Predictability of a Nested Limited-Area Model

Abstract

This note investigates the nature of the extended predictability commonly attributed to high-resolution limited-area models (LAM) nested with low-resolution data at their lateral boundaries. LAM simulations are performed with two different sets of initial, nesting, and verification data: one is a set of regional objective analyses and the other is a synthetic high-resolution model-generated dataset. The simulation differences (equivalent to forecast errors in an operational framework) are studied in terms of their horizontal scale distribution normalized by the natural variability in each scale, as a measure of predictability, which constitutes an original contribution of this note. The results suggest that the extended predictability in LAM is confined to those scales that are present both in the initial condition and lateral boundary conditions (LBCs). No evidence is found for extended predictability of scales that are not forced through the LBCs. Instead, these smaller scales exhibit predictive timescales in direct relation to their spatial scales, similar to the behavior in autonomous global models.