LONG-RANGE WEATHER FORECASTS THROUGH NUMERICAL AND EMPIRICAL-METHODS

There has been much improvement in numerical weather prediction since L.F. Richardson (1922, Weather Prediction by Numerical Process, Cambri dge University Press, Cambridge, p. 236) wrote his famous book. NWP ha s primarily been successul in improving day-by-day forecasts starting from an observed detailed Initial Condition (IC) out to about a week. The purpose of this paper is to discuss first the state of the art in long-range NWP by presenting results of a new large numerical experime nt (named DERF90; from Dynamical Extended Range Forecasting in 1990 ou t to 90 days) conducted at the National Meteorological Center (NMC) du ring the summer and autumn of 1990 (Section 2). One hundred and twenty eight 90-day global forecasts were made from successive daily initial conditions (IC), thus giving us ample opportunity to assess skill of forecasts at lead times beyond 1 week. We then move on to define the n otion of a limit of predictability (LOP), and following a procedure by Lorenz (1982), give a numerical estimate of the LOP using the DERF90 data set. We then produce a list of reasons, as to why this estimate ( LOP = 18 days) should not be taken too literally. In particular, we ar gue that the LOP varies as a function of the flow itself, and it would be (much) larger if we had, as we will ultimately, a coupled ocean-at mosphere model for making long-lead forecasts. Last, but not least, we present results of empirical forecasts that point to modest but signi ficant skill well beyond the traditional LOP (a few weeks). A specific recent example of empirical forecasting is discussed. Through Canonic al Correlation Analysis (CCA), experimental forecasts are being made f or the United States surface temperatures at lead times of several sea sons. While modest, the skill is significant in that it defies the exi stence or a 3-week LOP, and so demonstrates the potential for model im provements.