Weatherlogics has been closely following the global impacts of COVID-19. Our business continues to operate normally, but with all our employees working remotely. We intend to continue providing critical weather information, without interruption, during this pandemic.
Given the economic consequences of this pandemic, agricultural producers are more important than ever. Farmers must continue to produce the food to feed the world, even during this difficult time. Due to the ongoing uncertainty, we are extending the early bird sale on our agriculture subscriptions by an extra week to April 7. We continue to monitor the situation for both our agricultural producers and the other sectors of the economy that we serve, should further support become necessary.
As always, please do not hesitate to contact us with any questions.
The Weatherlogics Team
Why do forecasts differ?
How come different weather forecasts sometimes differ so drastically in their predictions? This is a complex question, with multiple variables at play, but we’ll tackle the main reason in this article.
When you’re using a forecast to make crucial decisions for day-to-day activities, it is important to use a source of weather information that can be trusted. To do this, the first question that should be asked is “How is the forecast produced?”. There are three primary categories of forecasts: automated, hybrid, and meteorologist-only forecasts.
An automated forecast is derived entirely from a computer model where there is no human input. These models are known to have biases (e.g. too sunny, not handling thunderstorm activity well, too cold at night, etc). If the forecast is completely automated, these biases go uncorrected as there is no meteorologist correcting them. These forecasts are often used by agencies with limited meteorological expertise – they simply load in raw output from one or more models to produce the desired weather information. To the surprise of many, the forecast you’re reading may not have been written by a meteorologist, it could just be computer output!
A hybrid forecast is where a weather model is used as the base
forecast and then a meteorologist adjusts parts of the forecast to improve its
accuracy. Depending on the source, meteorologist intervention may be extensive,
or limited. Meteorologists are often tasked with focusing on high-impact
weather, so their time may be mostly directed at parts of the forecast with the
most impact, leaving less important aspects unadjusted. Hybrid forecasts are
the most common type of forecast produced by government and private weather
The final type of forecast is a meteorologist-only forecast. These forecasts are produced entirely by a meteorologist. The meteorologist may use weather models to help produce the forecast, but there is no ‘baseline’ forecast that is adjusted. The meteorologist composes the entire forecast each time. These types of forecasts are rare because they are very time consuming to produce.
The Role of Forecast Types
The forecast type plays a large role in why forecasts differ. Automated forecasts are produced strictly by models and these models often update four or more times per day. As a result, the forecast can change numerous times per day, often drastically, because no meteorologist is adjusting it to ensure consistency. In addition, there are many different models available. If one forecast uses the American model (called the GFS or Global Forecast System) it may be different than the Canadian model (called the GDPS or Global Deterministic Prediction System). As you get closer to the forecast date, the model’s forecasts often become similar, but not always. During complicated events, model forecasts may differ even as an event begins.
While automated forecasts are most prone to differ, hybrid forecasts can as well. Some large agencies have rolling shifts of different meteorologists, so if a new shift comes on and doesn’t like the previous forecast, it can be changed. However, when meteorologists are overseeing the forecast process, these changes are often less drastic as they can make smaller adjustments. Meteorologist-only forecasts are rarely distributed publicly, so you may not have used one. However, they are often more consistent than other forecast types since there are no underlying models that frequently change.
Every year people ask us this question: Is it true that it always rains 90 days after fog? To the meteorologist, this is an unexpected question, because nowhere does such a rule appear in any textbook. While we always inform the questioner that this isn’t true, to our knowledge nobody has ever actually studied this question. In this blog post we put the legend to the test – does it actually rain 90 days after fog?
While this seems like a fairly easy rule to test, it’s actually not so simple. For one, the 90-day “rule” varies depending on who you talk to. Some say 60 days, some say 80-90 days, some say 90-100 days, and some say something in between, so which is it? Also, what is considered rain? Most people interpret this rule to mean a significant rainstorm is coming, but what qualifies as a significant storm? Is 13 mm (0.5 in) a big storm, or 25 mm (1.0 in), or 50 mm (2.0 in)? Or does this mean any amount of rain?
To account for all the different variations of the rule, we put it to the test using a number of different criteria at three different locations on the Prairies: Winnipeg, Regina, and Calgary. We checked how often there was precipitation (rain or snow) 30, 60, 90, and 120 days after fog. We also varied the criteria of what a significant storm is, by checking how often there was precipitation exceeding five different thresholds: 0.01” (0.2 mm), ¼” (6.4 mm), ½” (12.7 mm), 1” (25.4 mm), and 2” (50.8 mm).
Let’s start by checking the results for Winnipeg. As you can see, on average there is 0.2 mm of precipitation only 33.5% of the time following a fog event. However, this percentage drops off to less than 1% for large precipitation events of 1 or 2 inches. The numbers are also quite consistent for various time ranges, with the frequency of rain only varying by about 1% regardless of whether we use 30, 60, 90, or 120 days as the “rule” for a future storm. But this is just Winnipeg, how do Regina and Calgary fare?
As you can see, the numbers are not that much different for Regina and Calgary. In fact, it appears the frequency of rain is even lower following fog in those cities. On average, Regina received at least 0.2 mm of rain only 32% of the time after fog and Calgary gets 0.2 mm only 31.9% of the time. Like Winnipeg, large amounts rarely follow fog, with both Regina and Calgary receiving 1 or 2 inches of rain 0.5% of the time or less.
As shown by the statistics above, the 90-day “rule” clearly does not work. Even if you vary the criteria, the results are very poor. But then why does this rule often seem to work? There are a few explanations we can think of:
1. Significant rains are fairly common on the Prairies.
During the growing season, most of the Prairies receives over 300 mm on average, so there’s bound to be a few significant rains in a typical year anyway.
2. There is uncertainty in the criteria.
If you mark fog on the calendar, then it rains about 90 days later, it has appeared to work. But the word “about” is the key one. If it rains 85 days later does that count, what about 95 days? The larger the window you give, the more likely it will rain by chance.
3. We only remember when it works.
If it doesn’t rain 90 days after fog, you probably don’t remember it, but when it works you do remember. We used thousands of fog events for our study – so if the “rule” works a few times, that is just pure luck.
One strange thing about this legend is its origin. We tried to find out where this came from and why, but couldn’t find anything. If you have some idea where it comes from, we’d love to hear about it.
In conclusion, the 90-day rule is most definitely a pure myth. It only seems to work because rain was likely to happen anyway. Still don’t believe us? If you’ve marked fog on your calendar consistently, we can run the stats for your location. But we need a big sample! Just showing that it worked once or twice doesn’t cut it. For our stats, we used over 2000 fog days since 1953 as a test. So we’ll need more than a few examples to be convinced!
So if the 90-day rule doesn’t work – how can you tell if it’s going to rain? Luckily, Weatherlogics prepares real weather forecasts for farmers across the Prairies – giving them a reliable heads-up about upcoming rainfall and other significant weather. Check out our agriculture page for more information!
Earlier this week we announced that Weatherlogics had launched its Climate Portal. This portal allows users to retrieve climate data, records, and normals for any location in Canada. You can see full details about the climate portal, and its capabilities, by visiting the website at https://climate.weatherlogics.com
With the launch of this portal you may be wondering why Weatherlogics chose to build it in the first place? Many people assume climate data are already available and therefore we are just duplicating what already exists. While it’s true that the underlying data are already available – they’re just that, raw, underlying data. To use the data properly requires painstaking manipulation and analysis. Our database makes these climate data much more accessible and has three critical advantages:
We will briefly describe each of these
three advantages below:
When you retrieve historical climate data for a location, you want to maximize the period of record. However, in Canada this is difficult because weather stations have changed locations and names many times. For example, if you wanted to get Winnipeg’s climate history, all weather stations would have to be identified and downloaded. Afterwards, a method to combine stations into a single dataset would have to be established. In Winnipeg, there are a total of 41 stations to choose from, but Weatherlogics has identified six as primary weather stations to combine:
St. John’s College: 1872-1938
Winnipeg Richardson Intl A:
Winnipeg Richardson AWOS: 2008-2013
Winnipeg A CS: 1996-2020
Winnipeg Intl A: 2013-2020
can see, it is not easy, given that these stations overlap in time, have
varying levels of data quality, and some only contain certain variables. For
example, Charleswood2 is mainly used for snow measurements, while Winnipeg Intl
A is mainly used for hourly weather conditions. Similar caveats apply to the
In theory, Winnipeg’s climate history
extends from 1872 to present, but only if you can properly combine the six
above stations. Luckily for you,the Weatherlogics database
has already completed the complex process of combining (or joining)
stations. Not only has this process been done for Winnipeg, it has
also been completed for more than 700 active weather stations across Canada.
The table below shows some examples of how many stations needed to be joined at some locations across Canada.
Number of Stations Joined
Thunder Bay, ON
Edmonton City, AB
Toronto Island, ON
If our discussion about completeness hasn’t already got you concerned about trying to assemble climate histories yourself, just wait, because that’s just the tip of the iceberg. Let’s assume you managed to combine all the stations together – great, you’re done…actually not so fast. Unfortunately, climate data in Canada are minimally quality-controlled (QC). Most data only undergo limited automated QC, which often misses critical errors. Furthermore, much of the historical climate data were manually input, so input errors are more common than you might think. For this reason, Weatherlogics has instituted a rigorous quality-control process which identifies erroneous values and fills missing data.
The first step in QC is checking a data point to see if it’s valid. You can see a list of all our QC methods on the methods page of the climate portal. This process identifies all sorts of errors, ranging from unrealistic values to inconsistent data. Once the QC is complete, missing data are detected. If a value is missing, we always attempt to fill it. It can be filled in a variety of ways, ranging from using hourly data as an estimate, to using another nearby station. The reason we fill missing data is because many statistics cannot be calculated if data are missing. Monthly climate values, like total precipitation, cannot be computed if even a single data point is missing.
You might think QC is not that big a deal, but actually it is. Let’s say you want to know the highest wind gust ever recorded in Estevan, SK or Kelowna, BC. In the case of Estevan, the value that might come up is 298 km/h on July 24, 2017. In Kelowna, the value that might come up is 276 km/h on June 14, 2015. In both cases, these are erroneous values caused by sensor errors. In another situation, you might be looking for the wettest month on record in Manitoba. Your search might reveal that Carberry had 409.8 mm in November 1995, making it the wettest month. However, this is actually an erroneous total, as a quick comparison with another station in Carberry shows that there was only 49.6 mm in November 1995. If you used these data for something important, your conclusions could be completely wrong. These few examples show how QC is critical to ensuring the integrity of the data. That’s why we have associated a flag with each data point. Even if we think a data point might erroneous, but aren’t entirely sure, we’ll mark it suspect, so at least you know to look into it further.
After completing the climate history and performing quality control, we have a database filled with great data. However, unless all you’re looking for is past weather observations, the data itself isn’t that useful. It is far more powerful when it can be searched for specific records or if normals can be calculated. The ability to search our database for specific information is the third critical advantage of our portal.
One spin-off of having a database with complete climate histories is that our climate records and normals are also complete. If you view climate records for Winnipeg they are often based only on Richardson Intl A, which means they will only be using data from 1938-2008. This means all records prior to 1938 and after 2008 are missing. Therefore, you aren’t actually seeing the records, because a large amount of the data are missing. The same can be said for normals. Since Richardson Intl A ends in mid-2008, the Winnipeg normals for 1981-2010 would only be from 1981-2007 if based on that station. Since Weatherlogics has the complete underlying data, our records and normals are also complete!
There are two primary ways to search the
database: through the climate portal website or using APIs. The website allows
many basic searches for records and normals. However, due to the difficulty in
customizing the user interface for all possibilities, some records and normals can
only be retrieved using APIs. The APIs allow more complex searches than can be
done with the website. Here are some examples of unique queries?
When was the longest blizzard?
What is the earliest or latest
Which month has the most 33 C days?
What is the driest first half
What was the wettest month of
What was the highest dewpoint
What was the lowest relative
humidity in April?
When were the most consecutive
days below -30 C?
These are just a few examples of literally hundreds of possible searches. Even though the APIs are quite thorough, some very complex or large queries require one of our meteorologists to write a custom script for the data you require. Contact us if you run into such a situation.
In the previous few sections we’ve outlined the main advantage of our climate database. However, there are other advantages too. Since our database is updated hourly, the latest data are always available. This also means that our 1991-2020 normals will be available immediately on January 1, 2021. You don’t even have to wait until 2020 to get the latest normals, the 1990-2019 normals can already be searched!
As part of our ongoing updates, we also store a lot of data that isn’t available anywhere else. Some examples of this include hourly precipitation, sea-level pressure, and hourly wind gusts (to name a few). If you want access to these specialized datasets, just contact us!
The sky’s the limit when it comes to possible uses of our climate database. However, a few obvious cases come to mind. Television newscasts often focus on the weather and using records or normals is a good way to put the current weather in context. Our climate records are also a great way to wow viewers with interesting stats. There are other obvious use cases too, like using our data to help inform insurance decisions, or integrating our APIs into apps. Scientists can also benefit from our quality-controlled data sets. While we’ve noted a few specific industry-based examples, our data are open to anyone, so don’t be shy, take a look today!
While climate data might seem rather benign
at first, this post has shown how tricky it can be. We’ve gone into quite a bit
of detail here about what makes our climate data unique. If you have more
questions, there’s plenty of information under the Help section of the climate
portal. You can also contact us if you need assistance.
Case Study: May 4, 2018 Destructive Southern Ontario Wind Storm
Please click the button at the end of the article to download the full case study.
A historic damaging wind event occurred in southern Ontario on 4 May 2018. The event was notable for its impacts, including downed trees and power lines and three fatalities. In some locations, these winds were the strongest ever recorded in the month of May. Wind gusts reached over 100 km/h in many areas, with a maximum measured gust of 126 km/h. The damage from this event totalled over $380 million in Ontario, making it the costliest event since the 2013 Toronto floods, according to Catastrophic Indices and Quantification Inc. The map below shows the locations of all measured wind gusts on 4 May 2018 that were at least 87 km/h.
This was a unique event because record-setting wind speeds occurred with both weak thunderstorms and no thunderstorm activity at all. In this case study we examine the meteorological mechanisms that produced the damaging winds. To view the case study, simple click the link below to visit the download page.
What Makes Our Agriculture Weather Forecasts Different?
At Weatherlogics, we strive to provide accurate, detailed, and reliable weather forecasts to the agriculture industry. We understand your frustration with traditional sources of forecasts which can be confusing and lack detail and consistency, which is why we’ve developed our own forecasting service.
At Weatherlogics, our in-house meteorologists produce our own independent, daily forecasts by utilizing various tools such as radar, satellite, surface weather observations, upper-air observations and weather models. This contrasts to many other forecasts which tend to be automated using weather models, rather than allowing trained meteorologists to produce the forecast by utilizing all available tools. While computer models are a great tool to assist in the forecasting process, relying solely on them will inevitably lead to inconsistent and inaccurate forecasts, especially for high-impact weather events. Weather models can also update up to four times per day, causing the forecast to change often, making it difficult to know what to believe.
The forecasting service provided by Weatherlogics is used by the agriculture industry in all corners of Manitoba. Some reasons why so many have chosen Weatherlogics include:
Detail – By providing more detail, our clients are fully aware of what weather is coming before it arrives. We don’t just give the forecast, we explain why the weather is behaving the way it is and also address uncertainties in the forecast.
Proven accuracy – In 2017, our temperature forecasts were 30% more accurate than the public forecasts.
Meteorologists – Our forecasts are made by real meteorologists. This reduces variability in day-to-day forecast updates and increases confidence because the forecast process is guided by experienced meteorologists.
Communication – The uncertainties in the weather forecast are communicated to provide a better idea of where the weather may differ from the forecast
Our agriculture forecasts are sent out by email 6 days a week to subscribers. Features of these forecasts include:
5-day forecasts of temperature, precipitation, cloud cover, and wind.
Maps of precipitation amounts for the next 3 days.
Maps of wind speed and temperatures.
A short-term outlook discussing the weather and its uncertainties over the next few days.
A long-range outlook discussing the pattern over the next 1-3 weeks.
Email updates when significant weather has developed or there is a note-worthy change in the forecast.
In addition to our daily weather forecasts, we also provide other services that help our clients prepare for future weather events:
Seasonal outlooks, such as our annual summer forecast.
Fully quality-controlled climate data with no missing values.
Road weather services to help our clients plan for inclement weather conditions which may affect travel.
We currently offer our subscription service only in Manitoba, however we plan to expand our service to other provinces in Canada. We can offer customized weather forecasting services for any location in Canada, just let us know what your needs are! If you live outside of Manitoba, please feel free to contact us to show your interest!
Hail series (Part 3): Forecasting hail and insurance implications
When forecasting the potential for hail, the first thing we look for is the potential for thunderstorm activity and the type of thunderstorms that are expected to develop. As mentioned in part 2 of this series, supercell thunderstorms tend to produce the largest hail and thus, there is a greater risk for hail damage when there is the potential for supercell thunderstorms. This is because supercells tend to have stronger updrafts, which are required to keep hailstones in the cloud for sufficient amounts of time for them to grow. Supercells tend to develop in a highly unstable atmosphere with adequate wind shear to separate the downdraft and updraft regions, which promotes longevity of the storm.
The depth of the melting layer is also an important factor for determining if hail will melt completely before reaching the surface. The height of the wet-bulb zero can help with forecasting the potential for hail to reach the surface in thunderstorms. The wet-bulb temperature is the temperature at which a surface has cooled due to evaporation of liquid water. Thus, the wet-bulb temperature can be an estimate of the temperature of a precipitation particle as it falls through the atmosphere. We use the wet-bulb temperature because as the particle falls, evaporation is occurring on the wet surface of the particle. Generally, a height of about 2.1 km to 3.2 km (9,000-10,5000 ft) of the wet-bulb zero correlates well with large hail at the surface (source: NOAA Glossary). In the tropics, where the melting layer is deeper, large hail is less common than in the mid-latitudes.
The presence or absence of a layer of dry air in the middle part of the atmosphere is also taken into consideration when forecasting hail. Cooling from evaporation of water and melting of hailstones in this dry layer can lower the wet-bulb zero height inside a thunderstorm cloud, decreasing the depth of the melting layer and increasing the likelihood of large hail.
Surface elevation also affects the likelihood of hail. Higher elevations, such as the Alberta Foothills, result in thinner melting layers and less time for hailstones to melt. Hail is more frequent in these regions as a result.
Finally, the amount of water vapour in the atmosphere is considered. Generally, lower amounts of water vapour combined with a highly unstable atmosphere are more favourable for large hail. A highly moist atmosphere results in more liquid being present in the storm cloud, which reduces the speed of the updraft, thereby not allowing hail to remain suspended for as long.
By considering all these factors, the meteorologists at Weatherlogics are able to produce daily hail forecasts. We identify where hail is likely to occur, and how large it could get. The image above shows one of our severe weather outlook graphics from 2017. That graphic indicates the potential for hail, in addition to damaging winds, heavy rain, and tornadoes. Each forecast graphic is accompanied by a synopsis which describes the meteorological conditions. This information helps give hail-exposed sectors advance warning of damage potential. Once a hail storm has developed, we track the storm closely and gather information about it. Our hail data can be used to verify insurance claims and identify which areas were most impacted by a storm.
Thank-you for reading this series about hail. If you’d like to continue the conversation, feel free to contact us at firstname.lastname@example.org. You can also visit our insurance page for more information about our hail data. We would love to show you how our hail data and forecasts can help you manage your weather risk.
Hail series (Part 2): Storm patterns
Viewed from the air, we can see that hail falls along paths known as hail swaths. These can be quite small – a hectare or so (a few acres) in area – or quite large, 16 kilometres (10 miles) wide by 160 kilometres (100 miles) long. Hail swaths that persist over large distances are often produced by supercell thunderstorms.
Generally, supercell thunderstorms are the most frequent hail producers and produce the largest hail sizes. Supercells are rotating thunderstorms that almost always produce hail. Supercells have the greatest potential for damaging hail because of their stronger updrafts, allowing hail stones to remain lofted inside the storm cloud for longer periods of time. They are also longer-lived storms, allowing them to cause significant hail damage over longer distances.
The radar image above shows an example of a supercell thunderstorm southwest of Brandon, Manitoba on June 23, 2007. Not only did the storm produce tornadoes, but it also produced large hail. Notice the V-shape appearance of the storm on the radar image and the hook-like feature on its southern side. These are some of the features on radar often associated with supercells. The region above the hook with the strongest radar returns (bright purples) is the most likely location of very large hail, sometimes as large as softballs.
Other thunderstorm types, such as single-cell or multi-cell storms, usually do not produce hail as large as in supercells. Single-cell storms tend to last less than an hour before fizzling out. This reduces their ability to cause widespread damage and their weaker updrafts tend to produce smaller hail, as compared with supercell thunderstorms. This is not to say that severe hail can never occur under single-cell thunderstorms, but the incidence of large hail is less frequent.
Multi-cell storms last longer because they are continually regenerated along the storm’s boundary of outflow winds. However, their updrafts are also often weaker than in supercell thunderstorms. Squall lines (pictured above), which are lines of multi-cell thunderstorms, often occur late in the day or overnight, producing damaging straight-line winds. Again, large hail can still occur in multi-cell storms but it tends to be less frequent and lesser in size.
Hail outbreaks can occur if multiple supercell thunderstorms develop along a frontal boundary (such as a cold front). Once these storms develop, large hail will begin to fall not too long after the storms develop (sometimes in as little as 30 minutes), given their intense updrafts. As the day wears on, these storms will either die out when the sun sets, or if the atmospheric conditions are right, the storms may develop into a line of storms, such as a squall line, which races eastward producing damaging straight-line winds and smaller hail.
If you’d like to continue the conversation, feel free to contact us at email@example.com. You can also visit our insurance page for more information about our hail data. We would love to show you how our hail data and forecasts can help you manage your weather risk.
In our final post of this hail series next week we will discuss how Weatherlogics forecasts hail storms and impacts of these storms on various industries.
Hail series: What is hail and where does it come from?
Hail is frozen precipitation, born of the updrafts of thunderstorms. Updrafts are rising air currents, which combined with cold temperatures and water droplets, are the primary forces that create hail. Such conditions are frequently present in the middle and upper portions of thunderstorms.
Hailstone embryos form inside storm clouds on small frozen raindrops and on “snow pellets” called graupel. They grow into true hailstones by accumulating ice through a process called accretion, by capturing supercooled (below 0 °C) water droplets in cloud regions with temperatures below the freezing point, 0 °C (32 °F). Hailstones sometimes contain pebbles, leaves, twigs, nuts, and insects that have been lofted into the clouds by strong updraft winds.
For the hail embryos to grow, they must remain in a layer of supercooled water for a length of time — the longer they stay there, the larger the size. They are kept in this layer by strong updrafts that construct the great towers of the cumulonimbus (Cb) clouds. The updrafts push the Cb cloud tops high into the atmosphere, creating the environment for lightning, thunder, and the various wind characteristics of thunderstorms.
Rising air (updraft) of at least 60 km/h (40 mph) is required to form dime-size hail. Golf-ball size stones (4.5 cm diameter) form when updrafts reach approximately 100 km/h (60 mph), and softball-size hail forms inside updrafts reaching 160 km/h (100 mph; NWS). To form hailstones the size of golf balls, over ten billion supercooled droplets must be accumulated, and thus the hailstone must remain in the storm cloud for at least 5 to 10 minutes. Once a hailstone has reached a weight which the updraft can no longer support, it falls to the ground.
Hailstones form in onion-like layers of opaque and clear ice. Clear layers form when colliding water droplets freeze slowly in cloud layers that are slightly below freezing, allowing air bubbles to escape. This process is termed “wet growth”. Opaque layers, with a “milky” texture, form when colliding water droplets freeze rapidly in cloud layers that are well-below freezing. The rapidity of the freezing prevents air bubbles from escaping. This process is termed “dry growth”.
When hailstones collide inside the thunderstorm, they may break into smaller pieces or become welded together into larger irregular shapes. Large hailstones can fall at a maximum speed of 170 km/h (105 mph).
Hailstones don’t have to travel up and down in a thunderstorm cloud however. Sometimes they drift slowly downward through the storm, accumulating water droplets and growing as they pass through supercooled water layers. These stones will have a more uniform structure, rather than layered.
Not all hailstones survive that downward trip. Forty to seventy percent of the hailstones that form within a thunderstorm cloud melt before reaching the ground.
A series of descriptor terms are used to communicate the size of hailstones, ranging from pea-sized to softball-sized. The best way to report the size of a hailstone is to measure it with a ruler or to compare it with a commonly-known object that does not vary in size, such as a coin. Marbles, although often used as a descriptor, is not a great object to compare to due to the varying sizes of marbles.
One of the largest hailstones ever documented occurred on July 23, 2010 in South Dakota. The hailstone had a diameter of 8-inches, or about 20 cm (NWS). Jim Scarlett, meteorologist in charge at the National Weather Service in Aberdeen, SD remarked “I described this one as cantaloupe-size.” The July 23 storm sent hailstones that broke through roofs, leaving fist-size holes in interior ceilings, smashing through windshields and causing at least five injuries to stranded motorists on I-90. Dents in the ground were still visible the following day.
Weatherlogics predicts the locations of future hail storms and collects data on hail events. This information is critical to ensure we have an accurate understanding of hail risk. For more information about these services, click here.
In our next article in this series on hail, we’ll be discussing the characteristics of hailstones, storm patterns associated with hail, and weather forecasting and hail damage assessments.