Looking at recent events and trying to understand why is it hard to predict. Weather modeling and election modeling are chosen for the same

1. Why is it hard to Predict?
Ashok
Govindarajan
20-11-2017 Technology sharing series 1

2. Introduction – Speaker Bio
20-11-2017 Technology sharing series 2
• Interested in applied mathematics for algorithm development in wireless
communications
• Areas of interest include signal processing, communication systems and wireless
communication
• Helping ECE/CS students understand core principles in designing and debugging
field-deployable software by providing lectures in colleges
• More details at : https://www.linkedin.com/in/ashok-govindarajan-4001717/
• Reachable at gashok2@gmail.com

3. Motivation for this talk
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• Motivation to do the talk : Felt the need to create knowledge sharing sessions that
are relevant and would interest a large audience
• Based on discussion with a senior college professor who felt that relevancy-based
education is the need of the hour
• Example topics include, Rainfall/Hurricane prediction, Strategy of Reliance Jio data
plans, Strategy of whatsapp, facebook and Twitter
• Today’s talk is about Hurricane Irma that hit Florida and the Caribbean in Sep 2017

4. Contents
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• Why are we still failing to predict course of hurricanes?
• What went wrong in the predictions of US presidential elections?
• What are the areas we need to look into if we need to improve?
• In short – Taking stock of prediction modeling in areas that REALLY matter!

5. Details – what will be looking at today
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• Aim of the talk is to trying to understand the difficulties in prediction modeling
• 3 scenarios considered below
• High Impact : IRMA hurricane flow modeling, US presidential elections
• Damages between USD 50 to 100 billion in case of IRMA
• Vardah cyclone – Dec 2016 – was around 155 kmph,
Irma was around 285 kmph
• International politics and associated impacts, Nov 2016
• Medium Impact : Seasonal rainfall prediction (Mainly farmers)
• Low Impact : Big Boss winner prediction
• Focussing on IRMA, why should we predict accurately?
• People from which areas need to be evacuated and where should relief be
targetted. More the uncertainty in predictions, logistics are harder for
evacuation, volunteering etc
• Evacuation and people safety – In our control to an extent
• Damage to houses close to the coast, Lack of Electricity , water contamination,
internet down time - not in our control

6. How off were we in Hurricane IRMA?
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• During Hurricane Irma, American forecasters missed the path the hurricane would take by
an average of about 194 nautical miles (350 KM) in their five-day forecasts, a difference
more than twice as big as their European counterparts.
• 1Nautical mile = 1.8 kilometer
• In a local context, to give an equivalent representation, weather experts predicted it will hit
Velankanni and it hit Chennai.

7. Map illustration in a local context
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8. Weather Modeling - Facts
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How many weather stations are present around the world?
 11000 static stations
 Balloons, satellite, merchant ships – dynamic stations
 Not enough, also measuring devices are not calibrated regularly
What do they measure?
 Temperature, Air pressure, Wind speed, Humidity, the direction of the wind, rainfall
What are the models available?
 Mesoscale Models, Dynamic models and statistical models
What is the computing power that we have?
 The largest operational centres will soon be surpassing a petaflop (quadrillion, 10^15,
floating point operations per second) of peak performance.
Do we have Skilled resources – weather meteorologists?
Dr. Steve Lyons, Jim Cantore
Given all the above details, are we accurate today?
 No
What are we accurate about?
 Long range forecasts (beyond a week) are usually less accurate, because there are
many atmospheric factors that can come into play beyond that time. Dynamic models are most
accurate for three- to five-day forecasts
Reasons for inaccuracy in IRMA modeling
Bad weather models, lack of computing power, govt agencies working in silos
(bureaucracy, red-tapism), Sensor calibration
On a funny note As Warren Buffett likes to say, forecasts tell you little about the future and a lot
about the forecaster.

9. What went wrong in US presidential elections?
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Prediction:
As voters head to the polls, two of the most celebrated and successful election forecasters, Sam
Wang of the Princeton Election Consortium (PEC), and Nate Silver of FiveThirtyEight disagree
“bigly.” The former predicts the likelihood of a Clinton win at over 99 percent while the latter put
the chance of a Clinton win in the mid to high 60s all of yesterday, rising to the low 70s today.
Most other models, including those of The New York Times Upshot and betting sites, rate Clinton’s
chances somewhere in the middle, at about 85 percent.
By what margin did Trump win?
In key states by two to six percentage points
What went wrong?
This was a systemic undetected polling error — a kind of invisible “dark matter” of polling — that
underestimated support for Trump in key states by two to six percentage points.
More details at :
https://www.forbes.com/sites/startswithabang/2016/11/09/the-science-of-error-how-polling-botched-the-2016-
election/#1f88f0c37959

10. Conclusion
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Technology sharing series 10
Prediction Modeling for Hurricanes – Potential ways to improve
• Area to improvement is in the modeling world – viewing weather as a machine
to viewing weather as a life form. Evolve models from life sciences.
• Chaos theory – Small changes cause huge disturbances. Approximating initial
conditions during numerical computations leads to this.
For example : Standard sea level pressure is considered to be 1013.25 millibars.
Using 1013.25 to 1013.25765 could mean being off upto 160 kms!
Prediction Modeling for Hurricanes – Potential ways to improve
• Anonymity + Likert scale for optimum results
• Probabilistic election forecasts should be dispensed with altogether and replaced
with the seven-point qualitative scale already in wide use (Likert)

11. 20-11-2017 Technology sharing series 11
Thank You