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- Author or Editor: Bob Glahn x
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Abstract
Logistic regression is an alternative to regression estimation of event probabilities (REEP) and other techniques for estimating weather event probabilities based on NWP output or other predictors. Logistic regression has the advantage over REEP in that the probability estimates are constrained between zero and unity, whereas REEP can “overshoot” these values. It may be a detriment in some applications that the curves developed, one for each of several predictand categories (events), are symmetric. This paper shows how the logit curve can easily be made nonsymmetric as a function of a predictor, and thereby possibly achieve a better fit to the data. As with REEP, the probabilities estimated by logistic regression for each of several categories of a variable may not be consistent. For instance, the probability of snow > 2 in. may exceed the probability of snow > 1 in. Such inconsistencies can be avoided by developing a single equation involving all predictand categories and including another predictor that is a function of the predictand. This effectively, for a single predictor, produces parallel curves separated along the predictor axis but imposes restrictions on the equations and probabilities produced from them. The relationship between the predictor(s) and the predictand must be considered in determining the functional form. With only one predictor, defining the function is relatively straightforward. However, with multiple predictors, the process is more problematic. This paper demonstrates an alternative to imposing a functional form by using binary predictors. This formulation also achieves the goal of producing consistent forecasts and generalizes more readily to multiple predictors.
Abstract
Logistic regression is an alternative to regression estimation of event probabilities (REEP) and other techniques for estimating weather event probabilities based on NWP output or other predictors. Logistic regression has the advantage over REEP in that the probability estimates are constrained between zero and unity, whereas REEP can “overshoot” these values. It may be a detriment in some applications that the curves developed, one for each of several predictand categories (events), are symmetric. This paper shows how the logit curve can easily be made nonsymmetric as a function of a predictor, and thereby possibly achieve a better fit to the data. As with REEP, the probabilities estimated by logistic regression for each of several categories of a variable may not be consistent. For instance, the probability of snow > 2 in. may exceed the probability of snow > 1 in. Such inconsistencies can be avoided by developing a single equation involving all predictand categories and including another predictor that is a function of the predictand. This effectively, for a single predictor, produces parallel curves separated along the predictor axis but imposes restrictions on the equations and probabilities produced from them. The relationship between the predictor(s) and the predictand must be considered in determining the functional form. With only one predictor, defining the function is relatively straightforward. However, with multiple predictors, the process is more problematic. This paper demonstrates an alternative to imposing a functional form by using binary predictors. This formulation also achieves the goal of producing consistent forecasts and generalizes more readily to multiple predictors.
Abstract
Model output statistics (MOS) forecast relationships for temperature and dewpoint developed with least squares regression and put into operation by the National Weather Service (NWS) are unbiased over the sample period of development. However, short-term biases within that period can exist, and application of the regression equations to new data may produce forecasts with short- or long-term biases. Because NWP models undergo changes over time, MOS forecasts can be biased because of these changes, and also possibly because of local environmental changes. These biases can be largely eliminated. In the decaying average method, a “decay factor” is used. This value affects not only the short- and long-term bias characteristics, but also other accuracy measures of the forecasts. This paper shows how different values of the decay factor affect MOS temperature and dewpoint forecasts, and the range of factors that would be appropriate for bias correcting those forecasts. Biases and other quality measures are shown for both cool and warm season samples before and after various values of the decay factor have been applied.
Abstract
Model output statistics (MOS) forecast relationships for temperature and dewpoint developed with least squares regression and put into operation by the National Weather Service (NWS) are unbiased over the sample period of development. However, short-term biases within that period can exist, and application of the regression equations to new data may produce forecasts with short- or long-term biases. Because NWP models undergo changes over time, MOS forecasts can be biased because of these changes, and also possibly because of local environmental changes. These biases can be largely eliminated. In the decaying average method, a “decay factor” is used. This value affects not only the short- and long-term bias characteristics, but also other accuracy measures of the forecasts. This paper shows how different values of the decay factor affect MOS temperature and dewpoint forecasts, and the range of factors that would be appropriate for bias correcting those forecasts. Biases and other quality measures are shown for both cool and warm season samples before and after various values of the decay factor have been applied.
Abstract
The Meteorological Development Laboratory (MDL) has developed and implemented an aviation weather prediction system that runs each hour and produces forecast guidance for each hour into the future out to 25 h covering the major forecast period of the National Weather Service (NWS) Terminal Aerodrome Forecast. The Localized Aviation Model Output Statistics (MOS) Program (LAMP) consists of analyses of observations, simple advective models, and a statistical component that updates the longer-range MOS forecasts from the Global Forecast System (GFS) model. LAMP, being an update to GFS MOS, is shown to be an improvement over it, as well as improving over persistence. LAMP produces probabilistic forecasts for the aviation weather elements of ceiling height, sky cover, visibility, obstruction to vision, precipitation occurrence and type, and thunderstorms. Best-category forecasts are derived from these probabilities and their associated thresholds. The LAMP guidance of sensible weather is available for 1591 stations in the contiguous United States, Alaska, Hawaii, Puerto Rico, and the Virgin Islands. Probabilistic guidance of thunderstorms is also available on a grid. The LAMP guidance is available to the entire weather enterprise via NWS communication networks and the World Wide Web. In the future, all station guidance will be gridded and be made available in a form compatible with the NWS’s National Digital Forecast Database.
Abstract
The Meteorological Development Laboratory (MDL) has developed and implemented an aviation weather prediction system that runs each hour and produces forecast guidance for each hour into the future out to 25 h covering the major forecast period of the National Weather Service (NWS) Terminal Aerodrome Forecast. The Localized Aviation Model Output Statistics (MOS) Program (LAMP) consists of analyses of observations, simple advective models, and a statistical component that updates the longer-range MOS forecasts from the Global Forecast System (GFS) model. LAMP, being an update to GFS MOS, is shown to be an improvement over it, as well as improving over persistence. LAMP produces probabilistic forecasts for the aviation weather elements of ceiling height, sky cover, visibility, obstruction to vision, precipitation occurrence and type, and thunderstorms. Best-category forecasts are derived from these probabilities and their associated thresholds. The LAMP guidance of sensible weather is available for 1591 stations in the contiguous United States, Alaska, Hawaii, Puerto Rico, and the Virgin Islands. Probabilistic guidance of thunderstorms is also available on a grid. The LAMP guidance is available to the entire weather enterprise via NWS communication networks and the World Wide Web. In the future, all station guidance will be gridded and be made available in a form compatible with the NWS’s National Digital Forecast Database.
