Predicting the weather has always been about collecting and analyzing data. Long before we had satellites and electronic devices, sensors were as simple as weathervanes and udometers. Way back when, you could tell a storm was on its way with a limited amount of data. But you could not tell when it would arrive or how severe it would be.
It is only as our sensor technology has matured that we have been able to improve our weather forecasts. Still, there is plenty of room for improvement. Thus the National Oceanic and Atmospheric Administration (NOAA) recently awarded a $96.9 million contract to Ball Aerospace for developing a new satellite.
The satellite will pick up where older satellites leave off in 2024. It will be used to measure everything from solar winds to solar storm activity. The data it collects will play an integral role in forecasting the weather here on planet Earth.
Sensors as Data Receptacles
The NOAA’s eventual Space Weather Follow On (SWFO) satellite will be equipped with a range of sensors, each with its own function. According to California’s Rock West Solutions, a sensor is any device capable of receiving some sort of signal and doing something with it.
For example, the GPS system built into your smartphone utilizes a sensor capable of communicating with a GPS satellite. As the two communicate, a triangulation equation is run to determine your exact location on the planet. Sensors perform similar duties for weather satellites.
Likewise, your television is equipped with a sensor capable of picking up a signal sent by the corresponding remote. That signal can direct the television to raise or lower the volume, change channels, or power off.
Signal Processing and Data Analysis
The easy part of sensor technology is engineering the sensors themselves. It is harder for designers to come up with signal processing and data analysis tools for making sense of the information collected. The more complex the data, the more complicated signal processing and data analysis becomes.
As far as weather forecasting is concerned, accuracy relies a lot on the accuracy of data. For example, you have probably seen the spaghetti models that accompany hurricane forecasts every summer. Those models are based on years of historical data showing the track of previous hurricanes.
Effective analysis relies on knowing how past storms behaved. When data from past storms is compared to data currently being collected by hurricane hunting aircraft, complex data analysis can help predict how the storm at hand will behave.
Data Improves with Time
Sticking with the hurricane concept, it is clear to see how data has improved over time. In just the last 30 years, our hurricane predicting capabilities have drastically improved. And it’s all because of better data being collected by better sensors and analyzed by better software.
The NOAA relies on a ton of historical data to make its short-term storm predictions. As long as data collection and analysis continue to improve, their data will also improve over time. This will theoretically lead to more accurate weather forecasts in the future.
We may never reach the point at which weather forecasters can tell us the exact minute a storm will start and stop. We may never be able to accurately predict the path of a hurricane within a couple of miles. Then again, such accurate forecasts are not beyond the realm of possibility.
If we can continue building better sensors and signal processing software, there is no telling what we can do with weather forecasting. Who knows? The weatherman may one day be right every time.
