An Operational Subsynoptic Advection Model (SAM)

Glahn, Harry R.; Lowry, Dale A.

doi:10.1175/1520-0450(1972)011<0578:aosam>2.0.co;2

Cited by 11 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Introduced about 30 years ago (Glahn and Lowry 1972), MOS forecasts continue to provide excellent guidance for operational weather forecasters. The current operational implementation uses NWP model forecasts, available observations, and climatological information as predictors to produce improved values of products such as minimum and maximum temperature, dew-point, wind speed and direction, and precipitation information.…”

Section: Model Output Statistics Imentioning

confidence: 99%

“…The MOS technique (Glahn and Lowry 1972) statistically post-processes NWP forecasts to, amongst other things, correct partially for systematic, state dependent model bias. The technique involves determining a linear relationship between predictors (NWP forecasts, previous observations, and climatological information) and predictands (observed weather elements such as min/max temperature, probability of precipitation, etc.).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecast 4D‐Var: Exploiting Model Output Statistics

Hansen

Emanuel

2003

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

SUMMARYForecasts of certain weather elements are improved by linearly relating observed elements to past observations, climatological information, and numerical weather-prediction-model output. Model Output Statistics (MOS) is a statistical post-processing of model output that is capable of forecasting some subgrid scale, synoptically-forced events and of correcting some systematic, but state dependent, model bias. This tendency of accounting for model error is exploited by feeding MOS forecasts back into the state estimation problem. MOS forecasts and their associated uncertainty are treated as 'observations' of the future system state, and a fourdimensional variational assimilation procedure is employed to improve the original analysis and resulting model forecast. In a simple-model scenario, it is found that this approach has a small negative impact on the magnitude of forecast errors relative to MOS with a decrease in the number of very good forecasts, but a large positive impact on the variance about the forecast errors: forecast busts are reduced. As a further step, a second round of MOS is performed on the new model forecasts in a manner identical to the original MOS approach. This second application of MOS results in a signi cant reduction in both the forecast errors and the variance about those errors relative to the rst application of MOS, and includes an increase in very good forecasts.

show abstract

Section: Model Output Statistics Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecast 4D‐Var: Exploiting Model Output Statistics

Hansen

Emanuel

2003

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

show abstract

“…The future value of a parameter at a given point is determined by assuming the parameter will change only by advection with the characteristic wind; thus, prediction is accomplished simply by looking at upstream values at an appropriate distance and direction based on characteristic wind and forecast time. This procedure is similar to that used in the Subsynoptic Advective Model by Glahn et al (1969). The scheme is a very time-efficient computer algorithm to project evolution of atmospheric patterns and can be utilized as soon as mandatory level radiosonde data is received (one to one and one-half hours after observation time).…”

Section: Mcidas Short-range Advective Forecastsmentioning

confidence: 99%

focus on forecasting

1980

Bulletin of the American Meteorological Society

View full text Add to dashboard Cite

show abstract

“…A subsynoptic scale advection model (SAM), developed at the Techniques Development Laboratory by Glahn and Lowry (1967) and tested operationally by Glahn et al (1969), provides forecasts of precipitation occurrence on a space scale of 50 miles for the central and eastern United States. At present, the SAM is not entirely subsynoptic; it is a two-level model, 500 mb and surface.…”

Section: Snow Amount Prediction Techniquesmentioning

confidence: 99%

Snow Prediction—A Formidable Challenge

Spiegler¹

1970

Weatherwise

View full text Add to dashboard Cite

An Operational Subsynoptic Advection Model (SAM)

Cited by 11 publications

References 0 publications

Forecast 4D‐Var: Exploiting Model Output Statistics

Forecast 4D‐Var: Exploiting Model Output Statistics

focus on forecasting

Snow Prediction—A Formidable Challenge

Contact Info

Product

Resources

About