Why Meteorological Models Are Not Always Accurate, and How We Solve It
At Meteum, we push the boundaries of weather forecasting accuracy. Our algorithms, powered by machine learning, receive data from various sources, from weather radars to meteorological models. While the former is a simple tool to comprehend and work with, the latter requires a lot more tinkering. When discussing Fortran, we mentioned that meteorological models take some work to output high-quality forecasts.
Here’s an abstract about one of the models we use in case you missed it:
Take WRF, an open-source weather model. You can go to WRF’s GitHub repository and download the entire thing to your Linux machine right now, but be warned: this model’s out-of-the-box experience is not exactly a walk in the park. After scouring the manuals, you’ll discover that the WRF build relies on Perl and several UNIX utilities. A C compiler is needed to compile programs and libraries in the tools and external directories. The WRF source code, however, is primarily standard 90s-era Fortran with a few minor additions.
Even if you manage to run the model on your computer, you might find yourself dumbstruck by how inaccurate the WRF’s predictions may turn out. That’s because reliably predicting the behavior of something as frantic as the Earth’s atmosphere warrants an intricate preliminary setup of the model.
For researchers, WRF can produce simulations based on actual atmospheric conditions (i.e., from observations and analyses) or idealized conditions. WRF offers operational forecasting as a flexible and computationally-efficient platform while reflecting recent advances in physics, numerics, and data assimilation contributed by developers from the expansive research community. WRF is currently in operational use at meteorological centers as well as in real-time forecasting configurations at laboratories, universities, and companies.
Just like with any other meteorological model, WRF’s underlying numerical algorithms have been developing for decades. They’re incredibly sophisticated and constantly receive updates and refinements. Under the hood, every model follows a similar logic, but the interpretations of the fundamental formulas often differ.
Furthermore, the quality and quantity of weather data sources play a major role in the accuracy of the output. Models tend to use different sources, not to mention that Earth has no shortage of remote areas like oceans, small islands, mountaintops, rainforests, and deserts with minimal radar coverage. That’s where the magic happens: models begin improvising and attempt to fill in the blanks. Naturally, this approach doesn’t always yield accurate results.
So, Which Weather Model Is Better?
All the big weather models may use slightly different representations of mathematical formulas, but inherently, they are doing the same thing: predicting physical processes with an accuracy that harshly drops off when you peek further than a couple of days into the future.
But why do different models predict different outcomes? you may ask. There are many reasons. Firstly, each model assimilates weather observations differently which leads to slightly different starting points. The atmosphere is a chaotic system, so small differences in the initial state grow to larger differences when we step the equations forward in time. The models contain different formulations of the mathematical equations and make different assumptions. They handle the processes that occur within grid points differently. Some of the physical processes that take place at scales smaller than the grid spacing include cloud processes, turbulence, and solar and terrestrial radiation.
While setting up the model is essential to get the most out of it, a raw output is nothing to scoff at and provides a pretty robust general idea of the weather for the next few days: it’s just not perfect. Meteum’s AI algorithms need truly top-shelf data to preserve the consistently excellent forecasting accuracy we’re known for while also constantly improving quality.
Where Machine Learning Comes Into Play
When weather models first appeared in the 1980s and 1990s, the AI and Machine Learning fields were still in their infancy. Conceptually, meteorological models are simple programs: they perform calculations but can’t learn from their mistakes. To overcome this, our engineers at Meteum have developed groundbreaking AI algorithms that are constantly fed forecasts from weather models and data sourced directly from radars, stations, satellites, and radiosondes.
Our algorithms generate nowcasts based on numerical predictions, so we correct them on the fly based on live weather data. User reports submitted through the mobile app add another layer of clarifying data critical for Meteum to upkeep its hallmark accuracy.
We use only the most reliable data sources and refine our forecasts with AI and crowdsourced user reports. We offer an excellent API service that gives you unhindered access to hundreds of meteorological parameters.
Reap all the benefits of advanced machine learning and propel your business with Meteum, a cutting-edge weather forecasting platform. Come over to meteum.io to explore our solutions in more detail, and claim your free API key to get started!
