In forensic meteorology, we are often asked whether a given weather event was “foreseeable” – in other words, whether those impacted by or involved in the event should have or could have seen it coming.
Foreseeability generally has three components when considering weather events – climatology (what is “normal”), forecast (what is expected) and communication (whether those making decisions have access to crucial information).
In many ways, climatological expectation is about timeline. Over a long enough timeline, even extreme weather events can be expected to occur.
That may sound counterintuitive, so let’s take a familiar example. If you flip a coin 20 times, what are the odds that you’ll get a run of 15 tails in a row? It doesn’t take a statistician to tell you those odds are pretty low.
But what if you flip the coin 200 times? 200 thousand times? As the number of total coin flips grows so too do the odds of seeing a run of 15 tails in a row, for no other reason than that there are more opportunities for it to occur. While a run of 15 tails would be extremely unusual within a trial of only 20 flips, it would normal and expected within a trial of 200 thousand flips. In other words, if you flip a coin 200 thousand times, you should expect to see a run of 15 tails somewhere within that trial. An event that is unusual within a short trial is actually expected within a longer one.
The same is true of extreme weather events. Take the “100-year flood”. What a meteorologist or hydrologist means when they say a “100-year flood” is an event that has a 1-in-100 chance of occurring in any given year. Over a long period of time, we would therefore expect to see such a flood occur, on average, about once every hundred years.
What “100-year flood” doesn’t mean is that a given location is “due” for such an event if the last one occurred more than 100 years ago or that it is “safe” if the last one occurred less than 100 years ago. Every year, the odds are the same: 1-in-100.
The expectation of a given event (from a statistical, climatological perspective) does not change based on past weather. A “100-year flood” is just as likely to occur next year as it was last year.
Go back to the coin flip example: If you have a correctly balanced coin, each flip carries 1-in-2 odds of showing tails. Even if you flip 10 tails in a row, the 11th flip carries the exact same odds of coming up tails: 1-in-2. It may feel like you’re “due” for heads after 10 tails in a row, but that’s psychology speaking not statistics.
What does change after an extreme weather event is knowledge among the affected population. Once an extreme event occurs, people know 1) that it can happen, and 2) what the impacts are. So while the occurrence of one extreme event does not shift the climatological expectation for when a similar event will recur (e.g. suffering through a “100-year flood” this year doesn’t mean you’re safe next year), it can impact the foreseeability of a similar event. After all, people know that it can happen because they’ve already seen it happen.
The fact that a given weather event is within climatological expectations doesn’t necessarily make it foreseeable for the affected population. The weather-related impacts that population could reasonably have anticipated depend on a number of factors, including the weather forecast and impacts statements, as well as how that information was disseminated to the public and/or decision makers.
Weather forecasts, especially for major events, are generally quite good and have improved significantly over the past several decades. Anyone not living under a rock is unlikely to get surprised by a hurricane or a heat wave. Even the warning lead-time for tornadoes is up to an average of 13 minutes – plenty of time to take shelter if you get the warning. (We’ll return to that “if” in a moment.)
That said, sometimes meteorologists get it wrong. Small changes to atmospheric conditions can have major impacts on how a weather event evolves (that proverbial butterfly flapping its wings in Brazil). When there is significant uncertainty in a forecast, meteorologists try to express it. However, scientific uncertainty is a nuanced thing, and often the uncertainty statements are the first thing to get stripped for the headlines. So while meteorologists and decision makers with direct access to them may understand the forecast uncertainty and its implications, the general public may not.
Remember when Hurricane Irma was definitely going to wipe Miami off the map? Neither do I, because at no point during Irma’s evolution was an impact on the southeast FL coast a sure and immanent thing. But “Miami About to be Obliterated!” makes a far more clickable headline than “landfall near Miami looking possible, but storm could still swing further west or east”.
Which brings us to the final crucial piece in foreseeability: information access.
Even if a given weather event is within the climatological norms; even if the forecast is close to perfect, with uncertainty and potential impacts clearly and understandably communicated – even then a weather event can be “unforeseeable”.
If the people impacted by the weather event do not have access to or should not be reasonably expected to seek out forecast information, the event may not have been foreseeable. For example, every few years, hikers are injured or killed in the desert southwest when flash floods turn dusty arroyos into raging rivers.
That last question is arguably more difficult and is where forensic meteorology largely bows out of the discussion.
A forensic meteorologist can tell you whether a given event was climatologically unusual, whether the forecasts leading up to the event were accurate, and how that forecast information was disseminated.
But whether those who were impacted should have ultimately “seen it coming” is a more complex issue.
What if different forecasts disagreed? If a truly extreme event was forecasted, is it reasonable for people to think the forecast was exaggerated? What is reasonable ignorance or incredulity in this age of information-overload and often hyperbole? If someone has access to forecast information, should they be expected to actively seek it out? Should they be expected to act on it?
These are often the types of questions that arise in weather-related investigations and disputes. What starts out as a series of relatively straightforward meteorological questions ultimately winds into a complex web of human psychology and societal and cultural expectations.
The meteorological science is a crucial component of such investigations, but developing a rigorous and comprehensive understanding of the situation requires cross-disciplinary collaboration and communication. The weather is where the questions start, but not necessarily where they end.
If you are involved in a weather-related investigation and would like to discuss how a forensic meteorologist could support that work, please reach out to Blue Skies Meteorological Services for a free, no-obligation consultation.
A bolt from the blue. A rogue wave.
As anyone who has lived on planet Earth for more than a few seasons can attest, nature is full of surprises.
Lightning usually strikes near the core of a thunderstorm, but occasionally it will strike more than 20 miles away, arcing across an otherwise peaceful sky. Ocean swells on a placid sea tend to be of a similar height, but rarely, a rogue wave many times the average height can appear suddenly, damaging or devouring any ship unlucky enough to be in its path.
Bolts from the blue and freak waves are just two examples of variability and chaos in nature. Here chaos describes a sensitive dependence on initial conditions – the proverbial butterfly flapping its wings in Africa that leads to a hurricane over the Bahamas. If the surface temperature had been just slightly cooler, that bolt from the blue would have sliced the air directly under the storm. If winds over the ocean had been just a tad bit different, that rogue wave would never have formed.
It is due to this chaos that atmospheric scientists so often speak in terms of probabilities and distributions (as frustrating as that can be to folks who just want to know whether a rain shower will or will not do the work of watering their lawn this weekend – I promise if we knew, we’d tell you).
The chaotic nature of the weather leads to variability both between events and within a single event. Thunderstorms are a perfect example: no two thunderstorms are alike, and each storm changes from moment to moment.
To describe such variability, we typically speak of averages and deviations from that average. For thunderstorms, we can describe the average peak thunderstorm wind for an area – say 40 mph for summer afternoon thunderstorms in north-central Florida. However, peak winds in exceptionally strong thunderstorms have been recorded at over 70 mph in this region, while winds in weaker storms may not exceed 25 mph. And within a storm that produces a 70 mph gust, winds are not sustained at that speed throughout the event. The sustained wind speed within such a storm averaged over a 2-minute period may only be 45 mph.
Building codes typically ensure that structures and infrastructure are built to withstand expected conditions for a given area – roofs in snowy areas must be able to withstand a higher load than in areas without snow; structures in hurricane-prone regions must be able to withstand higher winds; structures in seismically active areas must be able to withstand earthquakes. It’s generally not average conditions that cause damage – it’s the exceptions, those events that vary greatly from what is normal and expected.
In the case of thunderstorm wind damage, it’s the gusts. How gusty winds are during a given event depends on a number of factors, but mostly on the surrounding terrain: the rougher the terrain, the gustier the winds. Winds are far gustier in the center of a city, surrounded by skyscrapers and densely packed buildings of many sizes than in a smooth, open farm field.
To illustrate, given a weather station measurement of the 1-minute average wind speed, we would expect peak 3-second wind gusts in the heart of a major city to be nearly 250% higher than that average speed, while peak wind gusts over open farmland would be less than 50% higher.
The orientation of landscape features to the wind direction also impacts wind speed and gustiness. For coastal areas, this is particularly noticeable. Onshore winds – those that have blown unimpeded over a lake or ocean – tend to be stronger and steadier, all else being equal, than winds that have blown over a rough landscape (say, across a large metropolitan area). Similarly, winds blowing parallel to city streets tend to be felt more strongly than those blowing perpendicular.
When evaluating weather station data to determine the peak wind speed associated with a given event, we must consider the location of the weather station. Is it a rural site or an urban site? What is the terrain like surrounding the station? Are there certain directions from which the wind would be more impeded or would be blowing over rougher terrain?
How does the weather station site compare to the site at which damage was reported? Is the surrounding terrain similar? Was damage reported at an elevated location – for example, the roof of a high-rise building, where winds tend to be stronger? Differences in both roughness and height must be considered.
Also of obvious importance in the evaluation of winds associated with tropical cyclones and thunderstorms is estimation of the relative strength of the system when it impacted the weather station versus the damage site. If the damage site was directly impacted by the core of a severe thunderstorm while the nearest weather station experienced only a glancing blow, we would expect the winds experienced at the damage site to be stronger than those recorded at the weather station.
Evaluation of these considerations – location, orientation, height, and roughness – allows forensic meteorologists to estimate peak wind speeds from measured average wind speeds and to evaluate the extent to which a given weather station is an accurate proxy for the site at which damage was reported. The weather station data is just the beginning.
“The devil is in the details.” Nowhere is this truer than in the beautifully chaotic weather, where details determine outcomes.
If you or a client experienced wind damage and need an estimate of the wind speed associated with that weather event, call or email Blue Skies Meteorological Services for a free consultation.
Weather radar works by emitting microwave radiation into the sky and then listening for the signal that’s reflected back. It’s a meteorological game of Marco Polo.
All sorts of targets reflect the microwaves – raindrops, snowflakes, hailstones, bats, airplanes, and even swarms of insects. How well a given target reflects microwaves depends on its composition, size, and shape. For instance, liquid water is a better reflector of radar energy than ice.
When a meteorologist looks at a radar display, she’s seeing the reflected signal from all those targets in a given slice of sky. The radar doesn’t “know” which piece of reflected energy came from a bird and which piece came from the hailstone that moments later cracked your car windshield. The radar simply aggregates the reflected signal. It’s up to the meteorologist to interpret the results.
Until just a few years ago, the National Weather Service’s network of weather radars collected information about only two quantities: the reflected energy from a given section of sky (reflectivity) and the velocity of the targets within that section (mean radial velocity and spectrum width). In complex meteorological situations like winter weather events or severe storms, these two pieces of information provide only an incomplete picture of the type of precipitation that’s falling. When you’re just looking at reflectivity and velocity data, for instance, it can be difficult to tell the difference between hail and heavy rain. Yet on the ground, knowing the difference can be critical.
Enter dual-polarization radar technology. If you’ve ever owned polarized sunglasses, you’re already familiar with the principle of polarization. The short-n-sweet version is that electromagnetic waves (like radio waves emitted by radar or visible light waves emitted by the sun) can be oriented along a certain axis.
Tilt your head from side to side while wearing polarized sunglasses, and you’ll notice that the image you see changes – the color of the sky darkens and lightens, glare off the pavement appears and disappears. As you tilt your head, you’re actually changing the polarization of the light that’s being let through your sunglasses, and that gives you additional information about the world around you.
The same is true with weather radar. Conventional radar sends out radio pulses polarized only in the horizontal direction, so the reflected signal carries only 1-dimensional information. Dual-polarization (or “dual-pol”) radar, on the other hand, sends out both horizontally polarized pulses and vertically polarized pulses, so the reflected signal carries 2-dimensional data.
This may seem rather trivial until you consider that precipitation types have characteristic shapes. Small raindrops are spherical, while big raindrops flatten out like a Frisbee. Hailstones are roughly spherical when they’re dry but can become oblong as their outer layers melt. The two-dimensional data provides invaluable insight into what types of precipitation are present within a storm.
Here in Florida, we don’t have to worry too much about winter weather, but hail is another matter. In the lightning capital of the United States, thunderstorms are part of the scenery for much of the year, and most thunderstorms, if they are strong enough and reach high enough into the atmosphere, produce hail.
But that hail doesn’t always reach the ground. In warm, moist atmospheres, hail melts as it falls toward the ground. If the hail starts out small or if the freezing level is high in the atmosphere, hail can melt completely before reaching the ground. Dual-pol radar data can reveal whether a storm is producing hail aloft, and by examining radar data at different heights within the storm, meteorologists can determine whether and how much that hail is melting before it reaches the surface (and people’s cars and houses).
Dual-pol radar adds three more tools to the meteorologist’s kit. Each of these tools provides unique information about the size, shape, and mixture of precipitation types within a storm.
Correlation Coefficient (CC)
Correlation coefficient measures how similarly the returned horizontal and vertical pulses are behaving. It’s like looking at the world under a strobe light. From one flash to the next, how much does the image change? When the targets within a given region are of the same shape and type (for example, all medium-sized raindrops), one pulse will look much like the next, and the correlation coefficient will be high. If, on the other hand, precipitation types are mixed (like rain and hail swirling together), correlation coefficient values will be lower. Generally, the larger the hail, the lower the correlation coefficient.
Differential Reflectivity (ZDR)
Differential reflectivity compares the reflectivity values returned in the horizontal and vertical directions, like comparing how much the image through your polarized sunglasses changes as you tilt your head. Targets that are wider than they are tall (like large raindrops) have higher differential reflectivity – they reflect more horizontally polarized energy than vertically polarized energy. Hailstones, on the other hand, are more spherical and tend to tumble as they fall, reflecting roughly equal amounts of horizontally and vertically polarized energy. Hail typically has low to near-zero ZDR values.
Specific Differential Phase (KDP)
Specific differential phase is a bit more complicated than correlation coefficient and differential reflectivity. Physically, KDP measures the phase shift of the returned horizontal and vertical signals. In practice, this means that specific differential phase responds to both the shape and the density of liquid water targets. Frozen precipitation, like dry hail and snow, do not contribute to KDP – KDP “ignores” frozen precipitation and sees only liquid precipitation. Specific differential phase is therefore useful for determining rainfall rate.
As part of the dual-polarization upgrade, National Weather Service weather radars now incorporate an algorithm that estimates precipitation type from the dual-pol variables discussed above. Numerous automated hail report websites use the National Weather Service algorithm or a custom one to identify regions of hail. While such algorithms provide a useful first-pass to identify regions within a storm where hail is likely being produced aloft, they do not provide information about whether that hail is reaching the ground and at what size.
When Blue Skies Meteorological Services investigates the presence of hail for a forensic meteorology case, we don’t just run an algorithm and depend on the radar to “know” what was happening in the storm and to assume what was happening on the ground. We examine official storm reports, severe weather warnings and advisories, the atmospheric profile, and dual-polarization radar data at multiple heights and throughout the lifetime of the storm to reconstruct a comprehensive picture of the weather situation – both high in the storm and on the ground, where it matters.
Typically, forensic meteorology is applied to weather events a few years old at most – Did damaging hail really strike that commercial facility in April of last year? Was it a tornado or a microburst that ripped off roofs and uprooted trees last week? Did a lightning strike start that house fire a few months back?
Occasionally, though, forensic meteorologists look decades or even centuries into the past. Such has been the case with Tropical Cyclone (TC) Mahina, which struck Bathurst Bay, Australia, on 5 March 1899 as a Category 5 storm. Since the publication of a research paper by H.E. Whittingham in 1958 titled “The Bathurst Bay Hurricane and associated storm surge” reported that TC Mahina produced a storm surge of 13 meters (or over 42 feet), that storm has generally been credited with the largest ever-recorded storm surge.
Contemporary accounts of the storm reported a waist-deep wall of water inundating a 40 ft tall ridge where several law enforcement officers were camped during the storm, dolphins being found stranded atop 15 m high cliffs after the storm had passed, and fragments of Aboriginal canoes being deposited 70 to 80 feet above the normal high tide.
TC Mahina was a undoubtedly a monster storm. With sustained winds of over 175 mph, it sank 54 ships (mostly pearling vessels) and killed more than 300 people, sweeping devastation across the Bathurst Bay region of northeastern Australia.
Yet despite its impressive statistics and a number of contemporary (albeit generally third-person) accounts of storm-related inundation, meteorologists have long regarded the 13 m storm surge record skeptically. It just didn’t seem possible. The commonly reported central pressure of 27 inches of mercury (914 mb), while extremely low, just doesn’t support a storm surge as high as a four-story building.
Previous studies that used computer models to estimate storm surge given the most likely track and intensity of the cyclone over this topographically complex region had come up well short of 13 m, and field work in the region had not found evidence of debris deposits to the reported 13 m height.
Something was amiss – either the central pressure was lower than 27 inHg or the storm surge wasn’t actually 13 m high. Possibly both.
Despite the contradictory evidence, little research had been done to set the record straight, until recently. In the May 2014 issue of the Bulletin of the American Meteorological Society (BAMS), several Australian scientists revealed the results of their forensic analysis of TC Mahina’s storm surge.
In that analysis, they utilized methods that forensic meteorologists often use to evaluate much more recent weather events: they investigated historical records, examined the physical evidence, and modeled the event. What they learned is that, as is often the case, the devil is in the details.
Previous modeling studies had relied upon a thirdhand account of Mahina’s central pressure published in an anonymously authored report several months after the cyclone made landfall. That central pressure – 27 inches of mercury (or 917 mb) – was simply too high to produce a 42 foot storm surge.
By combing through the historical record, though, the authors of the May 2014 study found several references to the storm’s central pressure as an astonishing 26 inHg (880 mb). All of those accounts ultimately were traceable to the same man – a ship captain whose schooner was the only vessel to experience – and survive – the eye of the cyclone. That captain, William Field Porter, also happened to write a letter to his parents recounting his harrowing experience. In it, he stated plainly that “the barometer was down to 26 [inches of mercury].”
So it would seem that the central pressure that had been used in previous modeling studies – studies that had failed to reproduce anything nearing a 13 m storm surge – had been too high. Perhaps the lower pressure of 26 inHg would create the reported record storm surge?
Before jumping straight into the modeling, though, the authors also re-examined the physical evidence – debris that was washed up and deposited by the storm. They found wave-deposited sandy sediments 6.6 meters above mean sea level at Ninian Bay, the location where law enforcement officers reported the 13 m storm surge, but they found no evidence of inundation above that.
This doesn’t necessarily rule out the 13 m water level, however. During tropical cyclones, the highest debris tends to consist of biological material that floats, like leaves, sea grasses, and small marine animals. This material also tends to biodegrade after a few years or decades, leaving no trace for forensic meteorologists peering 115 years into the past. Observations after more recent tropical cyclones suggest that sandy sediments can be deposited at only half the height of maximum inundation, so it is quite possible that the water did reach a height of 13 m above mean sea level at Ninian Bay, where those sandy deposits were found at 6.6 m.
Once the authors had concluded that there was a decent probability that the storm’s central pressure really was 26 inHg and that the waves at Ninian Bay really did reach 13 m above mean sea level, they set about to model the storm surge based on a range of storm forward speeds and storm tracks suggested by ships’ wind and pressure recordings as well as damage assessments after the storm passed.
What they discovered was that even in a worst-case scenario (Mahina approached Bathurst Bay from the northeast with a central pressure of 26 inHg), the storm surge would “only” be about 9 m.
And here is where the devil is in the details. You see, there’s a difference between storm surge and maximum inundation. Storm surge is the abnormal rise of water generated by a tropical cyclone, over and above the natural (astronomical) tides. Click here for an animation of storm surge in an area of steep topography, similar to Bathurst Bay. Storm surge is influenced by the size of the storm, winds speed within the storm, the forward speed of the storm as it approaches land, the angle of approach to the coast, the topography of the sea floor and coast, and the storm’s central pressure.
Maximum inundation, on the other hand, is just what it sounds like – the maximum height that water reaches above mean sea level. Maximum inundation is influenced by the height of the storm surge, the timing of astronomical tides, and various types of wave action like wave setup and wave run-up. It’s the high water mark.
And the high water mark is almost always higher than the storm surge. In fact, in severe tropical cyclones in northeastern Australia, wave and tidal effects have added approximately 25% to the height of maximum inundation.
What this means for Tropical Cyclone Mahina is that the 1899 accounts of a monster cyclone that brought the sea to the top of a 40 ft high cliff may in fact have been accurate. If the central pressure was actually 26 inHg and the storm approached from the northeast, it could have generated a storm surge of up to 9 meters (30 feet). Mahina struck during astronomical high tide, and that combined with wave setup and run-up could have added an additional 4 m (12 feet) of water on top of the storm surge.
So, the record for highest storm surge may have to be revised downward (Mahina’s storm surge was probably 9 meters or less), but its maximum inundation may still take the gold. It is entirely possible that on March 5th, 1899, men waded through seawater atop a 40 ft high cliff and dolphins swam through the tops of 50 ft tall trees.
Blue Skies Meteorological Services offers forensic meteorological analyses of a wide range of weather events — from hail storms to lightning strikes, from flooding to tornadoes, from fog to sun glare. We typically don’t look 115 years into the past, but we’re always up for unique and interesting challenges! Give us a call or send us an email to discuss your weather-impacted legal case, insurance claim, or investigation.
All good things must come to an end. After almost four months of relatively quiescent weather, the 2014 tornado season kicked off quickly and tragically over the weekend.
On Friday evening, the year’s first intense tornado (defined as an EF3 or stronger) touched down in Chowan County North Carolina, killing an 11-month old child who was trapped beneath the debris of his home. That storm brought to an end two record-breaking streaks of benign weather, marking both the latest calendar date for a year’s first EF3 tornado as well as the latest calendar date for a year’s first tornado death.
Only two days later, on Sunday, April 27th, an outbreak of severe storms spawned multiple tornadoes that killed 16 people in Oklahoma and Arkansas. The most substantial damage occurred in central Arkansas, where an 80-mile-long path of destruction swept through northern Little Rock, leaving damage reportedly indicative of an EF3 or stronger tornado. The same slow-moving severe weather system hammered Mississippi, Alabama, and Tennessee on Monday and is expected to continue bringing dangerous weather, including the possibility of strong tornadoes, to the southeastern US through at least Wednesday.
Although intense tornadoes are relatively rare, accounting for approximately 5% of all tornadoes nationally, they are responsible for a disproportionate 75% of all tornado fatalities (statistics for North Carolina). While each tornado fatality is tragic, tornado deaths have been generally declining in the US since the 1920’s, with an average of 80 people killed each year by tornado activity.
Although the majority of tornado damage and fatalities are attributable to rare intense tornadoes, even much more common weak tornadoes and severe straight-line winds can cause substantial damage to property, felling trees, removing shingles and siding from homes, and flinging debris into structures and vehicles. Most homeowners insurance covers storm damage, including damage caused by wind, hail, lightning, debris, and falling trees. One notable coverage exception found in almost all insurance policies, however, is storm-induced flooding, including street flooding, storm surge, and areal flooding due to rising rivers, streams, and creeks. For such coverage, a separate flood insurance policy is required.
In some cases, though, street flooding is caused not by an exceptional storm (i.e. an “act of God”) but rather by an insufficient storm water drainage system. In such instances, liability for damages may rest with the planning or maintenance authority responsible for the storm water system, rather than with the homeowner.
If a neighborhood or section of a neighborhood regularly floods, even during normal, everyday storms, the drainage system may be deficient. A forensic meteorological analysis (like this one from BSMS) of known storm events that led to street flooding, considered in the context of the local rainfall climatology, can reveal whether the drainage system was adequately designed and maintained to handle foreseeable events.
In addition to flood damage, homeowners insurance will not cover damage caused by a lack of proper maintenance. Occasionally, negligence may be suspected as a contributing factor to storm damage, leading to a denial of claim, even when it is not immediately clear whether damage would have still occurred with proper maintenance.
For instance, if a tree falls during a storm and is later found to be rotten, the insurer may deny the homeowner’s claim, insisting instead that negligence on the owner’s part (failing to remove a rotten tree) caused the tree to fall, rather than the storm. Such insurance disputes can lead to nasty legal battles. Investigation as to whether the homeowner knew or suspected that the tree was rotten (i.e. whether he or she was on notice), examination of other damage throughout the area (did healthy trees of a similar size fall nearby during the same storm?), as well as a forensic meteorological analysis (were wind speeds with the storm sufficient to fell a healthy tree of that size? did heavy rainfall and saturated soils reduce the root stability of the tree?) can greatly assist in determining the ultimate cause of the damage and thereby assist in settling such disputes.
So, the bottom line is this: storm season is here, and after a late start, it appears to be making up for lost time (at least at the moment). Extreme weather, which includes severe local storms as well as tropical cyclones, droughts, heat waves, areal flooding, wildfires, and winter storms, causes tens to hundreds of billions of dollars in damage annually in the US.
Severe local storms are, on average, responsible for more than 10% of all damages, with tropical cyclones and droughts/heat waves responsible for nearly 50% and 25%, respectively. While severe local storms are not responsible for the largest percentage of damage costs, they do represent the most common/frequent type of extreme weather experienced in the United States, and almost everyone, at some point, will experience storm damage. Make sure you understand your property insurance policy, including any exceptions, and take care of any nagging maintenance issues (like rotten trees or loose roof shingles) that could jeopardize a storm-related insurance claim.
In the event that you do find yourself in a weather-related insurance or legal dispute, whether as the insured or the insurer, the plaintiff or the defendant, do not hesitate to contact Blue Skies Meteorological Services. We will gladly provide a complimentary consultation to discuss how a forensic meteorological analysis could determine the role that the weather played in your case and how such an analysis could facilitate an advantageous resolution of the dispute.
Next time: Weather-impacted automobile accidents