Forecasting length-of-day using numerical weather prediction models

Abstract

A new approach to forecasting changes in length-of-day (δl.o.d) with lead times from one to ten days is examined. The approach is based on the high correlation that has been shown to exist between high frequency changes in l.o.d. and those in the atmosphere's angular momentum (M). Because forecasts of tropospheric values of M can be calculated from the zonal wind fields produced by operational numerical weather prediction models, it seems worth investigating whether these forecasts are sufficiently skillful to use to infer the evolution of δl.o.d. Here, we examine the quality of M forecasts made by the Medium Range Forecast (MRF) model of the U.S. National Meteorological Center (NMC). By comparing these forecasts against those based on a simple model of persistence, we find that skillful forecasts of M are being achieved on average by the MRF, although there has been much month-to-month variability in forecast quality. Overall, our results indicate that for prediction lead times of 1–10 days, dynamically-based forecasts of δl.o.d. represent a viable alternative to the empirical approaches currently in use.