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Assuming spherical shape, Earth's circumference is about $40,000\mathrm{km}$. These are split into $360^\circ$, so one degree is a bit more than $100\mathrm{km}$. This is true for latitudes. For longitudes, the circles of constant latitude are shorter as you move away from the equator. Therefore you need to multiply that number by $\cos(latitude)$. To ...


4

It sounds to me like the reviewer is fixated on a 'rule' they encountered in grad school, but the rule ignores some real-world nuance (as rules do). Quick caveat: my answer isn't definitive, it's more of an opinion, but it was too long for a comment. It seems intuitively true that some phenomena just are smooth, so the resolution of the data is not the ...


2

The simplest way is to download another variable in which data is only available over the ocean, such as Sea Surface Temperature (SST). You should be able to download it in the very same way you do with precipitation (let us call it PREC hereafter). Then, you can use any software that handles this type of data (Matlab, Python, NCL, GrADS...) to mask out the ...


2

Here is a simple 'back of the napkin' idea for Python: Get a shapefile of the borders and use Python to create a polygon of the borders. Use mpl_toolkits to convert the climate model to X and Y coordinates Check to see if the model grid points are within the Polygon


2

As @gansub implicitly suggested, you may have a better outcome by defining the output grid as well. Check this very similar question: How to interpolate scattered data to a regular grid in Python? After comments, I realize I missed the irregular target grid. Maybe this answer helps: https://stackoverflow.com/questions/3242382/interpolation-over-an-...


1

The land-sea mask for ERA Interim is available here.


1

Yes, but it will not be a very good dataset. You will only be limited to the size of the dataset. You can use Python's griddata function. But of course, you will need to make your grid before using the function. Or you can use the interp2d function.


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