Smart neighbour

The smart neighbour approach picks a nearby gridpoint from the coarse grid that has similar characteristics to the lookup point. The nearest neighbour may for example not be at the same elevation as the lookup point.

The method requires a search radius. The larger the search radius, the more likely it is to find a similar location. However, picking a far away point can be problematic if there are horizontal gradients in the field.

If the nearest neighbour is very close to the correct elevation, it makes sense to pick this one, even if an even more exact location further away is found. This can be set using the minElevDiff option, which specifies how large the elevation difference must be before the nearest neighbour is not to be used.

In many application, smoothing the field spatially can be beneficial. This can also be done with this method by allowing the average to be taken of several similar points. The numSmart option specified how many smart neighbours to average. If 5 is chosen, then the best 5 neighbours will be averaged.

The method is relevant for temperature, wind speed, and precipitation.