Long-Range Weather Forecasting Using an Analog Approach

Abstract

An analog selection method relying an the coincidence of main features (large ridge lines) in the Northern Hemisphere is presented and used for making 30-day weather forecasts for Hungary. Numerous analog model trials were tested, with the aid of the advance selection of the ?best circulation analogs? of the Atlantic-European forecast region, for every target month of the 27-yr calibration period and the 5.5 yr test period. The best predictor types are a one pentad (i.e., 5-day) predictor period with spatial smoothing (allowing slight longitudinal shifts between pressure patterns), and a 2 pentad predictor period with time averaging (with a weighting factor of 0.4 on data from outside the forecast region in both cases). A subset of each group of analogs with similar circulations during the forecast period was identified. Using the subset 1eads to further significant increases in skill. Monthly weather forecast for temperature (5-day subperiods) and precipitation quantity (10-day subperiods) in any of three climatologically equal probable categories were given. Different statistics, which were slightly but significantly better than chance expectation and persistence, were employed to area the skill of the forecast. By means of the previously chosen best circulation analogs, the potential monthly analog predictability based on our dataset and methods were also determined. Accordingly, the operable forecasting method realizes 30%?60% of potential predictability. Using lengthened data series for selecting analogs, the improvement in both analog predictability and actual forecasting skills was investigated. Extrapolating the experimental data for the future by comparing it with a logistic curve, an estimate was obtained of increased forecast skill from the present 38%?39% to 42% within 15 yr.