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Abstract
The Model Output Statistics (MOS) technique has been applied to the prediction of surface winds. Warm and cool season forecasting equations were developed by screening as potential predictors several forecast fields from the National Meteorological Center's Primitive Equation (PE) model. Four additional weather parameters from surface reports were also screened to provide the latest observed conditions for the initial forecast projection. Separate equations for the U and V wind components and wind speed, S, were derived for each of 233 stations for projections of 12 to 48 b. For any given station and projection, the U,V, and S equations were required to use the same predictors. Initially, the first three predictors were forced to be boundary layer U,V, and S forecasts from the PE model.
Comparative verification was carried out on independent data for test forecasts at 20 widely distributed stations during warm and cool seasons. In addition, operational guidance forecasts were verified for 92 stations during November 1973 to March 1974. Both of these tests suggested that although the automated method tended to underforecast strong winds, its objective forecasts were generally more accurate than the subjective National Weather Service forecasts. Adjustment and transformation procedures to enable the automated system to produce more strong wind forecasts were also tested.
Abstract
The Model Output Statistics (MOS) technique has been applied to the prediction of surface winds. Warm and cool season forecasting equations were developed by screening as potential predictors several forecast fields from the National Meteorological Center's Primitive Equation (PE) model. Four additional weather parameters from surface reports were also screened to provide the latest observed conditions for the initial forecast projection. Separate equations for the U and V wind components and wind speed, S, were derived for each of 233 stations for projections of 12 to 48 b. For any given station and projection, the U,V, and S equations were required to use the same predictors. Initially, the first three predictors were forced to be boundary layer U,V, and S forecasts from the PE model.
Comparative verification was carried out on independent data for test forecasts at 20 widely distributed stations during warm and cool seasons. In addition, operational guidance forecasts were verified for 92 stations during November 1973 to March 1974. Both of these tests suggested that although the automated method tended to underforecast strong winds, its objective forecasts were generally more accurate than the subjective National Weather Service forecasts. Adjustment and transformation procedures to enable the automated system to produce more strong wind forecasts were also tested.
Abstract
We have applied the Model Output Statistics (MOS) approach to the prediction of cloudiness. Final guidance warm and cool season forecasting equations were developed by screening forecast fields from the primitive equation and trajectory models. We derived separate equations for each of 233 stations to estimate the probability of clear, scattered, broken and overcast conditions 12 to 48 h in advance. The same predictors were used in all four equations for any given station and projection. In like manner, we also derived a set of early guidance equations for the warm season by screening forecasts from the limited-area fine mesh model. Here, separate equations were developed for 230 stations and projections of 6 to 24 h. Weather parameters from surface reports were also included as potential predictors for the first two forecast projections to provide the latest observed conditions for the early and final guidance systems.
We verified both experimental and operational cloud forecasts made from the final guidance equations for approximately 90 widely distributed test stations. These objective cloud forecasts were compared with subjective National Weather Service local forecasts after transforming the objective probability estimates into categorical form. However, using the category with the highest probability produced too many forecasts of clear and overcast. So we transformed the objective estimates in such a way that the percentage of correct forecasts was still high, but with the restriction that the categorical forecasts were relatively unbiased (i.e., each category of cloud amount was forecast about as often as it occurred). The verification scores showed that both the experimental and operational objective forecasts compared favorably with the subjective forecasts.
Abstract
We have applied the Model Output Statistics (MOS) approach to the prediction of cloudiness. Final guidance warm and cool season forecasting equations were developed by screening forecast fields from the primitive equation and trajectory models. We derived separate equations for each of 233 stations to estimate the probability of clear, scattered, broken and overcast conditions 12 to 48 h in advance. The same predictors were used in all four equations for any given station and projection. In like manner, we also derived a set of early guidance equations for the warm season by screening forecasts from the limited-area fine mesh model. Here, separate equations were developed for 230 stations and projections of 6 to 24 h. Weather parameters from surface reports were also included as potential predictors for the first two forecast projections to provide the latest observed conditions for the early and final guidance systems.
We verified both experimental and operational cloud forecasts made from the final guidance equations for approximately 90 widely distributed test stations. These objective cloud forecasts were compared with subjective National Weather Service local forecasts after transforming the objective probability estimates into categorical form. However, using the category with the highest probability produced too many forecasts of clear and overcast. So we transformed the objective estimates in such a way that the percentage of correct forecasts was still high, but with the restriction that the categorical forecasts were relatively unbiased (i.e., each category of cloud amount was forecast about as often as it occurred). The verification scores showed that both the experimental and operational objective forecasts compared favorably with the subjective forecasts.
Abstract
The production of interpretive weather element forecasts from dynamical model output variables is now an integral part of the centralized guidance systems of weather services throughout the world. The statistical forecasting system in the United States probably generates the most extensive suite of operational products, although other nations including Australia, Canada, France, Italy, The Netherlands, and the United Kingdom also routinely provide guidance for many weather elements and locations.
The United States' statistical guidance system has evolved throughout the past 20 yr. The two principal formulation methods that have been employed are the model output statistics (MOS) and “perfect prog” approaches. These techniques have advantages and disadvantages that influence both aggregate and specific day-to-day performance characteristics of the associated weather element forecasts. Verification results indicate that forecasts from both statistical approaches provide useful guidance for most weather elements and projections for locations throughout the contiguous United States and Alaska. The MOS forecasts have generally been superior to the perfect prog guidance; the drawback to MOS is the necessity to rely on a relatively stable numerical prediction model. As dynamical models change and increase in skill, the perfect prog approach may be preferred for some applications.
Abstract
The production of interpretive weather element forecasts from dynamical model output variables is now an integral part of the centralized guidance systems of weather services throughout the world. The statistical forecasting system in the United States probably generates the most extensive suite of operational products, although other nations including Australia, Canada, France, Italy, The Netherlands, and the United Kingdom also routinely provide guidance for many weather elements and locations.
The United States' statistical guidance system has evolved throughout the past 20 yr. The two principal formulation methods that have been employed are the model output statistics (MOS) and “perfect prog” approaches. These techniques have advantages and disadvantages that influence both aggregate and specific day-to-day performance characteristics of the associated weather element forecasts. Verification results indicate that forecasts from both statistical approaches provide useful guidance for most weather elements and projections for locations throughout the contiguous United States and Alaska. The MOS forecasts have generally been superior to the perfect prog guidance; the drawback to MOS is the necessity to rely on a relatively stable numerical prediction model. As dynamical models change and increase in skill, the perfect prog approach may be preferred for some applications.
Abstract
Comparative verification of operational 6-h quantitative precipitation forecast (QPF) products used for streamflow models run at National Weather Service (NWS) River Forecast Centers (RFCs) is presented. The QPF products include 1) national guidance produced by operational numerical weather prediction (NWP) models run at the National Centers for Environmental Prediction (NCEP), 2) guidance produced by forecasters at the Hydrometeorological Prediction Center (HPC) of NCEP for the conterminous United States, 3) local forecasts produced by forecasters at NWS Weather Forecast Offices (WFOs), and 4) the final QPF product for multi-WFO areas prepared by forecasters at RFCs. A major component of the study was development of a simple scoring methodology to indicate the relative accuracy of the various QPF products for NWS managers and possibly hydrologic users. The method is based on mean absolute error (MAE) and bias scores for continuous precipitation amounts grouped into mutually exclusive intervals. The grouping (stratification) was conducted on the basis of observed precipitation, which is customary, and also forecast precipitation. For ranking overall accuracy of each QPF product, the MAE for the two stratifications was objectively combined. The combined MAE could be particularly useful when the accuracy rankings for the individual stratifications are not consistent. MAE and bias scores from the comparative verification of 6-h QPF products during the 1998/99 cool season in the eastern United States for day 1 (0–24-h period) indicated that the HPC guidance performed slightly better than corresponding products issued by WFOs and RFCs. Nevertheless, the HPC product was only marginally better than the best-performing NCEP NWP model for QPF in the eastern United States, the Aviation (AVN) Model. In the western United States during the 1999/2000 cool season, the WFOs improved on the HPC guidance for day 1 but not for day 2 or day 3 (24–48- and 48–72-h periods, respectively). Also, both of these human QPF products improved on the AVN Model on day 1, but by day 3 neither did. These findings contributed to changes in the NWS QPF process for hydrologic model input.
Abstract
Comparative verification of operational 6-h quantitative precipitation forecast (QPF) products used for streamflow models run at National Weather Service (NWS) River Forecast Centers (RFCs) is presented. The QPF products include 1) national guidance produced by operational numerical weather prediction (NWP) models run at the National Centers for Environmental Prediction (NCEP), 2) guidance produced by forecasters at the Hydrometeorological Prediction Center (HPC) of NCEP for the conterminous United States, 3) local forecasts produced by forecasters at NWS Weather Forecast Offices (WFOs), and 4) the final QPF product for multi-WFO areas prepared by forecasters at RFCs. A major component of the study was development of a simple scoring methodology to indicate the relative accuracy of the various QPF products for NWS managers and possibly hydrologic users. The method is based on mean absolute error (MAE) and bias scores for continuous precipitation amounts grouped into mutually exclusive intervals. The grouping (stratification) was conducted on the basis of observed precipitation, which is customary, and also forecast precipitation. For ranking overall accuracy of each QPF product, the MAE for the two stratifications was objectively combined. The combined MAE could be particularly useful when the accuracy rankings for the individual stratifications are not consistent. MAE and bias scores from the comparative verification of 6-h QPF products during the 1998/99 cool season in the eastern United States for day 1 (0–24-h period) indicated that the HPC guidance performed slightly better than corresponding products issued by WFOs and RFCs. Nevertheless, the HPC product was only marginally better than the best-performing NCEP NWP model for QPF in the eastern United States, the Aviation (AVN) Model. In the western United States during the 1999/2000 cool season, the WFOs improved on the HPC guidance for day 1 but not for day 2 or day 3 (24–48- and 48–72-h periods, respectively). Also, both of these human QPF products improved on the AVN Model on day 1, but by day 3 neither did. These findings contributed to changes in the NWS QPF process for hydrologic model input.
Abstract
Currently, two sets of automated numerical-statistical forecasts of maximum/minimum (max/min) temperatures for calendar day periods are produced in the day-to-day operations of the National Weather Service. The “early” guidance forecasts are based on output from the Limited-area Fine Mesh (LFM) model, while the “final” guidance relies primarily on predictions from the hemispheric Primitive Equation (PE) model. This paper describes recent improvements to the early guidance surface temperature prediction system.
The Techniques Development Laboratory recently developed new early guidance equations to forecast calendar day max/min temperatures for projections out to approximately 60 h and hourly temperatures at 3 h intervals out to 51 h for approximately 230 stations in the conterminous United States. A combination of LFM model output, surface weather observations and climatic factors were used in this development. We derived three sets of temperature prediction equations for both the 0000 and 1200 GMT forecast cycle as follows: 1) max/min equations for the first (24 h) period and 3 h equations for projections of 6 to 27 h;2) max/min equations for the second (36 h) period and 3 h equations for projections to 27 to 39 h; and 3) max/min equations for the third (48 h) period and 3 h equations for projections of 39 to 51 h. To enhance consistency among the various max (or min) and 3 h forecasts, all the equations within each set are comprised of the same 10 predictors. We also derived a separate set of 60 h max/min equations.
Comparative verification indicates that, in sharp contrast to past results, max/min forecasts from the new early guidance system are now better than those from the final guidance system. In addition, the automated 3 h temperature predictions are superior to persistence forecasts based on 3 to 6 h old temperature observations, as well as to persistence forecasts based on reports taken 24 h earlier.
Abstract
Currently, two sets of automated numerical-statistical forecasts of maximum/minimum (max/min) temperatures for calendar day periods are produced in the day-to-day operations of the National Weather Service. The “early” guidance forecasts are based on output from the Limited-area Fine Mesh (LFM) model, while the “final” guidance relies primarily on predictions from the hemispheric Primitive Equation (PE) model. This paper describes recent improvements to the early guidance surface temperature prediction system.
The Techniques Development Laboratory recently developed new early guidance equations to forecast calendar day max/min temperatures for projections out to approximately 60 h and hourly temperatures at 3 h intervals out to 51 h for approximately 230 stations in the conterminous United States. A combination of LFM model output, surface weather observations and climatic factors were used in this development. We derived three sets of temperature prediction equations for both the 0000 and 1200 GMT forecast cycle as follows: 1) max/min equations for the first (24 h) period and 3 h equations for projections of 6 to 27 h;2) max/min equations for the second (36 h) period and 3 h equations for projections to 27 to 39 h; and 3) max/min equations for the third (48 h) period and 3 h equations for projections of 39 to 51 h. To enhance consistency among the various max (or min) and 3 h forecasts, all the equations within each set are comprised of the same 10 predictors. We also derived a separate set of 60 h max/min equations.
Comparative verification indicates that, in sharp contrast to past results, max/min forecasts from the new early guidance system are now better than those from the final guidance system. In addition, the automated 3 h temperature predictions are superior to persistence forecasts based on 3 to 6 h old temperature observations, as well as to persistence forecasts based on reports taken 24 h earlier.
