Ordinarily, calculating the center of mass from a mass density (or the center of charge from a charge density, or the center of...) is very straightforward - simply integrate the position vector over the entire domain, weighted by the density function (and then normalize by dividing by the total mass).
But how does this work when calculating the center of mass of a distribution which exists on the surface of a 2-sphere? The normal procedure seems to rely on the ability to meaningfully add vectors together, which does not work on a sphere (or indeed in any other curved space). How do you calculate center of mass in such a case? Is the concept even well-defined?
That was the question - the following is merely context:
The particular case I am faced with is a field of precipitation data, given as a function of latitude and longitude (actually a discrete dataset and not a continuous function, of course). I am trying to calculate the 'center of mass' of such a dataset as required for the calculation of a certain quality measure of weather forecasts. Unfortunately, the paper describing the measure does not go into any details on the subtleties of center of mass on a sphere - they seem to mainly consider small domains and neglect the curvature of the Earth entirely. But I would like to extend the quality measure to larger domains, if possible.
(Please note that I am not talking about the rather more trivial matter of finding the center of mass in ${\rm I\!R^3}$ of the points on the sphere - unless, of course, it should happen that the projection of that point onto the sphere is also equivalent to the center of mass on the sphere. But it is not obvious to me that this should be the case.)