(Note this article references my previous post entitled “Gustav and New Orleans: A Political Risk Assessment for the Republican Decision Maker” dated Saturday, 08-30-08).

Revision of Likeliness Estimates

According to the National Weather Service, National Hurricane Center, Gustav is veering to the right (albeit slightly) of yesterday’s projected storm track centerline (see below).  Moreover, the National Weather Service has deemed it sufficiently likely that Gustav will significantly impact New Orleans that it has issued a Hurricane Warning to the city and points all along the Louisiana and Mississippi coastlines (and a little bit of Alabama too).  Accordingly, I revise my probability estimate for the event that Gustav will afflict New Orleans in an appreciable way (in terms of storm surge) as follows:

• Pr(A1) = 0.75 (increase from yesterday’s 0.50)
• Pr(A2) = 0.25 (corresponding decrease from yesterday)

Note that I did not use Bayes’ Theorem to update my probability estimates, but rather did a complete reassessment of the probability.  Also note I continue to entertain non-zero chances that the storm will not afflict New Orleans since the storm track envelope admits some degree of likeliness to the storm veering to the west.

Now let’s turn out attention to the time Gustav makes landfall.  According to the right-most figure above, the storm center will probably make landfall sometime between 7AM CDT and 7PM CDT on Monday, 08-31-08.  According to the maximum wind speed probability table (NWS advisory 28, dated 10AM CDT, 08-31-08), the chances that Gustav will be a category 3 storm or greater is [37%, 81%].  Since Katrina was a category 3 storm, lets assume that if Gustav is classified as a category 3 storm or greater at landfall AND the storm is one that is slated to yield an appreciable storm surge affecting the New Orleans Hurricane Protection System, then Gustav will be as bad or worse than Katrina.  Otherwise, it will not be.  With this information, I can revise my probability estimates as follows:

• Pr(B1|A1) = [37%, 81%] (probability that Gustav will be as bad or worse than Katrina)
• Pr(B2|A1) = [19%, 63%] (probability that Gustav will not be as bad or worse than Katrina)

Since the other events C1, C2, D1, and D2 have to do with the integrity of the NOHPS (which is assumed to have not changed in the last day) and the consequences to New Orleans in the event of NOHPS failure (also assumed to be unchanged), the revised probability for the scenarios E1 through E7 is as follows:

Revised Consequence Assessment

The press recently reported to Republican decisions that, though most likely pursued for unselfish and humanitarian reasons (as they should), can also be viewed as strategies to decrease the potential for unfavorable outcomes and increase the potential for favorable outcomes.  These are as follows:

• As stated by White House Press Secretary Dana Perino (cited by CNN and elsewhere), President Bush and Vice President Cheney will not attend the Republican National Convention in St. Paul Minnesota in person; instead, Bush may address the convention via satellite.
• As reported by CNN (link), RNC organizers are examining options to change the schedule of the convention, to include delaying opening ceremonies, turning the convention into a massive telethon, or postponing the convention altogether.

In the context of this study, these measures may very well decrease the severity of the resulting political fall-out in the event of a NOHPS failure, but perhaps not by much because after all, the NOHPS failed despite having three years to rebuild.  On the flip side, if Gustav does afflict New Orleans in an appreciable way (again with respect to storm surge along the NOHPS), and the NOHPS successfully protects the city, then the Republicans will surely emphasize the improved ability of the federal government (under the Bush administration) to prepare for and protect against natural perils.  In this case, more of the same might not be viewed as being all that bad with respect to domestic preparedness (and perhaps by extension, homeland security).  But now, in the event that Gustav does not afflict New Orleans, the political fallout is slightly in favor of the Republicans since they are taking preventive measures to protect the Gulf coast across the board, and you can’t deny that doing a good job at this by putting the people first (which I argue they will do) will garner some brownie points from the electorate.

Given the above, I revise my political outcome assessment as follows:

• E1: unchanged at  “–”
• E2: unchanged at “-”
• E3: unchanged at “++”
• E4: unchanged at “–”
• E5: unchanged at “-”
• E6: improved to “++”
• E7: improved to “+” (may change back to “N” if the Democrats can somehow display on a national stage that they are doing something too)

Given the revised likeliness and political outcome assessments above, the revised risk profile over the space of political outcomes is as follows:

Revised Results

Based on the revised risk profile above, we can draw the following revised conclusions:

• The most likely major scenario is that the Republicans will benefit from Gustav [86%, 93%]
• The must less likely scenario is that the Republican campaign will suffer from the effects of Gustave [7%, 14%]
• It seems at this point that Republicans will feel something given that the probability of a neutral or “no effect” outcome is near zero at this stage

From the Democrat perspective, one strategy to mitigate the Democrat risk is to do something that will gain them visibility on the national stage in relation to Gustav preparedness.  A visible substantial effort may serve to transfer some of the Republican benefits resulting from scenario E7 back to the neutral position (I doubt they can do much to pull any support in their favor for this scenario).  But even still, given the probability assessment being what it is, the ordering of the major scenarios will remain unchanged.

CAVEAT: Given the probabilistic nature of my assessment, the only verifiable portion of my assessment is whether or not the neutral outcome is realized.  But this requires me to stand by my assessment in light of new information (which I am sure to come across).  I suspect that I will find myself updating this analysis again sometime in the next day or so.

Note: as stated in my previous post, I assign LOW CONFIDENCE to this analysis given that I am not an expert in hydrodynamics, nor am I an expert in politics and political sentiment.

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Recently I received a message from Professor Stephen Marrin (of Mercyhurst College) via the IAFIE (International Association for Intelligence Education) listserv talking about a U.S. presidential election prediction model developed by Professor Allan J. Lichtman of American University.  What was cited by Professor Marrin was the 08-25-08 Washington Post article entitled “In the Quake Model, Rumblings Favor Obama.”  Following the chain, the Washington Post article was really just a summary of Professor Lichtman’s peer-reviewed article entitled “The keys to the white house: An index forecast for 2008″ published in the International Journal of Forecasting, Vol. 24, No. 2, pp. 301-309 (doi: 10.1016/j.ijforecast.2008.02.004).  The abstract for this article is as follows:

The Keys to the White House are an index-based prediction system that retrospectively accounts for the popular-vote winners of every American presidential election from 1860 to 1980, and prospectively forecast the winners of every presidential election from 1984 through 2004 well ahead of time. The Keys give specificity to the theory that presidential election results turn primarily on the performance of the party controlling the White House. The Keys include no polling data and consider a much wider range of performance indicators than economic concerns. Already, the Keys are lining up for 2008, showing how changes in the structure of politics will produce a Democratic victory, in a dramatic reversal from 2004. The Keys also suggest that candidates need not follow the empty scripted campaigns of the recent past, but should instead be liberated to offer forthright discussions of the issues and ideas that will shape America’s future.

While I haven’t yet read this paper in great detail, I am curious as to how it works, and what amount of analytic confidence can be afforded to the model’s output.  What I am particularly curious about is the form of the output – is it an absolute answer, or does the model provide a probability distribution over the space of party candidates?  And what about third parties (or are they just in the noise)?

But what I can say is that, in principle, prediction technologies such as the “keys to the White House” model can be used by any individual or group of individuals (e.g., foreign countries) with a vested interest in the outcome of this or any other US presidential election to assess their environmental (e.g., political) risks, thereby providing a frame with which to evaluate their future decision options.  But do you think the model is robust enough to include the effects of supposed “game changers” (e.g., Ms. Palin’s recent entry into the GOP ticket)?  Keep in mind that if the “keys to the White House” model is, in fact, a credible model, it could also be used, at least to some degree, as a tool to help the otherwise losing party reshape their campaign toward winning, whether by pursuing a target PR campaign, changing policy positions, or whatever else that can influence the underlying “keys” (or rather, indicators) that enable prediction.  So perhaps the choice of Ms. Palin to run as the GOP vice president was targeting one of these keys…  After all, according to Professor Lichtman,

“Not only elections, but also election forecasts have consequences for politics.  The models that we use to predict and understand presidential elections shape the conduct of campaigns, the relationships between candidates and the American people, and ultimately the policies of government.”

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The following is an excerpt from an email I received awhile back from a colleague passionate about getting the word out about risks of failure of the U.S. Bulk Power Grid due to solar events.

The U.S. bulk power grid can fail due to a continental wide, common mode failure of many of the large transformers (>= 500KV) that support the grid backbone. This failure is caused by very large geomagnetically induced currents (GIC) that saturate the transformers. These large GICs (hundreds of amps) are induced in the transmission wires due the large rate of change of the magnetic field at the Earth’s surface due to disturbance of the Earth’s magnetosphere. This magnetospheric disturbance is caused when a large Coronal Mass Ejection (CME) interacts with the magnetosphe during a large solar storm.

If the definition of risk is the “probability of occurrence multiplied by the consequences” then the risk of the loss of the bulk power grid is very large.

Some data for the consequences of an electrical blackout can be found in the documentation associated with the blackout in August 2003 (https://reports.energy.gov). The total accumulated cost of this event has been estimated as high as $70 billion. But this was not a long term outage as power was restored within a week and only the North East was affected. The real costs of a continental wide Long Term Outage (LTO) will be a significant fraction of the current Gross National Product $14 trillion!

The probability of occurrence of a damaging solar storm can be estimated by looking at the historical occurrence of these rare types of storms.

In the last 67 years there have been 8 major storms with a magnetic Ap index > 250. Storms of this size are associated with potentially damaging geomagnetically induced currents (GIC) that could cause failures of large 500 kV large transformers. The storms occurred: 3/23/1940, 9/18/1941, 7/15/1959, 3/31/1960 11/12/1960, 10/4/1960, 3/13/1989, and 10/29/2003. These large solar storms appear to be random events and not tied to the eleven year solar cycle. Given the frequency of these solar storms it can be estimated (using the Poisson distribution) that there is a probability of ~5% of a storm occurring. This probability makes the risk very large.

The storms before 1989 were prior to the introduction the 500 kV grid in 1964. The reason why the grid was not damaged before 1989 that the impedance of the grid’s transmission wires was too high to give rise to damaging GICs but the 500 kV transmission wires impedance is very low (~.03 Ohms/mile). The first U.S. transformer damaged occurred during the 1989 march solar storm, was the 500 kV step-up transformer at the Salem New Jersey nuclear plant. Also in the October 2003 Halloween solar storm, fourteen 400 kV transformers in South Africa were damaged and had to be replaced. It should be noted that there are about 216 potentially susceptible U.S. transformers supporting the 500/765 kV backbone grid and most of these were part of the grid system in 1989 and therefore were subjected to the stresses of the 1989 storm. This implies during the next large storm there may be many multiple failures.

As there has been only one documented large transformer failure due to GICs in the history of the U.S. electrical grid in the last one hundred years, the utilities do not have spares as there is no apparent need for spares. Since it takes about two years to order and deliver these large transformers, the grid may be down for an extended time. Theses transformers are no longer manufactured in the USA and the world’s current manufacturing capacity is only 100 per year.

Conclusion: the probability of a grid failure is not low and the risk is large and could be catastrophic. Without the electrical grid the Internet would also go down, Also nobody at the nation’s leadership level appears to be concerned.

I have attached an authoritative reference to this subject. It is the prepared Testimony of John G. Kappenman, Metatech Corp. entitled, “The Vulnerability of the US Electric Power Grid to Space Weather and the Role of Space Weather Forecasting” before the U.S. House Subcommittee on Environment, Technology, and Standards And the Subcommittee Hearing on “What is Space Weather and Who Should Forecast It?” on October 30, 2003.

Perhaps you can help to raise awareness of the problem.

I am curious to hear your feedback on this issue.

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