Earth Observation Synergy

Photo Credit: Patrick Fore

Over the last few years, the amount of data generated by consumer oriented sensors rose significantly. Everything from smart watches, thermostats, to cars and public transport are producing some form of numerical output. In the industrial world, the data flow is even stronger — every production line, robot, bit on a logistics chain generates some sort of identifier. To make sense of it all, technology companies built tools to equip each industry with their own set of solutions proselytized to churn out valuable insights.

Outside of What We Produce…

When we look outside of our machinations and into the data production of our natural world we see a fragmented collection of earth and weather observations assembled over various time frames, geographies and formats. It’s not as if Earth starts and stops production either, it is always on, always producing new and exciting indicators.

So with plenty of valuable input, why haven’t we been able to synchronize our worldly data collection? Quite simply because harmonizing such disparate types of information on a massive scale is, really hard, and despite the task being left to some of the brightest generational minds–it’s no an accident that today’s most crucial Earth observations are coming from the national space agencies (like NASA, ESA and similar)–we still run into the fundamental problem of a fragmented data landscape.

Here Comes SYNOP!

In the spirit of confluence, we’ve released weather station (synoptic) Earth observations on the Planet OS Datahub — Meteorological Observations From Regional Basic Synoptic Network (RBSN). This dataset can serve the incredibly useful function of harmonizing variables, like temperature, precipitation, wind speed, and atmospheric pressure, from reanalysis datasets like ERA5 with weather station data that serves as the gold standard for observation. Weather stations are like the smart watches of Earth, serving as a barometer for the health of the planet over a more extended period of time.

Comparing SYNOP with ERA5 Reanalysis

Due to the vast applicability of this dataset, we will also accompany SYNOP with an example of how to use it in conjunction with ERA5. Our data integration engineer and all around weather wiz, Eneli Toodu put together a very useful description of how to work with this data and made it available on our Github page. As always, please reach out if you have any questions about using the Planet OS API to acquire synoptic observation data.

In Conclusion

Predicting what the weather will be tomorrow, or where the next huge tsunami will be and its impact, or the annual precipitation for breadbaskets like the central valley in 2028 can involve looking back in time. Painting a historical narrative around temperature, sea level and other vital parameters not only influence our predictions but is indicative of the overall health of our climate and ecosystem.

In recent years, weather archivists have done their duty in compiling this data, making it available, at least in part, in the Historical Climatology Databases. Fitting together reanalysis like ERA, with synoptic observations, along with historical climatology datasets, as a backdrop, provides the basis for coordinating our attempt to synchronize the knowledge we make available, digestible and actionable for those who will build the tools of tomorrow. The Planet OS team built Datahub as a repository and access point for making sense of of the world through the data it produces and we are excited to see how people use it.

Many of the datasets made available through the Planet OS Datahub have been at the request of our users. For those who require a consolidated, easy to use, resource for accessing data not currently available on Datahub, please reach out to the team and we will work toward bringing it onboard. If you like to receive email updates when new data becomes available, subscribe to the Planet OS newsletter via the form at the bottom of our landing page!