Large (>22 trillion items) geospatial dataset with rapid (<1s) read query performance
How up-do-date do your read queries need to be?

You could partition the database by time if the map just needs to show the most recent measurement.  This would reduce your query load for the map.

For the history of a given point, you could hold a second store by x and y showing the history.  This could be done with a nightly refresh/update as the the historical data won't change.

Then you could pre-compute averages at more coarse resolutions for integrating with maps at different zoom levels.  This would reduce the number of points to retrieve for large map areas (zoom out).  Finer resolutions would be used for more zoomed in maps which were querying smaller areas.  If you really need to speed this up you could compute tiles as blobs and interpret them in your application.

Because these would involve some re-computing of aggregate information there would be some latency in query results.  Depending on how much latency was acceptable you could use this sort of approach to optimise your reads.

OK, so your points need to be computed averages over time.  With this computation I guess your actual queries come down quite a lot from 22 trillion items as the raster values can be pre-calculated for querying.