Evaluation of interpolation methods and boundary data interval resolution for the Lateral Boundary Conditions of Regional Climate Models

Abstract: A Regional Climate Model (RCM) is a comprehensive tool to simulate high- resolution climatic factors. A RCM is driven by low resolution data obtained from a Global Climate Model (GCM). In the one-way nested method, is the GCM data fed into the RCM as a Lateral Boundary Condition (LBC) in certain updates in time, the boundary data interval resolution. The necessary information in between these updates is obtained by using linear interpolation techniques. The ability to reproduce high-resolution RCM output with low-resolution GCM data depends on the accuracy of this LBC. This thesis investigates whether third order interpolation methods lead to a more accurate approximation than the linear method. This is investigated in combination with lowering the boundary data interval resolution. The conclusion is that a third order interpolation method does not lead to a more accurate approximation for a high boundary data interval resolution. But when the resolution is lowered, the linear interpolation method looses its accuracy earlier than the third order method. This results in a boundary data interval resolution of 1.5 hours for the linear method compared to 7.5 hours for the third order method. Implementing a lower boundary data interval resolution in combination with the third order method lead to significant gain in computational time and storage for the RCMs.