This is querying a model rather than real observations, so it may more reflect the model's understanding of the world. Still useful; just worth noting I think.
The data is output from a climate model run that is constrained to match weather observations.
So where we have lots of weather observations, ERA5 should match it closely.
And where we do not have any weather observations, ERA5 will be physically consistent and should match the climatology, i.e. the simulated weather’s statistics should match reality.
ERA5 covers the entire globe at 0.25 degree resolution, and stretches back in time to 1940 with hourly resolution.
What I'd like to see in such tools are two things:
* Indications of actual observed data points (location time) .. so that the observed point mesh for a day in 1949 about the globe can be seen, and
* Interpolation for a modern day (with dense observed data points) from a reduced set of sparse points (just the ones, say, from that day in 1949) to get a sense of how the infill from sparse points matches the observed but unused points.
I'm not a climate skeptic in the least, global estimation of a coarse global mean from relatively sparse points is fine in my experience (I've done a lot of exploration geophysics for mineral | energy resource mapping) and a bit of interpolated wiggle in the model doesn't blow the big corase estimation .. but this question of interpolation from sparse data is one that always gets raised by skeptics.
Totally agree that it would be cool to see when and where the weather observations are that are informing the climate model. A quick search didn't turn up this dataset, but it may be somewhere. I'll see if I can find this data.