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- Author or Editor: Joseph R. Bocchieri x
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Abstract
A new system is developed which gives both conditional and unconditional probability of snow amount forecasts and categorical forecasts at stations for the 12–24 h period after both 0000 and 1200 GMT. To derive the new equations, the Model Output Statistics technique is used with output from the Limited-area Fine Mesh model (LFM).
First, experimental probability of snow amount forecast equations are developed for the 12–18, 18–24, 12–24 and 24–36 h projections. Verification of the experimental forecasts on independent data indicates that the scores generally deteriorated as compared to the scores for the developmental sample. The deterioration was worse for the 18–24 and 24–36 h projections than for the 12–18 and 12–24 h projections. It's concluded that the snow amount forecasts for the 24–36 h projection aren't skillful enough for operational implementation at this time.
Next, to improve the stability of the forecast equations for the 12–24 h projection, the developmental and independent samples are combined (nine winter seasons) and new equations are developed for ≥5,≥10 and ≥15 cm snow amount categories. In this development, the results of a statistical screening procedure indicate that, generally, the most important predictor is the LFM precipitation amount forecast followed by the mean relative humidity (surface–500 mb) forecast. Other predictors which are relatively important include LFM forecasts of circulation intensity such as 700 mb vertical velocity, 700 mb east–west wind component, 850 mb divergence, 850 mb relative vorticity, and 850 mb east–west wind component. Verification of the new equations on another independent sample indicates that the scores were generally stable except for some deterioration for the ≥15 cm category. The new equations were implemented in the fall of 1982 and should be more useful than the old operational system, which provided forecasts for the ≥10 cm category only and which had been operational within the National Weather Service since October 1977.
Abstract
A new system is developed which gives both conditional and unconditional probability of snow amount forecasts and categorical forecasts at stations for the 12–24 h period after both 0000 and 1200 GMT. To derive the new equations, the Model Output Statistics technique is used with output from the Limited-area Fine Mesh model (LFM).
First, experimental probability of snow amount forecast equations are developed for the 12–18, 18–24, 12–24 and 24–36 h projections. Verification of the experimental forecasts on independent data indicates that the scores generally deteriorated as compared to the scores for the developmental sample. The deterioration was worse for the 18–24 and 24–36 h projections than for the 12–18 and 12–24 h projections. It's concluded that the snow amount forecasts for the 24–36 h projection aren't skillful enough for operational implementation at this time.
Next, to improve the stability of the forecast equations for the 12–24 h projection, the developmental and independent samples are combined (nine winter seasons) and new equations are developed for ≥5,≥10 and ≥15 cm snow amount categories. In this development, the results of a statistical screening procedure indicate that, generally, the most important predictor is the LFM precipitation amount forecast followed by the mean relative humidity (surface–500 mb) forecast. Other predictors which are relatively important include LFM forecasts of circulation intensity such as 700 mb vertical velocity, 700 mb east–west wind component, 850 mb divergence, 850 mb relative vorticity, and 850 mb east–west wind component. Verification of the new equations on another independent sample indicates that the scores were generally stable except for some deterioration for the ≥15 cm category. The new equations were implemented in the fall of 1982 and should be more useful than the old operational system, which provided forecasts for the ≥10 cm category only and which had been operational within the National Weather Service since October 1977.
Abstract
A new system is developed which gives conditional probability forecasts for three precipitation type categories: snow or sleet, freezing rain and rain. Also, the probability forecasts are transformed into categorical forecasts so that a “best category”is provided.
The Model Output Statistics (MOS) technique is used with output from the Limited-area Fine Mesh (LFM) model to develop statistical forecast equations for each of several regions in the conterminous United States. To help account for the evaporational cooling effect, predictors such as LFM forecasts of boundary layer and 850 mb wet-bulb temperatures and observed surface and dew-point temperatures are included. Also, joint predictors are designed to help account for predictor interactions. The values of the joint predictors are relative frequencies of the freezing rain or snow categories taken from graphs that show these frequencies as joint functions of various pairs of LFM predictors.
Results from a statistical screening procedure vary by region but generally indicate that the 850 mb temperature and boundary-layer wet-bulb temperature joint predictor accounts for most of the reduction of variance of the snow category. For the freezing rain category, the 850–500 mb thickness and 1000–850 mb thickness, 1000–500 mb thickness and boundary-layer potential temperature, and the 850 mb temperature and boundary-layer potential temperature joint predictors are found to be important along with the observed surface temperature and dew point.
Verification of the new system on developmental and independent data samples indicates that the scores for the snow category are generally very good and stable; the results for the freezing rain forecasts are not newly as good.
Abstract
A new system is developed which gives conditional probability forecasts for three precipitation type categories: snow or sleet, freezing rain and rain. Also, the probability forecasts are transformed into categorical forecasts so that a “best category”is provided.
The Model Output Statistics (MOS) technique is used with output from the Limited-area Fine Mesh (LFM) model to develop statistical forecast equations for each of several regions in the conterminous United States. To help account for the evaporational cooling effect, predictors such as LFM forecasts of boundary layer and 850 mb wet-bulb temperatures and observed surface and dew-point temperatures are included. Also, joint predictors are designed to help account for predictor interactions. The values of the joint predictors are relative frequencies of the freezing rain or snow categories taken from graphs that show these frequencies as joint functions of various pairs of LFM predictors.
Results from a statistical screening procedure vary by region but generally indicate that the 850 mb temperature and boundary-layer wet-bulb temperature joint predictor accounts for most of the reduction of variance of the snow category. For the freezing rain category, the 850–500 mb thickness and 1000–850 mb thickness, 1000–500 mb thickness and boundary-layer potential temperature, and the 850 mb temperature and boundary-layer potential temperature joint predictors are found to be important along with the observed surface temperature and dew point.
Verification of the new system on developmental and independent data samples indicates that the scores for the snow category are generally very good and stable; the results for the freezing rain forecasts are not newly as good.
Abstract
Linear screening regression is used to derive relationships between parameters computed from observed upper air soundings (RAOBS) and concurrent observations of precipitation type. Precipitation type is defined as three categories: liquid (rain or drizzle), freezing (freezing rain or freezing drizzle) and frozen (snow or ice pellets). Statistical screening results indicate that of the parameters tried the following are important: the mean temperature in the surface–1000 m and 500–2500 m layers; the depth of the warm layer (temperature >0°C), if one exists; the area between the temperature profile and the 0°C isotherm in the warm layer, the depth of the surface-based cold layer, if one exists, with respect to the wet-bulb temperature profile; and the area between the wet-bulb temperature profile and the 0°C isotherm in the surface-based cold layer.
Verification of the specification equations on both developmental and independent data samples indicates that the scores are generally stable. The equations show excellent discrimination ability for liquid and frozen precipitation but have some difficulty with freezing precipitation.
Part of the problem with the freezing category is the fact that freezing drizzle, which is included with freezing rain in this category, can occur with a RAOB in which the temperature is ≤0°C at all levels (no warm layer). It is found that about 44% of the freezing drizzle RAOBs examined have no warm layer.
Abstract
Linear screening regression is used to derive relationships between parameters computed from observed upper air soundings (RAOBS) and concurrent observations of precipitation type. Precipitation type is defined as three categories: liquid (rain or drizzle), freezing (freezing rain or freezing drizzle) and frozen (snow or ice pellets). Statistical screening results indicate that of the parameters tried the following are important: the mean temperature in the surface–1000 m and 500–2500 m layers; the depth of the warm layer (temperature >0°C), if one exists; the area between the temperature profile and the 0°C isotherm in the warm layer, the depth of the surface-based cold layer, if one exists, with respect to the wet-bulb temperature profile; and the area between the wet-bulb temperature profile and the 0°C isotherm in the surface-based cold layer.
Verification of the specification equations on both developmental and independent data samples indicates that the scores are generally stable. The equations show excellent discrimination ability for liquid and frozen precipitation but have some difficulty with freezing precipitation.
Part of the problem with the freezing category is the fact that freezing drizzle, which is included with freezing rain in this category, can occur with a RAOB in which the temperature is ≤0°C at all levels (no warm layer). It is found that about 44% of the freezing drizzle RAOBs examined have no warm layer.
Abstract
The model output statistics (MOS) technique consists of determining a statistical relationship between the forecast output of numerical prediction models and a predictand. This paper presents some results obtained in applying the MOS technique to the prediction of ceiling height by means of screening regression.
Data from 3 winter seasons and 95 eastern U.S. stations are combined in a generalized operator approach to develop multiple regression equations. The potential predictors subjected to screening include surface variables observed at 0700 GMT and forecast output from both the National Meteorological Center's primitive-equation model and the Techniques Development Laboratory's subsynoptic advection model. Prediction equations are developed for 5-, 11-, and 17-hr forecast projections representing ceiling height forecasts valid at 1200, 1800, and 2400 GMT, respectively.
Ceiling height is treated both as a categorized and as a continuous predictand. Where ceiling height is categorized, the regression estimation of event probabilities (REEP) screening technique is used to develop probability forecast equations. Where ceiling height is treated as a continuous variable, specific ceiling height forecast equations are developed by ordinary screening regression.
The independent sample used for testing consists of data for 20 stations in the eastern United States from the winter of 1970–71. Several verification scores, including the Brier P-score, Allen utility score, Heidke skill score, and percent correct, are presented. The verification results indicate that forecasts from the REEP equations are generally better than those from the equations that produce specific heights. Also, the REEP forecasts are better than persistence and climatology.
Abstract
The model output statistics (MOS) technique consists of determining a statistical relationship between the forecast output of numerical prediction models and a predictand. This paper presents some results obtained in applying the MOS technique to the prediction of ceiling height by means of screening regression.
Data from 3 winter seasons and 95 eastern U.S. stations are combined in a generalized operator approach to develop multiple regression equations. The potential predictors subjected to screening include surface variables observed at 0700 GMT and forecast output from both the National Meteorological Center's primitive-equation model and the Techniques Development Laboratory's subsynoptic advection model. Prediction equations are developed for 5-, 11-, and 17-hr forecast projections representing ceiling height forecasts valid at 1200, 1800, and 2400 GMT, respectively.
Ceiling height is treated both as a categorized and as a continuous predictand. Where ceiling height is categorized, the regression estimation of event probabilities (REEP) screening technique is used to develop probability forecast equations. Where ceiling height is treated as a continuous variable, specific ceiling height forecast equations are developed by ordinary screening regression.
The independent sample used for testing consists of data for 20 stations in the eastern United States from the winter of 1970–71. Several verification scores, including the Brier P-score, Allen utility score, Heidke skill score, and percent correct, are presented. The verification results indicate that forecasts from the REEP equations are generally better than those from the equations that produce specific heights. Also, the REEP forecasts are better than persistence and climatology.
Abstract
An automated system for forecasting the conditional probability of frozen precipitation was put into operation by the National Weather Service in November 1972. The Model Output Statistics (MOS) concept was used to develop the system, and both teletypewriter and facsimile products have been distributed to field offices twice daily. In this paper, guidance forecasts from this system are compared to subjective (local) forecasts prepared at Weather Service Forecast Offices. The local forecasts have been archived since September 1973 as part of a combined aviation/public weather forecast verification program within the National Weather Service. The comparative verification between the guidance and locals for two different data samples shows the guidance has produced better forecasts for the 18, 30, and 42 h projections.
In attempting to improve the operational system, we experimented with the Regression Estimation of Event Probabilities (REEP) screening technique. The operational system had been developed with the logit model, but our logit computer program does not objectively screen predictors as REEP does. A comparison of the REEP and logit systems on independent data shows logit to be better.
We used the logit model to develop a new operational system. Five winters of developmental data were used for the new system; the old system was developed from three winters of data. A comparison between the new and the old systems on independent data shows that they are equally accurate for the short-range (12 h) projection but that the new system is more accurate for the longer-range (36 h) projection.
The predictors in the new system include the 850 mb temperature, boundary-layer potential temperature, 1000–500 mb thickness, and 1000–850 mb thickness. These variables are forecast by the National Meteorological Center's primitive equation model. The new system was made operational during the winter of 1975–76 and provides forecasts for the 12, 18, 24, 30, 36, 42, and 48 h projections twice daily.
Abstract
An automated system for forecasting the conditional probability of frozen precipitation was put into operation by the National Weather Service in November 1972. The Model Output Statistics (MOS) concept was used to develop the system, and both teletypewriter and facsimile products have been distributed to field offices twice daily. In this paper, guidance forecasts from this system are compared to subjective (local) forecasts prepared at Weather Service Forecast Offices. The local forecasts have been archived since September 1973 as part of a combined aviation/public weather forecast verification program within the National Weather Service. The comparative verification between the guidance and locals for two different data samples shows the guidance has produced better forecasts for the 18, 30, and 42 h projections.
In attempting to improve the operational system, we experimented with the Regression Estimation of Event Probabilities (REEP) screening technique. The operational system had been developed with the logit model, but our logit computer program does not objectively screen predictors as REEP does. A comparison of the REEP and logit systems on independent data shows logit to be better.
We used the logit model to develop a new operational system. Five winters of developmental data were used for the new system; the old system was developed from three winters of data. A comparison between the new and the old systems on independent data shows that they are equally accurate for the short-range (12 h) projection but that the new system is more accurate for the longer-range (36 h) projection.
The predictors in the new system include the 850 mb temperature, boundary-layer potential temperature, 1000–500 mb thickness, and 1000–850 mb thickness. These variables are forecast by the National Meteorological Center's primitive equation model. The new system was made operational during the winter of 1975–76 and provides forecasts for the 12, 18, 24, 30, 36, 42, and 48 h projections twice daily.
Abstract
No abstract available.
Abstract
No abstract available.
Abstract
Experimental objective forecasts of probability of precipitation (PoP) were made and verified for a large number of United States cities in order to make preliminary tests of the usefulness of the limited area fine mesh (LFM) model. The Model Output Statistics (MOS) technique was used to derive the forecast equations for the winter and summer seasons. Predictors were selected from forecast output of the Primitive Equation (PE), Trajectory (TJ) and LFM models, and from the sine and cosine of the day of the year. Forecast equations were developed with data from only one winter and one summer season (“small sample equations”) from PE predictors only, TJ predictors only, LFM predictors only, and various groupings of these. The small-sample equations were compared on independent data with each other and with operational equations developed on a much larger data sample.
It was found that the small-sample combinations that included the LFM gave better results than the operational equations in winter. However, in summer, all small-sample combinations were significantly worse than the operational equations. It was also found that adding the harmonic terms of the day of year consistently improved the forecasts. Based on these preliminary results, the LFM will be incorporated into the operational PoP equations starting in the winter of 1975–76. Further tests will be made when a larger developmental sample is available for the LFM.
Abstract
Experimental objective forecasts of probability of precipitation (PoP) were made and verified for a large number of United States cities in order to make preliminary tests of the usefulness of the limited area fine mesh (LFM) model. The Model Output Statistics (MOS) technique was used to derive the forecast equations for the winter and summer seasons. Predictors were selected from forecast output of the Primitive Equation (PE), Trajectory (TJ) and LFM models, and from the sine and cosine of the day of the year. Forecast equations were developed with data from only one winter and one summer season (“small sample equations”) from PE predictors only, TJ predictors only, LFM predictors only, and various groupings of these. The small-sample equations were compared on independent data with each other and with operational equations developed on a much larger data sample.
It was found that the small-sample combinations that included the LFM gave better results than the operational equations in winter. However, in summer, all small-sample combinations were significantly worse than the operational equations. It was also found that adding the harmonic terms of the day of year consistently improved the forecasts. Based on these preliminary results, the LFM will be incorporated into the operational PoP equations starting in the winter of 1975–76. Further tests will be made when a larger developmental sample is available for the LFM.
Abstract
A system is developed which produces objective forecasts of conditional probability of frozen precipitation for the conterminous United States. Development of the system consists of two basic steps, in each of which the MOS (Model Output Statistics) concept is used. First, for each of 186 stations, we find a “50%” value for each of three variables predicted by the National Meteorological Center's Primitive Equation (PE) model: 1000–500 mb thickness, boundary-layer potential temperature, and 850-mb temperature. For instance, we find the value of the 1000–500 mb thickness which indicates a 50–50 chance of frozen precipitation at a particular station, provided precipitation occurs. These 50% values are determined by using the logit model to fit data from three winter seasons, September 1969 through March 1972.
Secondly, the deviations from the 50% values are determined for each station for each variable; the relative frequency (for those cases when precipitation occurred) of frozen precipitation is then computed, again with the logit model, as a function of these new variables. In order to get stable results in this last step, data for all stations are combined. In addition to the meteorological variables, we also use the first harmonic of the day of year and station elevation as predictors. Separate logit equations are determined for each of the PE run times, 0000 and 1200 GMT, and for each of four projections, 12, 24, 36, and 48 hours.
This system was put into operation by the National Weather Service in November 1972. Both teletypewriter and facsimile products are being distributed to field offices twice daily.
A comparative verification on independent data for the 12- and 36-hr forecast projections shows the objective system produced better forecasts than those prepared subjectively at the National Meteorological Center.
Abstract
A system is developed which produces objective forecasts of conditional probability of frozen precipitation for the conterminous United States. Development of the system consists of two basic steps, in each of which the MOS (Model Output Statistics) concept is used. First, for each of 186 stations, we find a “50%” value for each of three variables predicted by the National Meteorological Center's Primitive Equation (PE) model: 1000–500 mb thickness, boundary-layer potential temperature, and 850-mb temperature. For instance, we find the value of the 1000–500 mb thickness which indicates a 50–50 chance of frozen precipitation at a particular station, provided precipitation occurs. These 50% values are determined by using the logit model to fit data from three winter seasons, September 1969 through March 1972.
Secondly, the deviations from the 50% values are determined for each station for each variable; the relative frequency (for those cases when precipitation occurred) of frozen precipitation is then computed, again with the logit model, as a function of these new variables. In order to get stable results in this last step, data for all stations are combined. In addition to the meteorological variables, we also use the first harmonic of the day of year and station elevation as predictors. Separate logit equations are determined for each of the PE run times, 0000 and 1200 GMT, and for each of four projections, 12, 24, 36, and 48 hours.
This system was put into operation by the National Weather Service in November 1972. Both teletypewriter and facsimile products are being distributed to field offices twice daily.
A comparative verification on independent data for the 12- and 36-hr forecast projections shows the objective system produced better forecasts than those prepared subjectively at the National Meteorological Center.
Abstract
A Model Output Statistics system for forecasting the conditional probability of precipitation type (PoPT) became operational within the National Weather Service in September 1978. Forecasts are provided for three precipitation type categories: snow or ice pellets, freezing rain, and rain. To develop the forecast equations, data are combined from different stations because of the limited amount of developmental data. To justify combining the data, the Limited-area Fine Mesh (LFM) model predictors are transformed from their original values through the use of the logit model. In one experiment, it is shown that probability of snow forecasts are made more accurate through an improved use of the logit model for predictor transformation.
The new transformation procedure is then used in the development of a set of experimental PoPT forecast equations. The experimental equations differ from the operational equations in other ways also. The developmental sample for the experimental equations included approximately three winter seasons more data than the sample used for the operational system. Also, improvements are made to the potential predictors used to develop the experimental equations. Finally, freezing rain mixed with any other precipitation type is defined as freezing rain in the experimental system; in the operational system, this mixture of precipitation is defined as rain.
A comparative verification between the experimental and operational systems on independent data indicates that, overall, the experimental PoPT forecasts are better than the operational forecasts, especially for 12–24 h freezing rain forecasts. Based on these results, new operational PoPT forecast equations are developed incorporating the features associated with the experimental equations. The new system was implemented in the fall of 1982.
Abstract
A Model Output Statistics system for forecasting the conditional probability of precipitation type (PoPT) became operational within the National Weather Service in September 1978. Forecasts are provided for three precipitation type categories: snow or ice pellets, freezing rain, and rain. To develop the forecast equations, data are combined from different stations because of the limited amount of developmental data. To justify combining the data, the Limited-area Fine Mesh (LFM) model predictors are transformed from their original values through the use of the logit model. In one experiment, it is shown that probability of snow forecasts are made more accurate through an improved use of the logit model for predictor transformation.
The new transformation procedure is then used in the development of a set of experimental PoPT forecast equations. The experimental equations differ from the operational equations in other ways also. The developmental sample for the experimental equations included approximately three winter seasons more data than the sample used for the operational system. Also, improvements are made to the potential predictors used to develop the experimental equations. Finally, freezing rain mixed with any other precipitation type is defined as freezing rain in the experimental system; in the operational system, this mixture of precipitation is defined as rain.
A comparative verification between the experimental and operational systems on independent data indicates that, overall, the experimental PoPT forecasts are better than the operational forecasts, especially for 12–24 h freezing rain forecasts. Based on these results, new operational PoPT forecast equations are developed incorporating the features associated with the experimental equations. The new system was implemented in the fall of 1982.
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.
