Real-time weather data

How Local Weather Data in Real-time Can Eliminate Negative Outcomes From Inclement Weather

Nearly every person alive today has had to alter their plans and/or schedule due to an inaccurate or outdated weather forecast. Although weather forecasting has been attempted for millennia, in the modern age of advanced technology and mathematical modeling, we still experience the effects of inaccurate weather forecasting on a daily basis. In this article we’ll discuss some of the problems that weather inaccuracies can cause, and outline the most important ways that real-time data can address these issues. While weather will always be inherently unstable, our ability to gather actionable data about changing weather phenomena can have a real effect on how we deal with inclement weather.

Weather Forecasting: Where it is currently

In order to generate predictions about the weather, forecasters utilize computer-driven mathematical models. By plugging known information into a, hopefully, accurate model, forecasters are able to provide general guidance as to what they think the weather will be over a given period in the future.  Weather forecasting technology has advanced rapidly with the computer age. Weather forecasters can now utilize data from a myriad of points, including fixed weather stations and satellites, in order to more accurately predict weather patterns.  Additionally, weather forecasting algorithms and mathematical models have become more complex over time, and are able to incorporate a large number of data points when generating a prediction.

While advances in weather forecasting are abundant, there remain significant drawbacks to how weather data is currently gathered and acted upon. The first major drawback is that weather forecasting requires predicting the future based upon a set of probable outcomes. Weather forecasters take data from the near-past to create a prediction of what the weather will be like in the future. This approach is inherently inaccurate for a number of reasons. The first is that weather can change in an instant.  Weather itself is created through instabilities in the atmosphere or local conditions. These instabilities can change faster or slower than prediction models can account for. The second weakness of traditional weather forecasting is that the data used to generate forecasts is primarily drawn from either static sensors or satellite imagery. Although satellites have been used for weather forecasting since the 1960’s, many are still limited from gathering usable data closer than 50m from the Earth’s surface. Weather stations themselves can collect a large amount of useful data from one particular point, but can only accurately depict conditions at that particular location. Weather forecasters must rely on a network of weather stations to offer a prediction of weather patterns over a larger area.

Essentially, these two drawbacks combined require that meteorologists fill in the technological gaps left by fixed weather stations and orbiting weather satellites. Despite these limitations, weather forecasting is very useful for offering predictions of large-scale weather trends and patterns over a longer period of time. However, these predictions begin to suffer at the local and hyper-local level, particularly when rapid instability occurs and changes happen faster than weather models can predict.

Weather Inaccuracies and their Impact

When inaccurate weather forecasts occur, they can have a profound impact on the daily lives of millions of people. Additionally, inaccurate weather forecasts can cause immense amounts of damage to the local and national economy, as the transit of goods and services are hindered by unforeseen inclement weather.

The most noticeable impact of an inaccurate weather forecast can be seen on the roadways. For commuters, an unexpectedly heavy snowfall can create dangerous weather conditions and exponentially longer commutes. Given an accurate forecast, many people who live in areas with snow or heavy rain would allow extra time for their commute, or arrange to work from home if possible. An inaccurate weather forecast results in more people on the roads during rush hour, which often creates gridlock traffic with increased accidents.

For local municipalities, inaccurate weather forecasting creates even more problems. State and local Department of Transportation crews rely on accurate weather predictions to determine when and where to clear roads of snow and ice. An inaccurate weather forecast can result in the failure to proactively clear snow and ice from roads, or cause problems when determining which roads need to be cleared first. Additionally, inaccurate weather forecasting can cause problems for EMS crews. Fire departments will typically schedule extra personnel in anticipation of particularly heavy snow or rain, and can be understaffed and overtaxed if not fully prepared. In summary, inaccurate weather forecasts can severely overburden the local infrastructure in place to deal with harsh weather and it’s accompanying effects.

While everyday citizens and local infrastructure can experience the negative impact of inaccurate weather forecasting, local and regional business can be seriously impacted as well. Roadways and airports are the hubs through which the vast majority of goods are transported. Inaccurate weather forecasts can cause both of these networks to stall or even completely shutdown. Both air and road transportation networks are extremely susceptible to poor weather. In bad weather, roadways can quickly become stalled or shutdown due to accidents or simply the inability of vehicles to travel at the minimum speed limit. Problems on one major highway can quickly ripple out to adjacent roadways and cities, causing even more problems for businesses that rely on the transportation of goods. Inaccurate weather forecasts for airports can cause even more profound problems, the effects of which can sometimes be seen on a national scope. Weather on the ground can force flights to be rerouted if they are in the air, or ground scheduled flights due to safety concerns. While these delays can be extremely inconvenient for travellers, for transportation businesses that utilize air transport, delays caused by weather can have a significant impact on their bottom line.

These extreme examples of the effects of inaccurate weather forecasting help illustrate some of the most significant ways our daily lives are intertwined with our local and regional weather. However, there are many more mundane, yet still important, ways that inaccurate weather forecasting can have an impact. For example, changes in local weather patterns commonly cause delayed or inaccurate delivery times for goods. This can be frustrating for both the distributor and the client.

Local and regional weather patterns are inexorably intertwined with our daily lives. From the length of our commute to the availability of fuel at the pump or food on the grocery store shelves, local weather has an impact on businesses and people every day. Now that we have examined some of the ways that inaccurate weather forecasting can cause problems for us, in the next section we’ll outline some of the strategies being implemented that can help minimize or even eliminate negative outcomes from inclement weather.

Local Weather Data in Real-Time

As we touched on briefly, some of the most significant drawbacks to current weather forecasting technology are the inherent gaps left by contemporary data gathering tools. These gaps have begun to be filled through the utilization of Internet of Things (IoT) technology applied to weather.

IoT technology utilizes disparate nodes of sensors and tools to create an expansive and flexible data gathering network. One of the primary ways that IoT has been applied to weather has been by taking the concept of a fixed weather station, and making it mobile and portable. By doing so, weather station information can be gathered from many points on the ground, and relay that information back to a data dashboard to be analyzed and acted upon. For example, current IoT technology allows a handheld weather station to be affixed to a vehicle. In a fleet application, this creates a mobile network of nodes to gather and transmit weather station information in real-time. Alternatively, weather station instruments can be placed at certain points along busy or high traffic routes.

The form factor of handheld weather stations allows for their flexible application in a variety of different industries or fields. Local municipal governments can use this technology to create a broad network of data gathering tools, giving them access to information drawn from weather station instruments across tens or even hundreds of different points. Companies that specialize in transportation and delivery can attach these devices to vehicles, and gain access to an unprecedented amount of data concerning not only weather, but also road conditions.

The most important aspect of IoT driven weather systems is that they generate real-time data that is relevant and actionable. IoT weather station instruments placed on vehicles relay meteorological and road condition data such as: barometric pressure, humidity, ambient temperature and road temperature, as well as the speed and trajectory of the vehicle, and rate of windshield wiper movement. This data is then constantly transmitted back to a central cloud location where it is analyzed, filtered, and rendered into a visual graph or data table. IoT technology allows mobile weather station instruments to provide hyper-local road and weather conditions that can be quickly accessed and acted upon. Additionally, the size of the network of sensors can be expanded to as large as is necessary for the application. This can create a broad base of nodes providing weather station information over a large area, giving traffic managers a complete view of conditions on the ground.

Customized Alerts Systems

IoT-driven weather data collection platforms are also ideal due to their cloud-based design. Because information from weather station instruments is uploaded in real-time to a cloud location and then pushed to a data dashboard, administrators and managers can access this data from anywhere using their mobile device or laptop. This means that they are no longer tied to a central location, but rather can also deploy in the field while remaining confident they will have the information they need to react to conditions as they arise. Additionally, with an IoT setup, managers can easily configure weather mobile alert systems. Once a threshold is met, weather mobile alerts can be sent to the mobile devices of users connected to the network or to digital road signs. This can be particularly crucial for drivers, giving them the information they need to react to road and weather conditions ahead of them.

Weather mobile alerts can also allow local city infrastructure to proactively address deteriorating road conditions. Snow plows or work crews can be quickly and efficiently dispatched to trouble spots before they become worse, potentially saving both money and lives by keeping roads at safe operating conditions. Weather mobile alerts can help ease the impact that rapidly changing weather conditions have for both drivers and the infrastructure that supports them. They can allow agencies to more accurately and efficiently distribute resources to the locations that need them most, based on accurate real-time data. This system will help replace past efforts by agencies to examine weather forecasts and guess where and how they should deploy their resources most effectively. Additionally, for companies that specialize in transportation and distribution, weather mobile alerts allow drivers to react swiftly to changing conditions on the ground. In doing so, they can maintain accurate delivery schedules and meet their goals.

Utilizing IoT technology in weather applications will allow industries that rely on transportation, as well as the infrastructure that supports them, to respond effectively to weather phenomena. In particular, IoT technology is being used to create broad networks of mobile, fixed, and handheld weather stations to more accurately collect and act upon weather station information. These efforts save not only money, but lives, by creating safer roads and driving conditions. Real-time data collection in weather applications closes the gaps left by old weather forecasting techniques. While traditional weather forecasting is effective for predicting broad weather trends, it fails to accurately predict the local and hyper-local weather conditions. These conditions can arise at a moment’s notice, and often vary locally and regionally depending on microclimates. Effectively addressing these shortcomings requires utilizing tools that can collect and accurately render hyper-local weather data in real-time.

Additionally, in the digital age, critical weather information needs to be accessible via mobile devices to allow users to stay up-to-date on changing conditions. IoT technology creates the platform whereby these requirements can be met. Above all, IoT technology is flexible enough to be utilized in a number of different industries and locations, and creates a network that can be easily expanded as the need grows. Eventually, handheld weather stations will relegate many of the existing problems caused by inclement weather to be a thing of the past, as both individuals and organizations can effectively adapt and change as quickly as the weather does.

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The Fathym Forecaster is a robust, feature-rich API that offers a powerful suite of weather forecasting and open-source data visualization tools. The Forecaster combines the world’s best weather forecasts with statistics-based, machine-learning techniques to tackle the largest datasets, including road weather.
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