Long time no see! I’ve been busy settling into my new position, but I’m back with a fresh article on Medium. 🚀
When working with weather data, ensuring data quality is not just a nice-to-have—it’s a necessity. Weather forecasts, decision-support tools, and climate analyses all rely on accurate measurements. But what happens when a sensor malfunctions, a station is installed incorrectly, or—believe it or not—a bird decides to nest in a rain collector? 🐦
At Sencrop, the network of weather stations fuels a variety of downstream processes, from simple aggregations to complex agricultural decision-making tools. Without robust anomaly detection, these processes could be thrown off by faulty measurements, leading to inaccurate insights.
In this article, I explore:
- Why data quality control matters in meteorology
- Common data quality control methods and why they are not adapted to our case
- How we implement an inovative anomaly detection to keep our data reliable
If you’re curious about how to keep your data clean and meaningful, check it out here: Sencrop’s data quality control: Beyond the Z-score
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