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.
Even Florida got in on this week’s pre-holiday winter chill, with Blue Skies’ home base of Gainesville, FL, breaking records for lowest maximum temperature (53 degrees on Nov 18) and lowest minimum temperature (24 degrees on Nov 20). (Yes, yes, the world’s tiniest violin is playing the world’s saddest song for the poor, shivering Floridians while upstate New Yorkers roll their eyes and stoically shovel out from 6+ feet of snow.)
This fierce onset of winter caught many people across the country by surprise. After all, it’s not even Thanksgiving. Although temperatures will be moderating over the weekend for much of the US – bringing the risk of flooding to many affected by this week’s snowstorm in the northeast and a welcome and benign warm-up to many others – the annual rollercoaster of winter weather is just beginning.
If you’ve ever wondered why winter weather fluctuates so dramatically, you’re not alone. And you can place much of the blame on the location of the polar jet stream. Yeah, we know – it’s easier to blame the meteorologist messenger. But hear the messenger out on this one.
Jet streams are like fast-moving rivers of air in the upper troposphere, at approximately the same altitudes that commercial aircraft cruise. And just as rivers of water flow faster when the elevation change is dramatic and steep, jet streams become more vigorous during the winter, when the temperature difference (“gradient”) between the poles and the equator is more dramatic (it’s beach weather year round in Key West, but come January in Maine, you’re going to want to be wearing more than just a swimsuit).
As winter progresses, the pool of cold air at the poles expands and sinks southward. Since jet streams are found where the temperature gradient is largest – at the boundary between cold and warm air – the polar jet slides southward along with the expanding pool of cold air. Although in summer, the polar jet is typically pinned near the US-Canada border, in winter it can plunge as far south as Florida.
The polar jet isn’t straight, either, but rather meanders from north to south, bringing that characteristic wintertime rollercoaster of relatively warm and sunny weather (under ridges) followed by cold, dreary, and occasionally downright miserable weather (in troughs).
Where the jet stream ends up draping itself and how strong it is determines much about the winter’s weather. Certain large-scale factors (like the presence of an El Niño or La Niña), can exert a powerful influence on the average position of the jet stream and therefore on seasonal temperatures and precipitation.
Despite indications this summer of a developing strong El Niño, it hasn’t materialized. Forecasters at the Climate Prediction Center are now calling for about a 60% chance of a weak El Niño developing this winter. This lack of a strong climate driver, like El Niño, makes seasonal forecasts somewhat less certain.
Although seasonal forecasts will never be able to predict daily high temperatures or the probability of afternoon precipitation months in advance, they can offer insight into general patterns and trends, like whether this winter is likely to be warmer or cooler than average. The strength of those patterns and trends, and therefore the skill of the seasonal forecast, is highest when strong, large-scale climate drivers dominate.
So, what about this winter’s weather? NOAA forecasters at the Climate Prediction Center are anticipating cooler than average temperatures across much of the Southeast and Southern Plains, with above average temperatures favored in the western US and throughout Alaska. Wetter than normal conditions are more likely throughout the southern US and along much of the East Coast, while drier than normal conditions are favored in the Northwest and Upper Midwest.
Given the fairly weak El Niño signal this year, forecast confidence isn’t particularly high, but for those folks in the northeast still reeling from this week’s snowstorm, take heart. That winter pummeling isn’t likely to be the season’s norm. But for those of us in Florida, it might be worth picking up another couple of ugly holiday sweaters from the sale racks… you know, for layering.
Fresh water is among the earth’s most precious resources – we drink it, cook with it, bathe in it, farm with it, and use it in the generation of much of the world’s electricity. It is fundamental not only to life, but to our way of life.
Yet water availability is not assured for billions of people across the planet, and research has indicated that in the near future, an even larger percentage of people will likely face water scarcity.
The reasons behind the projected increase in water scarcity can be boiled down to supply and demand.
The supply of fresh water comes from precipitation and is stored in lakes, rivers, aquifers, and snowpack. Weather obviously affects the water supply from season to season and from year to year, but over the long term, climate is the main driver.
When the climate is in a relatively steady state (as it was for about the past 12,000 years as humanity developed agriculture, civilization, and technology), so too is water availability. Sure, droughts and very wet periods occur, but over decades and centuries, it tends to even out.
However, when the climate is rapidly changing (as it is now), water availability becomes less certain. Precipitation patterns shift and so too do the locations and levels of lakes and rivers, aquifers and snowpacks. The sources we have depended on for water become undependable.
That’s what we’re facing now. The supply of fresh water is shifting – increasing in some places and decreasing in others. Unfortunately for us, many of the regions that are expected to see a decrease in total water availability are also heavily populated.
And here is where supply predictably meets demand: people use water. Primarily, we use it to grow food and to produce electricity. In the US, these two uses account for over 75% of total water withdrawals.
As the global population grows and becomes more industrialized, we have more mouths to feed and more high-tech lifestyles to power. If we continue with business as usual, we could face a direct conflict between agriculture, electricity generation, and other water uses by 2040. We could literally use up all of the available water in the system.
Judicious and mindful use of water (i.e. not being blatantly wasteful) and adoption of more water-efficient farming practices can go a long way towards conserving water resources (demand side), while the energy sector offers opportunities for a “twofer” — both reducing water use (demand) as well as mitigating climatic changes that threaten to disrupt water availability (supply).
All thermoelectric power systems (like the combustion of coal or natural gas to produce steam that drives turbine generators) require inputs of water, both to create the steam and often to cool it. Meanwhile, if the power plant relies on a hydrocarbon fuel, it’s also emitting carbon dioxide and other greenhouse gases.
Solar and wind power are familiar and growing alternatives to traditional thermoelectric electricity generation methods, and they offer the twin benefits of significantly reduced water use and dramatically reduced greenhouse gas emissions. For people living in developed regions that can provide the supporting infrastructure and dependable maintenance that solar and wind systems typically require, these alternative energy solutions are very promising.
But for people living in less developed or simply less accessible regions, portable gasoline- or propane-powered generators are often their only option — although perhaps not for much longer. Andrew Kazantsev and his team of Russian scientists have reportedly developed a device that collects atmospheric moisture and channels it down to the ground where it can be used for both drinking water and electricity generation.
The device, called Air HES looks like a small dirigible (aerostat) with a fine mesh hanging below it. The aerostat rises to the mid-levels of the atmosphere, where water vapor and water droplets in clouds condense onto the mesh and are funneled to the ground. The water pressure from the descending stream of droplets can then be used to power a generator and create electricity.
Kazantsev reported that the prototype Air HES was able to create approximately 5 liters of fresh water per hour from low level clouds. If the technology scales successfully, it could provide not only portable clean electricity generation but also potable water to inaccessible and/or undeveloped regions where both are sorely needed.
Technology and the need for electrical power have inarguably propelled us into this water scarcity and climate change challenge, but with ingenuity and willpower, technology may well help us out of it as well.
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.
Having grown up in Oklahoma, in the heart of Tornado Alley where annual violent twisters are just part of the springtime scenery, even I was initially a bit surprised when I heard of a new report out of the Southeast Regional Climate Center (SRCC) at the University of North Carolina. According to research by Charles Konrad II and his team at the University of North Carolina (UNC), the state in which tornadoes kill the most people per mile tracked on the ground is not Oklahoma or Kansas, not Texas or Arkansas or Mississippi – it’s Florida.
Now, Florida is no stranger to tornados. In fact, per square mile, Florida has more tornados than any other state in the country. But they’re usually not violent tornadoes – not like the EF5 monsters that ripped through Joplin, MO, in 2011 and through Moore, OK, in 1999 and 2013.
The vast majority of violent tornadoes are spawned by long-lived supercell thunderstorms, and weather patterns in Florida just don’t support those sorts of storms. Instead, Florida typically experiences weaker tornadoes, often spun up by interactions with the Gulf Coast and Atlantic sea breezes or by tropical cyclones. These tornadoes can cause substantial damage (e.g. roofs and siding removed, trees uprooted, cars flipped), but it’s not the sort of damage that one usually thinks of as causing widespread loss of life.
And therein lies the initial – but not necessarily warranted – surprise. When we think about risk, we tend to oversimplify the equation. We tend to assume that exposure = risk. We figure that the bigger, badder, and more frequent the hazard, the more people are likely to be harmed by it. By that reasoning, the southern Plains and the Deep South should have the deadliest tornadoes. Those are, after all, the regions of the country that experience the highest frequency of strong tornadoes. In other words, that’s where the greatest exposure per square mile is.
But that’s not where the highest density of tornado-related deaths occur. According to Konrad and his team, that dubious honor – greatest number of deaths per mile along the track of a tornado – goes to Florida.
To understand why, we have to look at the real risk equation.
Risk = Exposure x Vulnerability
Exposure per square mile is only part of the story. Sure, you have to have tornadoes on the ground for people to be killed by them – but you also have to have people in the path of the tornado who lack the appropriate resources to protect themselves.
To understand why Florida’s risk for tornado deaths is so high, we can compare it another state with almost exactly the same average number of tornadoes per square mile: Kansas.
According to the SRCC study, the number of deaths per mile along tornado tracks is nearly five times higher in Florida than in Kansas. Yet, while Florida and Kansas experience almost the same number of total tornadoes per square mile, tornadoes in Kansas are, on average, stronger than in Florida.
So, why isn’t Kansas at the top of the list? The answer has to do with population density and population vulnerability.
The number of people in the path of the tornado is maximized when tornadoes form and track over populated areas. In Florida, tornadoes tend to cluster along the populous Atlantic coast and along a stretch of Intersate-4 from Tampa to Orlando.
The population density in these regions ranges from about 300 – 1000+ people per square mile. By contrast, only one county in Kansas has a population density above 1000 people per square mile, and the vast majority of the state has a population density below 50 people per square mile. In fact, the average population density of Florida is more than ten times greater than that of Kansas.
So, when a tornado touches down in Florida, it’s much more likely to encounter people along its path.
There are also a number of demographic factors that make Floridians more vulnerable to tornados than Kansans.
This study out of UNC reminds us that risk assessment often has more to do with human systems and the built environment than with the natural hazards themselves. Risk exists in that intersection of exposure and vulnerability – exposure is largely a matter of where we live, while vulnerability is largely a matter of how we live. Effective risk mitigation requires understanding and addressing both.
Blue Skies Meteorological Services can help businesses identify their exposure and vulnerability to weather and climate impacts so that risks can be effectively targeted and reduced while resiliency is simultaneously built into operations.