I figured out a way to to plot the probability of good visibility of the solar eclipse. Define a function that can get the data for cloudiness over a large time span 2000 - 2015 taking US county name or a geo location as an argument:

ccd[c_] := WeatherData[c, "CloudCoverFraction", {{2000, 1, 1}, {2015, 1, 1}, All}]

I run it over all county names and fine the mean value:

seccd = ParallelMap[ccd, usco];
ccm[ts_] := Mean[DeleteMissing[Normal[ts], 1, Infinity][[All, 2]]];
avcc = ParallelMap[ccm, seccd] /. {_Mean -> .5, 0 -> .5};

Note DeleteMissing needed due to not reliable weather station services. When we did not have any data for a county we substituted 0.5 value for the average, - I bet there are better ways of estimation. Now we again use the same technique:

GeoRegionValuePlot[Thread[usco -> avcc], ColorFunction -> "Rainbow"]

And here trying to see the influence of the Earth surface topology:

GeoRegionValuePlot[Thread[usco -> avcc],  ColorFunction -> "Rainbow", 
GeoBackground -> "ReliefMap",  PlotStyle -> Opacity[.7]]

For anyone looking for more resources ahead of the April 8, 2024, eclipse (especially Wolfram Language resources for computing and analyzing eclipses), check out Stephen Wolfram's new book "Predicting the Eclipse: A Multimillennium Tale of Computation". You get a copy on Amazon: https://www.amazon.com/Predicting-Eclipse-Multimillennium-Tale-Computation/dp/1579550878