Climate Risk Analysis : Surface Temperature Projections
Professor: Vishal Dixit
UID: CM01
Tracking Clouds in Climate Simulation
Professor: Vishal Dixit
UID: CM02
WRF Simulations of extreme rainfall events in Mumbai
Professor: S. Ravichandran
UID: CM03
CM01
Description: "While the climate change is global, each
industry needs to prepare for the local changes in climate
variables that affect their assets. A good assessment of actual
and potential risk on short and long-time horizon becomes
critical for doing performance assessment and to disclose the
risk posed to assets due to changing climate. This research
project will focus on prediction of temperature trends on short
(2-3 years), intermediate (5-7 years) and long (20 years) time
horizons and create a corresponding spatial map of a risk index
for Mumbai. Most readily available online tools to assess
temperature extremes / projections and associated risk rely
purely on historical records or climate model projections. The
available ground based historical observational data are sparse
(1 station in the radius of 30km) and short in time-history to
resolve changing climate patterns. The climate model projections
are too coarse (a single value for 100km grid resolution). A
good risk assessment depends on realistic estimation of spatial
and time dependent variation in temperature during the day and
its accumulated influence. This project will explore novel
physics driven AI / ML and statistics based methods to overcome
these hurdles."
Number of students: 2
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 8 and above
Prerequisites: An ideal candidate will have basic
understanding of Earth’s surface energy budget that controls
surface temperature and will have a passion and curiosity to
explore climate patterns. Past experience with analyzing data
using Python based statistics / AI+ML tools will be a plus but
not essential.
Duration: 6 months (extension possible if required)
Learning outcome: "Take-aways for interns: 1.
Brainstorming and review of methods for temperature projections
2. Experience with Big Data processing 3. A short publication if
novel results are found"
Instructions for assignment: "Read basics of surface
energy budget (link 1 and 2) and write a short summary (2
paragraphs) about how daily surface temperature is linked to
energy budget. Explorations with link 1 will be a plus (not
necessary). Read paper in link 3 and explain the method used in
your own words (2 paragraphs) "
CM02
Description:"Clouds arise curiosity in the human mind,
with their beauty, their shapes and with a pertinent question:
“will it rain now?” Our understanding of how clouds form and how
they migrate is limited and in fact this limitation ruins our
efforts to estimate the climate sensitivity of the earth’s
climate change accurately! Identifying clouds at a single
time-stamp in a satellite image has become a relatively simple
problem with our current understanding but keeping track of them
in time is a notoriously hard problem. This project will test
cloud tracking algorithm in variety of set-ups starting from
simulated Clouds in a Large-eddy simulation to the real
satellite image. The goal will be to test the algorithm and
(hopefully) discover how clouds migrate on different
spatio-temporal scales. "
Number of students: 1
Year of study: Students entering 2nd year, Students
entering 3rd year, Students entering 4th/5th year
CPI eligibility criteria: 8 or above
Prerequisites: A strong background in data analysis,
python is necessary. Some background in Fluid dynamics will be a
great addition.
Duration: 4-5 months
Learning outcome: 1. Understanding of Cloud processes, 2.
Analyzing Big data with Python
Weekly time commitment:20 hours
General expectations: Passionate students are encouraged
to apply
Instructions for assignment: Read chapter 1 from link 1
and summaries in your own words why clouds are important for
climate change (2 paragraphs) and Read paper from link 2 and
write 2 paragraphs explaining what we learn from Fig. 2 and Fig.
3
CM03
Description:We plan to use the WRF suite to simulate and
understand extreme rainfall events in Mumbai. We will look at
data from past extreme events and look for precursors for
extreme rainfall events. We will then test how well these
precursors predict extreme rainfall using numerical simulations.
Such predictions may then be used by municipal authorities to
prepare for flooding in specific areas. the dynamical (as
opposed to statistical) approach will also tell us _why_ certain
precursors lead to heavy rain in certain areas.
Number of students: 1
Year of study: Students entering 4th/5th year
CPI eligibility criteria: 7+
Prerequisites: Working knowledge of WRF is required.
Duration: 2-3 months
Learning outcome:
Weekly time commitment:20 h
General expectations:
Assignment: "The WRF suite is described here:
https://www.mmm.ucar.edu/models/wrf
The student will need to know how to run the solver on a
computing cluster. "