As humans, our ability to recognize, interpret and predict weather patterns has only been effective for the last half-century. Thermometers and barometers have been utilized since the 18th and 19th centuries, but even then, weather forecasting was limited by communication and a large-scale understanding of weather patterns.
Thanks to technology breakthroughs in the mid-late 20th century, scientists were equipped with state-of-the-art tools to produce accurate and reliable weather forecasting.
Here, we'll examine some of the pillars of modern weather forecasting, including satellite imagery, sensors, data processing, communication, and the Internet of Things.
Satellite Weather Monitoring
The invention of satellite imagery has allowed scientists and meteorologists to track and understand large-scale weather patterns like never before. Satellite imagery has advanced far from its origination -- which consisted of low-resolution static imagery – to offer many other data formats and produce infinitely better imagery.
For example, the United States GOES-16 weather satellite can scan the Western Hemisphere every 15 minutes, the Continental U.S. every 5 minutes, and areas of severe weather every 30-60 seconds.
The satellite can provide near-real-time hydrologic, oceanic, climatic, solar, and space data. It can also map lighting locations, a first-of-its-kind technology. Data collected by satellites enables weather forecasting for areas both regionally and globally and can combine with weather data from other data sources to predict weather patterns many days in advance.
Satellite weather monitoring provides the most comprehensive picture of weather patterns, giving insight into 3-10 day weather forecasts. However, it's less effective at delivering precise local weather data for forecasting purposes.
Radar Sensors Used to Collect Weather Data
One of the most globally adapted weather data collection tools is Doppler radar, which can collect the location of particles in the atmosphere, such as rain, snow, and clouds, while indicating those particles' movement, direction, and speed.
Radar succeeds at providing mid-resolution data on a regional level, showing a 3-dimensional model of the atmosphere every 6-7 minutes. In the United States, 155 WSR-88D Doppler radar stations work together to provide near-real-time weather pattern data on a national level.
This data forms a collective series of weather snapshots to help establish patterns. Subsequently, these patterns can forecast weather within a 1-3 day timeframe.
Personal Weather Stations
High-resolution, real-time local weather data is best achieved with IoT-based personal weather stations (PWS). These stations generally consist of temperature, humidity, pressure, rainfall, and wind sensors.
While these personal weather stations cannot independently forecast weather, they can come together as a data collection network to form short-term weather prediction models based on high-resolution, real-time weather patterns.
Organizations such as Weather Underground provide a large, connected network platform for PWS owners to connect with. It's just one example of an IoT-based weather monitoring system. This data provides real-time, ultra-high-accuracy weather information that's not realizable with satellite or radar technology.
Weather Forecasting Using IoT - Everything is Connected
As described above, varying scales of weather-monitoring technologies exist, ranging from global to highly local, each with significantly different advantages over the other.
Modern weather forecasting uses these data sources to create fully aggregated computational forecasting models to forecast the weather accurately.
So, in a sense, satellite imagery and sensor data, Doppler radar, and PWS's all work together to form a weather-forecasting-focused Internet of Things. Using big data and artificial intelligence models, it's possible to understand this data in ways that have been historically impossible without modern technology.
Article Contribution By: Zach Wendt