on September 19, 2023

How to specify a certain place in a (CDS) weather data query?

Software & Programming

So, You Need Weather Data for a Specific Spot? Here’s the CDS Lowdown

The Copernicus Climate Data Store (CDS)… it’s a mouthful, I know. But trust me, if you’re diving into climate data, it’s a name you’ll want to remember. Think of it as a massive warehouse overflowing with climate info – observations, forecasts, the whole shebang. It’s a key part of the Copernicus Climate Change Service (C3S), and honestly, it’s a game-changer for anyone trying to figure out how our climate is changing and what we can do about it. Now, the tricky part: getting exactly the data you need, especially when you’re after info for a specific location. Let’s break down how to pinpoint your spot in the CDS.

Getting Specific: Location, Location, Location

Here’s the deal: the CDS API is designed to give you data in neat little grids. It’s not really set up to grab info for, say, your exact backyard. But don’t worry, there are ways around this! You just have to be a little clever.

  • The Bounding Box Trick: This is the most straightforward way to do it. Basically, you draw an imaginary rectangle around the area you’re interested in, using latitude and longitude. The CDS then coughs up all the data for the grid points inside that box. Simple, right?

    • Why it’s cool: Easy to understand, works well if you’re looking at a region.
    • The catch: You’re not getting just your spot; you’re getting the whole neighborhood.

  • Gridlock: You can specify a grid, but it’s the same problem as above. It’s not a specific point.

  • Python to the Rescue: This is where things get interesting. The best way to get data for a precise location is to download a slightly bigger chunk of data and then use Python to zoom in on your spot. Libraries like xarray and dask are your friends here. They let you slice and dice the data until you’ve got exactly what you need. I remember the first time I did this – felt like I was performing digital surgery!

    • Why it rocks: Pinpoint accuracy! Plus, you get to play with Python.
    • The downside: It takes a bit more coding know-how, and you’re downloading more data than you strictly need.

    • A Few Things to Keep in Mind

    • API Quirks: The CDS API wasn’t built for pinpoint accuracy. It’s more of a “general vicinity” kind of tool. Keep that in mind when you’re crafting your queries.
    • Interpolation Station: The data itself is often smoothed out (interpolated), so even if you specify a small area, the coordinates might not be exactly where you expect them.
    • File Size Frenzy: Downloading huge areas just to grab one tiny data point can lead to massive files and might even hit the API’s limits. Be smart about how you subset your data!

    Let’s Get Practical: A Step-by-Step Example

    Okay, let’s say you want to get temperature data for a specific spot. Here’s how you’d do it using the bounding box method and a little Python magic:

  • Craft Your Request: Head over to the CDS web interface and tell it what you want. Pick your dataset (like ERA5 hourly data), the variables you need (temperature, maybe?), the time period, and, most importantly, define your area of interest using those latitude and longitude boundaries.
  • Steal the Code: The CDS interface has a handy “Show API request” button that spits out the Python code for your request. Copy that code – it’s your starting point.
  • Tweak It (If Needed): You might want to loop through multiple years or months, for example. Adjust the code to fit your exact needs.
  • Download Time: Run that Python script and grab the NetCDF or GRIB data file.
  • Python Power: Now, fire up xarray and dask to open the file and extract the data for your specific location. You’ll basically tell Python to find the grid point closest to your location and grab the data from there.

    • python

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