Using forecast sensitivity patterns to improve future forecast skill

Abstract

A simple, relatively inexpensive technique has been developed for using past forecast errors to improve the future forecast skill. the method uses the forecast model and its adjoint and can be considered as a simplified 4-dimensional variational (4-D VAR) system. One-or two-day forecast errors are used to calculate a small perturbation (sensitivity perturbation) to the analyses that minimizes the forecast error. the longer forecasts started from the corrected initial conditions, although better than the original forecasts, are still significantly worse than the shorter forecasts started from the latest analysis, even though they both had access to information covering the same period. As a much less expensive alternative to 4-D VAR, the adjusted initial conditions from one or two days ago are used as a starting point for a second iteration of the regular NCEP analysis and forecast cycle until the present time (t = O) analysis is reached. Forecast experiments indicate that the new analyses result in improvements to medium-range forecast skill, and suggest that the technique can be used in operations, since it increases the cost of the regular analysis cycle by a maximum factor of about 4 to 8, depending on the length of the analysis cycle that is repeated. Several possible operational configurations are also tested. The model used in these experiments is the NCEP's operational global spectral model with 62 waves triangular truncation and 28 ő-vertical levels. an adiabatic version of the adjoint was modified to make it more consistent with the complete forecast model, including only a few simple physical parametrizations (horizontal diffusion and vertical mixing). This adjoint model was used to compute the gradient of the forecast error with respect to initial conditions.