| description abstract | Forecasters often develop rules of thumb for adjusting model guidance. Ideally, before use, these rules of thumb should be validated through a careful comparison of model forecasts and observations over a large sample. Practically, such evaluation studies are difficult to perform because forecast models are continually being changed, and a hypothesized rule of thumb may only be applicable to a particular forecast model configuration. A particular rule of thumb was examined here: dprog/dt. Given a set of lagged forecasts from the same model all verifying at the same time, this rule of thumb suggests that if the forecasts show a trend, this trend is more likely than not to continue and thus provide useful information for correcting the most recent forecast. Forecasters may also note the amount of continuity of forecasts to estimate the magnitude of the error in the most recent forecast. Statistical evaluation of this rule of thumb was made possible here using a dataset of forecasts from a ?frozen? model. A 23-yr record of forecasts was generated from a T62 version of the medium-range forecast model used at the National Centers for Environmental Prediction. Forecasts were initialized from reanalysis data, and January?March forecasts were examined for selected locations. The rule dprog/dt was evaluated with 850-hPa temperature forecasts. A total of 2070 sample days were used in the evaluation. Extrapolation of forecast trends was shown to have little forecast value. Also, there was only a small amount of information on forecast accuracy from the amount of discrepancy between short-term lagged forecasts. The lack of validity of this rule of thumb suggest that others should also be carefully scrutinized before use. | |
