An Even Greater American Eclipse

In 2017, I had a special opportunity to view the last total solar eclipse that passed over North America, dubbed the ‘Great American Eclipse’ for its sweeping cross-country path of totality from Oregon to South Carolina and unprecedented (at least for eclipses) social media virality. Now, another total solar eclipse will trace a path from Texas to Maine on Monday, with several million people expected to flock into the shadow of totality to catch a minutes-long glimpse of this rare celestial phenomenon. For weeks, I’ve seen article after article about the eclipse, how best to view it, how much economic benefit and traffic are expected; perhaps it’s just my algorithm, but I sense substantially more hype than last time. And I believe the hype is justified, since laying eyes on a solar eclipse is one of the few truly indescribable and soul-moving experiences I have had, however fleeting.

I’ve been asked several times where would be the best place to maximize one’s eclipse-viewing experience – in all honesty, getting a clear view of the sun at any given moment is largely up to chance. We can count on cloud cover predictions a few days in advance, which indicate that Texas will have a cloudy day but the rest of the path varies from sunny to partly cloudy. I will be spending the weekend in Lebanon, NH (all lodging was filled in the northern half of Vermont) and plan to drive to Waterbury, VT to view totality: by some strange luck, northern New England has the highest chances for a clear sky despite only ~30% of days being even partly sunny at this time of year. The region received about a foot of snow this week, which should help ensure a clear sky by suppressing surface-based absorption and convection (the most difficult clouds to predict)…I am keeping my fingers crossed that this will be enough to keep the clouds away from our view.

3-day cloud cover forecast (in % coverage, where white counterintuitively represents the clearest sky). Source: Pivotal Weather

While the rest of us gawk at the eclipse’s visual incredibleness, the brief minutes of totality present a unique opportunity for scientists to conduct meaningful research. In the past, solar eclipses have provided a window for the first observation of coronal mass ejections, the discovery of helium, and the first proof of light bending around a massive object like our sun. But several interesting studies came out of the last eclipse, presenting new questions that could be answered during this one. For example, NASA will be taking broad-spectrum imagery of coronal behavior, which should be especially interesting near the 11-year max of this solar cycle. Another study aims the instruments at the ionosphere, trying to measure its interaction with light in order to identify disruptions that could affect global communications. I will be particularly interested in the boundary layer observations that come from the eclipse path, since atmospheric soundings and remote sensing can reveal hidden airflows that can assist with turbulence and cloud formation models. As the next cross-country total eclipse is not until 2045, I hope we can make the most of this opportunity at all levels of observation!

Topography and Tornadoes: a Recent Case Study

In the ten years since I became interested in this question, there have been several significant advances in understanding the influence of topography on tornado development. The Midwest, a region known for flat and boring terrain, serves as an ideal testbed to observe these influences, offering the ability to isolate topographical variables against a flat control domain. This post seeks to identify topographical drivers and assess the physical mechanisms within a recent tornado outbreak that spawned over a dozen tornadoes across Ohio, Indiana, and Illinois on March 14th.

Zooming out to assess all possible terrain variables, Kellner (2012) first employs GIS methodologies to establish statistical correlations between tornado touchdown points and several spatial features: elevation, land use, surface roughness, antecedent rainfall, and more. Using a high-resolution buffer analysis, the study highlighted that 64% of all tornado touchdowns occurred near urban land use and 42% near forests, a moderate-to-strong correlation given the overwhelming presence of flat farmland and range. The consensus explanation for this correlation is that increased surface roughness causes more horizontal vorticity that can contribute rotational energy when advected into a tornadic storm via the streamwise vorticity current, though I would also posit that urban heat island effects can add significant energy (localized SBCAPE) as well. A strong example of this formation mechanism occurred when a tornado coalesced downwind of Muncie, IN as an EF2, briefly lifted off the ground over Farmland (that’s a town name, though the terrain type is implied), then churned at up to EF3 strength for another 40 miles into Ohio.

Selma, IN (EF2) and Winchester, IN (EF3) tornado damage paths from the evening of 3/14/24 (NWS)

To dive deeper into the physics of tornado-ground interactions, Satrio (2020) experiments with a well-established LES model to simulate how a tornado-like vortex moves over various hill configurations. Near-ground flows have been a popular research topic for their damage potential and effects on vortex stability, inspiring numerous observational studies using radar, photogrammetry, and debris impact analysis. Satrio’s simulation results paint a particularly cogent picture of tornado dynamics, how uphill slopes can cause a low-level vortex disruption, how downhill slopes cause an intensification of swirl, and how vortices can bend to remain perpendicular to sloped terrain. All three of these effects can be clearly observed in the tornado that touched down near Hanover, IN: after crossing the Ohio River, the tornado weakened to an EF0 at the top of the first ridge, intensified to an EF2 as the track turned to follow the southern edge of the valley, weakened back to an EF0 over the second ridge, and reintensified to an EF2 upon crossing the valley again. Variations of this tornado evolution behavior have been corroborated numerous times in recent literature, most clearly by Lyza and Knupp (2018) on the periodic ridges of northeast Alabama, Bosart (2006) in the Hudson Valley, and Wagner (2018) near the Kansas River.

Hanover, IN tornado (3/14/24) damage path strongly influenced by Ohio River valley terrain (NWS)

However, tornado damage paths do not always show such visible signatures of terrain influence. Sometimes, as in the brief EF2 tornado near Plymouth, OH, there are absolutely no interesting topographical features in sight (though I would argue that the absence of terrain heterogeneities likely prevented further intensification within this supercell). In other instances, inferences about the nature of storm inflow would be needed to consider any terrain interactions. For example, the long-track tornado that struck Lakeville, OH as an EF3 does not have any significant hills, cities, forests, or geographic boundaries along its path. But, when the initial EF0-EF1 tornado crossed Grand Lake, the vortex was turned northward to favor the inflow side. Several miles later, likely due to the increased humid inflow, the tornado reformed as an EF3 near Wapakoneta and rumbled for almost an hour toward the northern suburbs of Columbus. The presence of the urban/suburban roughness on the inflow side, along with the Scioto and Olentangy River valleys, likely gave this tornado the extra push to reform as a long-track EF1 near Delaware, OH. These terrain influences are more subtle and speculative, of course, but the potential for these types of observations sparked my interest in tornado simulation and prediction in the first place. I firmly believe that localized surface conditions play a major role in the formation of not just tornadoes but many weather phenomena, and it’s always gratifying to find physical evidence in support.

Grand Lake, OH EF1 tornado that preceded the Lakeville, OH EF3 on the evening of 3/14/24 (NWS)

Delaware, OH long-track EF1 tornado formed from the remains of Lakeville, OH tornado (NWS)

Bunch of nothing around Plymouth, OH EF2 tornado path, 3/14/24 (NWS)

Tornado path maps from the NWS Damage Assessment Toolkit, preliminary data

Second Nor’easter as a Northeasterner

Since my first one last year was a dud, I write from amid a true Nor’easter – this time I’m riding it out in Norwich, Connecticut. As of Sunday evening, it’s basically over: roads plowed, driveways shoveled, snowmen built. It’s been nearly 2 years since this caliber of storm impacted the Northeast, which probably led to some of the added hype that I felt from watching the Weather Channel. But as far as I could tell from my position, this was handled as a routine winter storm by emergency managers and the general public, which makes sense because it was a fairly typical snow event for the region. The first wave of snow started at 7pm yesterday, dropping 3 inches over a few hours to cover everything in a thick, wet, white blanket. Warmer temperatures overnight changed snow to rain, and I woke up to a scene where the snow blanket was even wetter and had slumped in spots. The winds shifted back to the north and northeast by mid-morning, then another 3 inches of lighter, dryer snow fell throughout the afternoon as the “Nor’easter” pattern took shape. By this evening, the flurries have dwindled as the low pressure center moves away over the ocean.

What surprised me most, in a storm with few surprises, was how accurate the forecasts were, even several days in advance. Meteorologists were in agreement that the freezing line would likely follow Interstate 95 (not because the highway affects the weather, of course, but because it happens to follow the boundary between the coastal plain and Appalachian foothills), where areas south and east of the I-95 corridor would primarily experience rain and areas merely 30 miles inland could see 8-12 inches of snow. The second phase of the storm had slightly more uncertainty, dependent on the rotation of winds to advect moisture from the colder Gulf of Maine back down to southern New England: despite some wavering about the timing of the storm, forecasts accurately captured the sharp difference between areas north and west of Boston that received upwards of a foot versus all points south that received only a couple of inches. All told, the snow totals, type of precipitation, windspeed and direction, and timing of the storm were well-forecasted and communicated in an operable way. 

Edit, a week later on January 13th: It turns out that the effects of a Nor’easter can be felt for several days after the fact. When the next low pressure system came on Tuesday night, bringing 2-3 inches of rain and 40+ mph winds with warm temperatures, all the snow melted and caused extensive flooding of low-lying areas across the region, including just a couple miles down the hill where the Yantic River burst through a 19th century dam and flooded Norwichtown. Coastal flooding inundated Portland, ME from the confluence of high tides, snow melt runoff, and storm surge. Even today, our walk around Minuteman NHP in Concord, MA was disrupted by a Concord River flowing several feet over its banks near the Battle Road bridge, the product of a foot of snowmelt plus 4-6 inches of rain from successive storms. These two lows followed similar tracks from SW to NE, but with the jet stream positioned a couple hundred miles farther north, the humid air pulled from over the Gulf Stream carried with it only unseasonable warmth and torrential rain. The fact that the last two winters have brought more moisture in the form of rain and almost no snow is certainly noteworthy, as New England is the fastest-warming area of the United States statistically. Perhaps this is what Nor’easters will look like in the future: with rain in warmer coastal areas and snow only inland or at elevation?

The Blue Hill Meteorological Observatory

I’ve lived in Massachusetts for a year now, watching the seasons change from week to week, month to month. My favorite place to observe this passage of time is Blue Hills, a large preserve a few miles south of Boston that serves as my natural retreat from the hustle and bustle. Whether I am hiking, jogging, or mountain biking on the 100+ miles of trails, I watch for the subtle shifts in color, the steady evolution of life through the seasons. From a largely brown forest in January, when pops of green came from tuffets of moss and stands of pine and hemlock. To February, when snow blanketed the forest floor and fell in clumps from slumping conifers. Then came the floods and the buds in March and April, as trees and grasses tried to break through the cold dampness. May brought warmth and an explosion of life, filling the forest with young green leaves, chirping birds, and bell-shaped blueberry blossoms. In June, the laurel blooms transformed my favorite trail into a lacy wonderland of white petals. July brought wild blueberries by the bushload, sweet from the sweltering sun.  The heat carried through August, and the park would be the perfect shady refuge if not for swarms of gnats and mosquitoes. Leaves began to turn with cooler weather in September, leading to a beautiful patchwork of yellow and orange by October. Wind, rain, and the first frost kicked off November, erasing the trails with an ankle-deep pile of colorful leaves. As December approached, the leaves faded to brown and flattened into the forest floor, my feet instead crunching on ice now as winter impends.

Now, I’m not the first person inclined to watch the seasons here – in fact, this very park is the cradle of American meteorology, historically speaking. In 1885, MIT scientist Abbott Lawrence Rotch founded a weather observatory on Great Blue Hill, a high point with panoramic views of the greater Boston area and Massachusetts Bay. From the stone tower, Rotch began what is now the oldest continuous meteorological record in North America, over 138 years of daily temperature and pressure readings, wind data, and other notes. In the 1890s, Rotch’s team pioneered the use of kitesondes for atmospheric profiling, using these results along with geometric techniques to develop an early understanding of cloud heights and movement. Some of the earliest weather balloons were released here in the 1930s, probing the upper atmosphere for the first time under a range of weather conditions. The highest hurricane windspeed over land was recorded here in 1938, a powerful 186 mph gust during the infamous Long Island Express.  Today, the tower is home to a large array of (mostly duplicative) instruments, automatically recording but manually analyzed by a team of volunteers for the best possible continuation of the record. Beneath the tower is a small museum, which is an interesting window into the history of meteorological observation for children and weather buffs alike. The view from the observatory is unmatched, an aerial view of Boston that can extend to Cape Cod, Narragansett Bay, and Mount Monadnock on clear days but usually just gives you an up-close-and-personal view of the clouds as they move and evolve overhead. In any weather, this is a very cool site to have close to home, and I am honored to participate as another data point in a rich observational tradition. 

Blue Hill Observatory

Skyline view in winter

Ponkapoag swamp pre-spring

Laurel path in full bloom

Lily pond in summer

Golden autumn

Disasters in the Headlines

This September, at least on the east coast, we have been inundated with storm-related coverage. Hurricane Lee was the top weather headline for multiple weeks as it churned all the way across the Atlantic, cycling between a tropical storm and a Category 5 hurricane and back over open water. But its threat was ominous enough to cause Massachusetts and Maine governors to declare states of emergency, sending Nantucket boat owners into a frenzy as they rushed to dry-dock their vessels ahead of the potential storm surge. While Lee’s chaos was brewing, a fast-moving evening cold front brought a line of severe thunderstorms, including a few spin-up tornadoes, which triggered tornado warnings for most of southeast New England. Not much damage resulted from these tornadoes, just a few downed trees here and there along paths that spanned between 2-3 miles of Connecticut, Rhode Island, and Massachusetts. Likewise, Hurricane Lee brought only a wave or two of blustery rain to the eastern portions of Massachusetts and Maine as it had already fizzled into a post-tropical depression, and a single person was killed in Searsport, ME when a tree branch fell on his car, a freak accident in an otherwise manageable storm.

Halfway around the world, there have been far worse disasters in northern Africa that we’ve heard comparatively little about. First, a powerful 6.8-magnitude earthquake shook Morocco on September 8th, causing 3,000 fatalities and widespread damage in and around Marrakesh (made worse by the rock and mortar architecture, which is particularly prone to crumble and collapse). Later in the same week, Mediterranean Storm Daniel poured 6-9 inches of rain on northeastern Libya, causing a poorly engineered dam to break and flood the coastal city of Derna. Tragically, about 4,000 people have died and 40,000 more have lost their homes, in a place that has no formal governmental means to help people recover. I’ve seen headlines linking Hurricane Lee – a typical September cyclone in the Atlantic – to climate change, while much more pressing are the climate implications of the warm Mediterranean that fueled Daniel’s historic rainfall. I understand giving precedent to storm stories closer to home, especially since their warning information is actionable; but I wish the public was also given the context that some events have far more catastrophic outcomes than others. With that context, I am grateful to live in a place with effective weather prediction, thorough warning communication, enforceable engineering standards, and few true natural disasters – the ultimate blessing of security that I have sometimes taken for granted.

Wildfire Summer

Summer 2023’s weather news, aside from some floods in Vermont and some El Niño-related heat waves, will be defined by wildfires. First it was the largest wildfire in Nova Scotia history, blazing for over 2 weeks and causing air quality warnings as far south as Virginia. These fires were superseded by a massive wildfire in northern Quebec, which raged out of control and caused terrible air quality throughout the eastern United States for much of June. As the summer wore on, wildfires ignited in drought-stricken Texas and other places across the southwest United States. And North America wasn’t alone: millions of acres burned in a historically severe wildfire season in Siberia, and numerous devastating wildfires sparked from heat waves in southern Europe. Even places where it’s not the usual dry season have been affected, with swaths of South Africa and South America going up in flames due to human activities such as deforestation and arson (I mention this not to cast aspersions, but despite all the talk about climate change as a major cause of wildfire, roughly 85% of wildfires are suspected to be directly sparked by human activity with 20-25% of those started intentionally, i.e. arson).

The wildfire that burned Lahaina, Maui was worse than all of these and will be emblazoned in my mind for awhile. Due to a number of factors, including that alarms didn’t go off for some inexplicable reason, over 100 people have perished in the disaster, making it the deadliest wildfire in the U.S. since 1918. Tensions that already existed between locals and the powerful tourism industry were exacerbated as historic Lahaina was completely burned along with any semi-affordable housing on the west side of the island. It’s an absolute tragedy – not only that a place that I described as a paradise was incinerated, but that the people who lived there will be scarred forever by this disaster. It’s also a sobering reminder that public officials and regular citizens alike need to take seriously the dangers associated with living with a changing climate, with non-native plants, and with our extensive but essential human infrastructure.

The Chaos of Massive Supercells

A rather unexpected disaster sprang from a ‘slight’ risk the other day in Oklahoma, as several supercells erupted within an energetic warm sector ahead of a slowing cold front. While the northern cells produced hail as they drifted to the northeast then dissipated, the three southernmost cells merged to form a large, rapidly cycling storm (radar loop linked) that would drop 13 tornadoes in 90 minutes, including an EF3 that devastated the town of Cole and an EF2 that wrought extensive damage upon Shawnee. Sometimes called ‘mothership’ supercells for their impressive rotating cloud formations, these storms often form over the high plains where land surface homogeneity favors large near-laminar inflow regions. In contrast to the predictable linear paths of tornadoes within typical supercell or QLCS thunderstorms, these massive supercells produce tornadoes that revolve around a central vortex, fanning out on highly chaotic paths. The tornadogenesis is clearly top-down from the large parent vortex, and the wide inflow largely smooths the effects of any topographical heterogeneities.

10+ tornadoes from a single supercell in central OK, 4/19/2023. NOAA Damage Assessment Toolkit

Rather than belabor my land surface model with a scenario where tornadoes evolved independently of terrain, I want to draw comparisons to tornadic events in recent memory, specifically a pair of events in 2017 when I was just beginning my deep-dive into tornado modeling and prediction. First, on April 14th, a mothership supercell formed in the Texas Panhandle then parked near Dimmitt, cycling in place for three hours and spawning several tornadoes including a mile-wide EF3 wedge outside of town. This spectacular parent supercell made another appearance on this blog for its textbook bubbling outflow as observed by the GOES-16 satellite. Then, just two weeks later, Canton TX was besieged by an analogous storm, a massive rotating supercell that spawned a couple of long-track tornadoes (EF3 and EF4) that ground chaotically northward over the course of more than an hour. While these three examples will be further examined as top-end tornadic events, mega-supercells (is this a good term, or do we need even more prefixes?) that produce several tornadoes are vanishingly rare, responsible for roughly 1% of all recorded tornadoes in the last decade. Worth studying, certainly, but I believe they deserve their own category distinct from conventional supercells because their behavior is so uniquely unpredictable.

Little Rock Tornado 3/31/2023

Perhaps the most predictable tornado in my memory struck Little Rock, Arkansas on Friday afternoon, causing widespread damage and dozens of injuries. The Storm Prediction Center issued a rare “Particularly Dangerous Situation” warning and the convective outlook showed a large swath of Level 5 hazard risk (the highest) from central Arkansas northward into much of the Midwest. Part of a large outbreak of over 100 tornadoes across the warned region, this particular tornado was preceded by radar-confirmed lowerings near Hot Springs about an hour prior to touchdown, prompting a tornado warning with an above-average lead time of 21 minutes. As the storm crossed over some hills into the west Little Rock suburbs, it spawned a high-end EF3 tornado with winds up to 165 mph along a 34-mile path of destruction that crossed the entire metropolitan area.

Zooming in on the beginning of the path, the pre-established rotation intensified after passing over a valley of sheltered air then touched down as a wedge tornado after clearing Ellis Mountain. Presumably, the air in this valley was warmer relative to the surroundings, carrying an additional >100 J/kg of surface-based CAPE (convective available potential energy) when it was forced into the inflow by the topographical obstruction. When cooled, this air mass would contribute to the pressure fall inside the vortex, tighten the rotation and increase the kinetic energy (read: velocity) of the rotating winds. The computational model that I started this blog in part to document specializes in forecasting this exact scenario, quantitatively simulating the gradients in surface temperature and humidity that affect characteristics of the inflow.

Of course, the three-dimensional simulation of a tornado within a parent supercell is computationally expensive, essentially requiring a supercomputer even for a restricted domain. Even generating the differential temperature map can prove challenging, both due to computational demands and because slight deviations in input parameters can yield significantly different results. Perhaps a more practical approach than on-demand simulation would be to aggregate a range of possible pre-storm environments into a base layer that can readily be created/used by forecasters – call it a “tornado intensification risk” map for generalized conditions. While my land surface simulation work garnered only tepid interest from meteorologists and academics, the prospect of this type of hazard map piqued the interest of several representatives for insurance companies. Not that insurance rate adjustment is the only application for this type of information – in the time that this project has sat on the back burner as my life became more hectic since 2020, the idea of an aggregate data layer for tornado risk has grown on me for public access in forecasting and emergency preparedness, sacrificing scientific specificity for ease of use.

An open-access risk map for tornadoes, even as a developmental release, would be highly beneficial, especially for urban areas where tornado strikes pose an elevated threat to life and property. Little Rock was highlighted in my preliminary simulation-based testing as having a tornado risk higher than its surroundings, part of a short list that included Birmingham, Nashville, Louisville, and Tulsa. Smaller cities with elevated risk included Rome (GA), Gadsden and Tuscaloosa (AL), Conway and Fort Smith (AR), Joplin and Cape Girardeau (MO), Quad Cities and Peoria (IL), among a few others. Locations downwind from the highlighted population centers would benefit from this information as well: the town of Wynne, Arkansas was leveled when the same parent supercell regrouped to spawn another long-track EF3 tornado that stayed on the ground for over an hour and carved a track of desolation all the way to Tennessee. For a number of reasons, a comprehensive yet accessible terrain model would be a worthwhile tool for improving tornado forecasts, and while it wouldn’t prevent tragedy outright, it could certainly help to warn people in harm’s way.

An Ode to Dark Sky

This week, the final curtains are closing on one of my absolute favorite online resources for weather data, Dark Sky. A startup originally founded to provide high-resolution rain forecasts for customers like airlines and event coordinators, Dark Sky had created a number of tools that were readily available for other app developers and weather enthusiasts.  The most remarkable of these, in my opinion, was a worldwide map interface that would display highly detailed colormaps of temperature, humidity, precipitation, wind, and other meteorological variables.  Their data accounted for topography more rigorously than any other resource I had seen, adjusting temperature and pressure for elevation differences, even estimating wind channeling effects over complex terrain.  Individual point values within these datasets were made available for use in other programs through the Dark Sky API. After the company was bought by Apple Weather in 2020, these free resources were no longer in the interests of the ownership and were gradually phased out.  While the map interface has long since disappeared from their site, the Dark Sky API has lasted until its scheduled termination date of March 31^st, after which developers must update their code to another source because this data will disappear for good.

I have some history with Dark Sky: in late 2019/early 2020, I was very interested in collaborating with (or even working for) the company, having several calls with team members over the course of a few months.  I saw their terrain heatmap as an ideal base layer for my tornado prediction model and thought that my tornado risk data (instantaneous and eventually aggregate) could be beautifully displayed with the same mapping interface.  I exchanged some code and sample data with a project manager.  In our conversations, the developers behind Dark Sky repeatedly gave me the disclaimer that their algorithms were not grounded in meteorology or physics, emphasizing that they were simply programmers using purely numerical methods. As I interviewed for the title of Data Engineer, I proposed running some of the same analyses on forecast validation that I had applied to my tornado probability model, knowing that it takes years of testing and documentation to be accepted by meteorologists as a dependable predictive tool. Ultimately our conversations wound down as Dark Sky finalized their sale to Apple, a deal that immediately constrained what these guys could share or work on outside of their core products.

It’s a little saddening to look back on what could have been – instead of merging my spatial predictive algorithms for severe weather with the sophisticated mapping and testing interface of Dark Sky, I began working in Texas shortly thereafter and all but abandoned my efforts on the project. I am grateful for their interest in my work, and I completely understand their decision to take the multimillion-dollar opportunity with Apple. After all, a corporate buyout is the end goal for most tech startups, and private weather analytics is an increasingly lucrative industry valued at over 17 billion dollars. However, as the market becomes saturated with products from the big players like Apple, Accuweather, Baron, and IBM (parent of The Weather Company), it has become more challenging for individual developers to break through without IP challenges. Coupled with the lobbying efforts that have weakened NOAA in favor of public-private partnerships, I worry that this general trend will have a negative impact on the amount of scientifically useful information that will be accessible for independent developers and scientists going forward.

First Nor’easter as a Northeasterner

I know that a certain groundhog said that spring is around the corner…but first, the most significant winter storm of the year is hitting the northeast. At the end of a warmer-than-usual winter season that has only brought about 2 inches of snow to New York City and 4 inches of snow to Boston, this storm is likely to double both of those totals while dumping up to a couple feet of wet snow on northern and western parts of New England. When I was a kid in Maine, snow totals like these were common within storms from any which direction, so no Nor’easter stands out in my memory. I’m watching this one from my front window, enjoying the serene sight of snow falling (mostly in a sideways manner) with an eye on my laptop as forecasts evolve.

Winter storm forecasts are fascinating to me because there’s such uncertainty, especially when conditions are close to the freezing line. While a Nor’easter was in the forecast more than a week in advance, snow predictions for my location ranged from nothing to over a foot (though always with a wait-and-see disclaimer) depending on temperature and the path of the low. The temperature stayed above freezing in my location throughout the storm, but somehow the meteorologists predicted spot on that the rain would change to snow around 1pm. The low crossed the base of Cape Cod and passed near my south shore location, so the characteristic northeasterly wind gusts only lasted a short time before ceding to lighter, variable winds. The snow slowed after the first hour, and I expect only 1-3 inches of light accumulation when the storm moves away tomorrow morning – luckily for the Boston area, this storm didn’t quite live up to the hype. Where I’ll be skiing in Vermont this weekend has received 2 feet and counting, though, which I’m pretty stoked about!

I haven’t lived in Massachusetts for long, but there is a very clear ‘snow line’ that generally defines winter weather here. The Atlantic Ocean regulates the temperature near the coast in the 30s throughout most of the winter months, which means we get some snow but arctic blasts are very infrequent (one cold snap this February set records as the first time in decades that Boston fell below 0 ^oF). However, a combination of the Appalachian mountain ranges and inland jet stream effects tend to leave areas north and west of a line between Lowell, Worcester, and Springfield with significantly more snow (that stays on the ground longer, too) than I will get just south of Boston. Unless there’s a Nor’easter with slightly colder conditions, as there was in 2018, then everybody on the eastern seaboard can get a blizzard. I will continue to be interested in the weather here in New England, which has interesting local differences posed by mountain ranges, coniferous and deciduous forests, lakes and oceans that contribute to a surprising variety in the climate.

Average yearly snow totals from 1981-2010, NOAA.