The Science and the Art of Measuring Weather Fatalities

I have a confession to make #1…in a series. From time to time, while writing The Leech and researching speech topics on various contemporary issues, I am presented the opportunity to challenge my presumptions and replace them with a more rigorous understanding. Although these essays may not be life altering or change the world, I share them for two possible benefits. First, to convey what I learned on a topic, how it deflated my original hypothesis, and replaced it with a better one. Second, to illustrate a hidden benefit and reality of the painful writing process: you often land in a very different place than where you targeted. That’s growth.

As an engineer, I have a life-long passion for STEM. I also have an inherent skepticism whenever non-STEM entities meddle in scientific matters. That often is a situation where science is demoted, contorted, or appropriated to fit a convenient narrative the journalist, artist, or politician is wishing to propagate. In 2020 we saw this troubling phenomenon of non-scientists playing fast and loose with science in heavy matters ranging from the pandemic to election polling.

When this occurs, the sanctity of scientific integrity is jettisoned, as long as the storyline is bolstered. This abuse of science cuts across party lines and political affiliations. As it becomes more common, tolerance for the ill-advised practice grows. Today, I fear much of our society has become desensitized to journalists and politicians treating the scientific method as nothing more than a convenient, disposable means to an end.

Weather is not immune to this crisis. Contemporary journalism has no problem manipulating science and data to create a desired premise and then broadcasting that premise across the wires. An important example is in the morbid, but potentially life-saving, pursuit of accurate hurricane fatality modeling. Hurricane Maria, which slammed into Puerto Rico in September 2017, serves as a case in point.

Instead of focusing on repairing vital infrastructure and delivering needed medical care, time and resources were being expended on both sides of the political spectrum on debating which model and fatality estimate were accurate. President Trump fired off tweets. Reporters upped the rhetoric. Both sides of non-scientists labeled the other as an attacker of science, with one side looking to push down the fatality count and the other looking to boost it. Both sides became distracted from the task at hand: getting the island functioning again and delivering aid to Puerto Ricans in need.

When Hurricane Maria hit Puerto Rico, the island was heavily damaged, and the electric grid was wiped out. The original death toll from the category-4 storm was reported by the Puerto Rican government to be 64 people using traditional historic measurement methods. Recognizing that even a single fatality is a human tragedy, the initial estimate of 64 deaths from Maria did not register the hurricane as being one of the top-ten deadliest U.S. hurricanes. The extent of the storm’s physical destruction quickly shifted attention from where the storm ranked all-time to the national response on how to restore power and repair the island’s infrastructure.

But that’s also when the commandeering of science began. The media and certain politicians desired a higher fatality estimate. A higher number would help bolster the case for catastrophic climate change and a host of other issues. So, the media, working closely with allies, modified the methodology used to estimate the storm’s fatalities. Under the new approach, deaths that occurred after the storm passed and that could be indirectly attributed to lost power, inability to get medical care, or other infrastructure failings were added to the total. The new estimate rose to somewhere around 3,000 people. That placed Maria as the third deadliest hurricane in U.S. history.

How did the death toll estimate increase by 4,500%? By using new, black-box statistical models and subjective judgments based off imperfect data. Before you knew it, other models and estimates were being constructed and published, each making its case as the best and most accurate. Certain estimates employed statistical models that included island deaths that occurred months after the storm. Other estimates were based on polls of funeral homes on the island. The range of these estimates was quite wide; all were significantly higher than initial estimate of 64 deaths.

After the post-Maria debacle, I wanted to know: What gives when it comes to estimating a storm’s deadliness? How’s it properly done under a rigorous scientific method?

I had the pleasure of spending a few weeks talking storm fatality modeling with an expert in the field. The individual, who is on the faculty at a renowned academic institution and wishes to remain anonymous, provided invaluable insight into this technical profession and craft.

Scientists and engineers in this arena are steeped in statistics, computer modeling, and data analysis. Expertise in meteorology or the health sciences is also common. The complex computer models built to estimate storm fatalities are constantly refined and improved. The history and data set of storms continue to grow, making the models more accurate as time goes on. The endeavor is a never-ending quest to build the better mousetrap to accurately estimate the impact of storms on human life.

My contact/expert had assessed Hurricane Maria and continued to recalibrate the assessment as refinements and improvements were adopted to the model and to the data inputs over the past few years. The expectation was to continue the effort for years to come.

So, where did this model land with its measure of Maria’s fatality number? Well, I am not at liberty to say. But suffice to say it was much higher than 64 and much lower than 3,000. Maria, according to this model and engineer scientist, ranked as one of the ten deadliest hurricanes in U.S. history.

But the estimated number is only a superficial output of the process. More importantly, when one screened the model results to search for correlations, some obvious and crucial conclusions quickly manifested. During the storm, fatalities don’t correlate to age or gender and highly correlate to location: hurricane force winds and flooding rain will indiscriminately kill different demographics, but location makes a huge difference. When the storm is raging, it doesn’t matter much who you are, but it matters terribly where you are. Yet in the days and weeks after the storm, a strong correlation links fatality rate to the youngest and oldest of the island, primarily due to lack of health care. Conclusions like these are crucial to drive informed policy and future investments for the island.

The vital need for access to reliable electricity also stood out in the modeling results: if the island’s grid was buttressed and dependable, storm fatalities would be drastically reduced, particularly for deaths occurring in the weeks immediately after storms. Such an improvement in Puerto Rico’s grid would also bring the long-term benefits of alleviating poverty and extending life expectancy, regardless of hurricane frequency. Truly life-altering and impactful.

The scientists and engineers working in this noble field deserve society’s support. Future lives are at stake. Society should demand the rest of us, including media and politicians, leave our technical wizards unencumbered and follow their findings with policy and action that aids the human condition.

My friend took my original hypothesis, deflated it, and built a superior view. I went in believing one estimate over another, based on where each estimate emanated from. I exited understanding a truth in between. More specifically, a scientific truth not tainted with political science but forged in logic, reason, and math. That kind of truth can save lives.

I thanked my friend and asked him what I could do in return. The answer: leave the science and engineering to us; please leave us alone. Perfect.

Credit Ratings Firms’ Dereliction of Duty

Credit ratings firms have been around for over a hundred years, and through those years the capital markets have created norms and rules that protect the ratings firms’ market and relevancy. These firms hold tremendous influence: a good or poor credit rating assigned to a company, industry, or government can have a profound impact on borrowing costs and decide if the rated entity enjoys access to the capital markets.
Call up any of these firms’ websites and you inevitably come across the much-vaunted values of the firm and the culture they warrant to embrace.

Perusing Moody’s website yields a bounty of virtuous tag lines: clear, credible, accurate, consistent, superior, quality, integrity, and fairly all play prominent in the firm’s website, policies, and communications. Other ratings firms mimic similar themes. These firms indicate to both the issuers and purchasers of debt that an objective rating will be provided, using quantitative metrics and clinical analysis.

The stark reality is quite different.

Credit rating agencies are notorious for picking subjective winners and losers across industries and entities by granting significantly higher or lower credit ratings than what quantifiable metrics should allow. Certain industries singled out as not worthy by these agencies unduly suffer costly, limited access to capital markets while too often fiscally inept governments can do no wrong in the eyes of the ratings firms. This creates hugely different credit ratings and borrowing costs.

A disciplined and stable corporation unlucky enough to be discriminated against by the ratings agencies can hardly access debt markets, and can only do so at elevated cost, due to the taint sprayed on it by the arbitrary credit rating. Meanwhile dysfunctional states and local governments on the verge of insolvency receive ratings as if they are Berkshire Hathaway.

The blatant bias of credit rating agencies in favoring broke states was underscored in 2010 when ratings firms, including Moody’s, decided to “recalibrate” their credit ratings for state and local government municipal bonds. Recalibration was code for a massive upgrade in ratings to tens of thousands of municipal bond issuers spread across state and local governments.

California and Puerto Rico (yes, the now insolvent Puerto Rico) were two of the biggest beneficiaries of the recalibration gift, receiving not just a single notch, but a three-notch upgrade in their ratings from Moody’s. These agencies had the audacity to state the new higher ratings did not reflect any change in the issuer’s credit worthiness. Yet no one was surprised when the arbitrary and one-sided upgrades resulted in significantly lower borrowing costs for the issuers and lower yields for the investors.

Contrast this artificial stacking of the deck in favor of government debt with how Moody’s and other ratings firms treat scores of public company issuers in the manufacturing and energy industries, including natural gas manufacturers.

There are scores of examples to choose from, but let’s select the company I work for: CNX Resources. CNX is the low-cost and high-margin manufacturer of natural gas in the northeast United States. CNX has been consistently free cash flow positive, is not highly leveraged, and is not faced with looming debt maturities in the coming years. Essentially, CNX is a creditor’s dream, offering healthy yields at a low risk of default. Yet Moody’s assigns a credit rating of B1, which under its scale represents “speculative” and “high credit risk.” Yet there are no data or quantifiable facts that support this view; the rating defies Moody’s ballyhooed objective analysis. Similar situations are evident across many companies in the natural gas, pipeline, and extractive industries.

If you placed Chicago’s and CNX’s financial metrics side by side on a sheet of paper without their names and asked a college freshman economics major to tell you which was higher credit risk and which was lower credit risk, the answer would be obvious.

So why is it that Moody’s, armed with thousands of bright employees, over a hundred years of experience, and a supposedly objective quantifiable ratings methodology, determines that Chicago’s debt is more credit worthy than a company like CNX’s debt?

The answer to this disconnect is not obvious – and Moody’s has done nothing to shed light on the answer. One is left to assume that Moody’s is blindly accepting the orthodoxy that fossil fuels are on the way out, soon to be replaced by so-called renewables, and therefore the entire fossil fuel industry is an elevated credit risk. Yet there is no math or science backing the blind faith in the supposed looming evaporation of the need, demand, or market for natural gas.

Reality—and numerous forecasts—dictates that natural gas is going to play the leading global role in electricity generation, transportation, and manufacturing for decades to come. Moody’s delusional zealotry will not change that reality; it will only delay its benefits for millions of humans desiring improved quality of life.

With Moody’s and other ratings firms, it is no longer first and foremost what your financial metrics are that determines your rating, access to capital markets, and cost of capital. Instead, it is what your entity does for a living and whether that endeavor is judged to be favorable or unfavorable through the lens of the ratings firm’s politics, ideology, and beliefs.

If you are lucky enough to be a coastal state or urban government that outspends budget year in and year out facing certain default in the future, Moody’s is more than happy to place its hand on the objective scales to push ratings in your favor, because Moody’s deems you one of the good guys. If you are a subsidy hunting, rent seeking renewable company feeding at the taxpayer trough and that can’t show a sustainable business model without such subsidy, Moody’s gladly assigns you ratings superior to your metrics in the name of saving the planet. Conversely, if you are unfortunate enough to be a government entity that operates within its budget or a company in the noble natural gas industry that thrives without subsidy, you run the risk of discrimination when your credit ratings are arbitrarily penalized. All because the ratings firm doesn’t consider what you do or how you operate as worthy. This is elite arrogance at its worst.

Credit ratings firms have a sorry history of missing financial calamities even as they are unfolding despite clear warning signals (look no further than the 2007 – 2009 global financial crisis). Distractions of playing judge and jury on what’s worthy and just makes matters worse and is a disservice to investors, issuers, and the economy. These ratings firms should immediately drop the subjective and ham-fisted attempts at social engineering and capital redistribution. Instead, they must return to their fiduciary duty of providing objective, transparent, and consistent ratings using quantifiable processes. If these firms refuse to do so, it is time for all market participants to seriously question the purpose and need for ratings firms.

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