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Abstract
An automated system for predicting maximum and minimum surface temperatures for 12- to 60-hr projections is described. The system uses multiple regression equations derived for 131 cities in the United States and 12 in southern Canada from 18 years of daily data stratified by 2-month periods. The predictors are selected by screening upper level heights and thicknesses observed at 67 grid points in North America and surface temperatures observed at the network of cities. On the average, about three-fourths of the temperature variance is explained by 4 or 5 variables, and the standard error of estimate is just over 4F. The system has been applied on an iterative basis twice daily at the National Meteorological Center (NMC) in Suitland, Md., since March 1963. Verification statistics are presented for 18 months of operational forecasts made by utilizing the barotropic and Reed numerical models as input to the multiple regression equations. During this period the automated temperature forecasts were superior to persistence and almost as good as subjective forecasts. Results of a one-month experiment are cited to demonstrate the improvement in temperature forecasting attainable by utilizing the NMC primitive equation model as numerical input to the system. Suggestions are also made for subjective improvements by considering factors neglected in the derivation.
Abstract
An automated system for predicting maximum and minimum surface temperatures for 12- to 60-hr projections is described. The system uses multiple regression equations derived for 131 cities in the United States and 12 in southern Canada from 18 years of daily data stratified by 2-month periods. The predictors are selected by screening upper level heights and thicknesses observed at 67 grid points in North America and surface temperatures observed at the network of cities. On the average, about three-fourths of the temperature variance is explained by 4 or 5 variables, and the standard error of estimate is just over 4F. The system has been applied on an iterative basis twice daily at the National Meteorological Center (NMC) in Suitland, Md., since March 1963. Verification statistics are presented for 18 months of operational forecasts made by utilizing the barotropic and Reed numerical models as input to the multiple regression equations. During this period the automated temperature forecasts were superior to persistence and almost as good as subjective forecasts. Results of a one-month experiment are cited to demonstrate the improvement in temperature forecasting attainable by utilizing the NMC primitive equation model as numerical input to the system. Suggestions are also made for subjective improvements by considering factors neglected in the derivation.
Abstract
Abstract
Abstract
The history of the National Weather Service's development efforts in terminal weather prediction for aviation is discussed and results from recent experiments involving three approaches are presented.
In one approach, single-station equations for predicting the probability of specific ceiling and visibility categories are developed. The equations are based upon weather observations at the local terminal only and are derived by using the Regression Estimation of Event Probabilities screening technique.
In another approach, Model Output Statistics (MOS) is used to develop probability forecast equations for ceiling and visibility. MOS consists of determining a statistical relationship between a predictand and the forecast output of numerical prediction models. The statistical relationship is determined by screening regression in this paper. The two numerical models used are the National Meteorological Center's (NMC) primitive equation (PE) model and the Techniques Development Laboratory's Subsynoptic Advection Model (SAM). A forecast system developed by MOS is shown to be useful as guidance to the Aviation Forecast Branch at NMC.
In a third approach, another probability forecast system, called SINGMOS (SINGle station and Model Output Statistics), is developed. SINGMOS is a combination of the single-station and MOS systems and includes observed surface data, forecast output from SAM and PE, and forecasts from single-station prediction equations as predictors. From comparison with official terminal forecasts (FT's) on independent data, it is concluded that SINGMOS is better than the FT's for the long-range (8–16 hr) forecasts; however, for the short-range (4 hr) forecasts, the FT's are better than SINGMOS.
Abstract
The history of the National Weather Service's development efforts in terminal weather prediction for aviation is discussed and results from recent experiments involving three approaches are presented.
In one approach, single-station equations for predicting the probability of specific ceiling and visibility categories are developed. The equations are based upon weather observations at the local terminal only and are derived by using the Regression Estimation of Event Probabilities screening technique.
In another approach, Model Output Statistics (MOS) is used to develop probability forecast equations for ceiling and visibility. MOS consists of determining a statistical relationship between a predictand and the forecast output of numerical prediction models. The statistical relationship is determined by screening regression in this paper. The two numerical models used are the National Meteorological Center's (NMC) primitive equation (PE) model and the Techniques Development Laboratory's Subsynoptic Advection Model (SAM). A forecast system developed by MOS is shown to be useful as guidance to the Aviation Forecast Branch at NMC.
In a third approach, another probability forecast system, called SINGMOS (SINGle station and Model Output Statistics), is developed. SINGMOS is a combination of the single-station and MOS systems and includes observed surface data, forecast output from SAM and PE, and forecasts from single-station prediction equations as predictors. From comparison with official terminal forecasts (FT's) on independent data, it is concluded that SINGMOS is better than the FT's for the long-range (8–16 hr) forecasts; however, for the short-range (4 hr) forecasts, the FT's are better than SINGMOS.
Abstract
Recent changes in the National Weather Service's automated system of forecasting maximum and minimum surface temperatures are described and illustrated. Modifications include use of the primitive equation model, later surface reports, computer-analyzed isotherms, and climatologically-determined forecast limits. Verification figures are presented to show the improvement of the new system over the old and to justify the replacement of centralized subjective temperature forecasts by completely objective ones.
Abstract
Recent changes in the National Weather Service's automated system of forecasting maximum and minimum surface temperatures are described and illustrated. Modifications include use of the primitive equation model, later surface reports, computer-analyzed isotherms, and climatologically-determined forecast limits. Verification figures are presented to show the improvement of the new system over the old and to justify the replacement of centralized subjective temperature forecasts by completely objective ones.
Abstract
An objective method of forecasting maximum and minimum surface temperatures for periods from 12 to 60 hr in advance is discussed and illustrated. The method makes use of multiple regression equations derived for 108 cities in the United States and 11 cities in Canada from 16 years of daily data stratified by 2-month periods. The predictors are selected by screening (by pairs) the following parameters:
a) 700-mb height and 700–1000 mb thickness observed at 67 grid points in North America about 12 hr before the valid time of the prognostic temperature;
b) maximum and minimum temperatures observed at the network of 119 cities about 12 or 24 hr before the prognostic valid time; and
c) the day of the year.
On the average approximately ¾ of the temperature variance is explained by 5 variables, and the standard error of estimate is about 4F.
The method has been applied in an iterative fashion twice daily at the National Meteorological Center at Suitland, Md., since September 1965. The first forecast, for 12 hr in advance, uses only observed values of height, thickness, and temperature as input to the multiple regression equations. Subsequent forecasts utilize numerical prognoses of height and thickness, as upper air input, and preceding automated forecasts of maximum and minimum, prepared by the system, as temperature input. Verification statistics are presented for a year's operation, and the resulting objective forecasts appear to be almost as good as subjective forecasts and superior to persistence or an older objective method.
Abstract
An objective method of forecasting maximum and minimum surface temperatures for periods from 12 to 60 hr in advance is discussed and illustrated. The method makes use of multiple regression equations derived for 108 cities in the United States and 11 cities in Canada from 16 years of daily data stratified by 2-month periods. The predictors are selected by screening (by pairs) the following parameters:
a) 700-mb height and 700–1000 mb thickness observed at 67 grid points in North America about 12 hr before the valid time of the prognostic temperature;
b) maximum and minimum temperatures observed at the network of 119 cities about 12 or 24 hr before the prognostic valid time; and
c) the day of the year.
On the average approximately ¾ of the temperature variance is explained by 5 variables, and the standard error of estimate is about 4F.
The method has been applied in an iterative fashion twice daily at the National Meteorological Center at Suitland, Md., since September 1965. The first forecast, for 12 hr in advance, uses only observed values of height, thickness, and temperature as input to the multiple regression equations. Subsequent forecasts utilize numerical prognoses of height and thickness, as upper air input, and preceding automated forecasts of maximum and minimum, prepared by the system, as temperature input. Verification statistics are presented for a year's operation, and the resulting objective forecasts appear to be almost as good as subjective forecasts and superior to persistence or an older objective method.
Abstract
The relationship between precipitable water, W; 1000–500 mb thickness, h; station elevation, E, and observed precipitation was examined to obtain an equation to estimate saturation thickness. Radiosonde observations were categorized by values of W, h, and E, and a value for saturation thickness, h3 , was determined for each precipitable water category and station elevation group on the basis of the precipitation frequency. A regression equation was then developed that relates h3 to InW and E.
Regression equations were then developed to relate InW to surface observations and the 12-h forecast of W from the LFM model to enable estimation of the saturation thickness at any hour. About 91% of the variance in InW explained by the natural logarithm of the LFM precipitable water forecast. An additional 2–4% was explained by the surface dew point observations. No other variable added significantly to the relationship. An equation relating InW to surface observations was derived to be used in the event the LFM forecast of InW is not available.
Abstract
The relationship between precipitable water, W; 1000–500 mb thickness, h; station elevation, E, and observed precipitation was examined to obtain an equation to estimate saturation thickness. Radiosonde observations were categorized by values of W, h, and E, and a value for saturation thickness, h3 , was determined for each precipitable water category and station elevation group on the basis of the precipitation frequency. A regression equation was then developed that relates h3 to InW and E.
Regression equations were then developed to relate InW to surface observations and the 12-h forecast of W from the LFM model to enable estimation of the saturation thickness at any hour. About 91% of the variance in InW explained by the natural logarithm of the LFM precipitable water forecast. An additional 2–4% was explained by the surface dew point observations. No other variable added significantly to the relationship. An equation relating InW to surface observations was derived to be used in the event the LFM forecast of InW is not available.
