Lecture

2023-08-21

Today

Introductions

Climate Hazards

What makes environmental data unique?

Course Organziaton

Should you take this class?

- 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

- 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 name
- Your field and program of study
- Your hometown(s)

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

Today

Introductions

Climate Hazards

What makes environmental data unique?

Course Organziaton

Should you take this class?

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

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

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:** \[
\mathbb{E}(R(a)) \neq R(a, \mathbb{E}(\bf{s}))
\]

- 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)

Today

Introductions

Climate Hazards

What makes environmental data unique?

Course Organziaton

Should you take this class?

Today

Introductions

Climate Hazards

What makes environmental data unique?

Course Organziaton

Should you take this class?

- 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.

- 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)

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

- 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:

- Downscaling
- Frequency analysis
- Weather typing

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

Today

Introductions

Climate Hazards

What makes environmental data unique?

Course Organziaton

Should you take this class?

Growing climate analytics opportunities in:

- Insurance
- Finance
- Agriculture
- Engineering

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

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:

- \(A_{2,1}\)
- \(A + B\)
- \(AB\)

- \(A x\)
- \(x^T x\)
- \(x x^T\)

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

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

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

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