You should have a course in applied statistics. You should be able to:

  • Compute summary statistics of a sample
  • Define joint, marginal, and conditional distributions
  • Understand probability density functions, quantiles, and cumulative distribution functions
  • Explain a few probability distributions and where they are appropriate
  • Perform and interpret linear regressions

Pre-requisites: Coding

We will use the Julia programming language. I think you’ll find it easy and fun to learn!

  • No experience in Julia is expected
  • Some prior experience coding (R, Python, Matlab, C, etc.) is suggested
  • If you have no prior coding experience, you will need to put in extra effort to learn the basics

Questions?

  • Wednesday: “What drives uncertain climate hazard?”
  • Friday: “Lab 01: Setting up Julia, GitHub, and Quarto”

References

Bonnafous, L., Lall, U., & Siegel, J. (2017). A water risk index for portfolio exposure to climatic extremes: Conceptualization and an application to the mining industry. Hydrology and Earth System Sciences, 21(4), 2075–2106. https://doi.org/f96k67
Doss-Gollin, J., & Keller, K. (2023). A subjective Bayesian framework for synthesizing deep uncertainties in climate risk management. Earth’s Future, 11(1). https://doi.org/10.1029/2022EF003044
Doss-Gollin, J., Farnham, D. J., Steinschneider, S., & Lall, U. (2019). Robust adaptation to multiscale climate variability. Earth’s Future, 7(7), 734–747. https://doi.org/10.1029/2019ef001154
Fagnant, C., Gori, A., Sebastian, A., Bedient, P. B., & Ensor, K. B. (2020). Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas. Natural Hazards, 104(2), 1597–1621. https://doi.org/10.1007/s11069-020-04235-x
Farnham, D. J., Doss-Gollin, J., & Lall, U. (2018). Regional extreme precipitation events: Robust inference from credibly simulated GCM variables. Water Resources Research, 54(6). https://doi.org/10.1002/2017wr021318
Walker, W. E., Lempert, R. J., & Kwakkel, J. H. (2013). Deep Uncertainty. In S. I. Gass & M. C. Fu (Eds.), Encyclopedia of Operations Research and Management Science (pp. 395–402). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4419-1153-7_1140