Interpolation

Interpolation

Interpolation:
Interpolation is the process of using known data values to estimate unknown data values. Various interpolation techniques are often used in the atmospheric sciences. One of the simplest methods, linear interpolation, requires knowledge of two points and the constant rate of change between them. With this information, you may interpolate values anywhere between those two points. More sophisticated interpolations are also available in the Data Library. They are often applied to station datasets with irregular spacing between stations. The Cressman and Weaver analysis interpolation techniques are covered in this tutorial section. Both methods are primarily used to estimate equally-spaced latitude / longitude grid data from station data or gridded data with non-constant spacing.

Linear Interpolation

Linear interpolation is a simple technique used to estimate unknown values that lie between known values. The concept of linear interpolation relies on the assumption that the rate of change between the known values is constant and can be calculated from these values using a simple slope formula. Then, an unknown value between the two known points can be calculated using one of the points and the rate of change. Linear interpolation is a relatively straightforward method, but is often not sophisticated enough to effectively interpolate station data to an even grid. Linear interpolation is often used to regrid evenly-spaced data, such as longitude / latitude gridded data, to a higher or lower resolution.