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This article is the first † of two brief reports on the activities and results of the Joint Numerical Weather Prediction Unit since the inauguration of routine numerical forecasting in May 1955. Following a broad statement of the Unit's objectives and a short chronology of the main changes of procedure over the past year, a description is given in general terms of the data processing and numerical forecasting routines of the JNWP Unit, together with the content and form of the numerical forecasts. The second report will deal with the accuracy and typical errors of such forecasts, as well as with the JNWP Unit's efforts to improve them.
This article is the first † of two brief reports on the activities and results of the Joint Numerical Weather Prediction Unit since the inauguration of routine numerical forecasting in May 1955. Following a broad statement of the Unit's objectives and a short chronology of the main changes of procedure over the past year, a description is given in general terms of the data processing and numerical forecasting routines of the JNWP Unit, together with the content and form of the numerical forecasts. The second report will deal with the accuracy and typical errors of such forecasts, as well as with the JNWP Unit's efforts to improve them.
This is the second of two brief reports on the activities and results of the Joint Numerical Weather Prediction Unit since May 1955, and is concerned primarily with the accuracy and characteristic errors of the numerical forecasts described in the previous report. The quality of the barotropic and 3-level forecasts has been measured by several statistical indices of error, and compared with that of the subjective forecasts issued by the National Weather Analysis Center. A breakdown of these statistics shows the dependence of forecasting accuracy on length of forecast period, level, data coverage, and proximity of lateral boundaries. Various sources of systematic error are discussed with reference to the JNWP Unit's efforts to isolate and remedy them.
After almost a year of experimentation and operational numerical weather forecasting, it is concluded that the quality of the numerical 500 millibar forecasts is not significantly different from that of the best subjective forecasts prepared by methods in current use. Recent results indicate that a significant improvement can be expected in the near future. The numerical 1000 mb forecasts are worse, but recent changes of model show promise of matching the performance of subjective methods. Finally, the most glaring systematic errors of the present numerical forecasts have adequate explanation in existing theory, and can be (or have already been) corrected by generalization of the models.
This is the second of two brief reports on the activities and results of the Joint Numerical Weather Prediction Unit since May 1955, and is concerned primarily with the accuracy and characteristic errors of the numerical forecasts described in the previous report. The quality of the barotropic and 3-level forecasts has been measured by several statistical indices of error, and compared with that of the subjective forecasts issued by the National Weather Analysis Center. A breakdown of these statistics shows the dependence of forecasting accuracy on length of forecast period, level, data coverage, and proximity of lateral boundaries. Various sources of systematic error are discussed with reference to the JNWP Unit's efforts to isolate and remedy them.
After almost a year of experimentation and operational numerical weather forecasting, it is concluded that the quality of the numerical 500 millibar forecasts is not significantly different from that of the best subjective forecasts prepared by methods in current use. Recent results indicate that a significant improvement can be expected in the near future. The numerical 1000 mb forecasts are worse, but recent changes of model show promise of matching the performance of subjective methods. Finally, the most glaring systematic errors of the present numerical forecasts have adequate explanation in existing theory, and can be (or have already been) corrected by generalization of the models.
1. Introduction Modern numerical weather prediction (NWP) models offer capabilities to simulate radar reflectivity from the output of NWP models, such as single-level radar reflectivity and composite reflectivity (CR) (the maximum reflectivity in a grid column). These simulated radar reflectivity products are not only a means to display more details of the temporal and spatial characteristics of convective weather systems ( Koch et al. 2005 ) and the thickness and height of clouds but also an
1. Introduction Modern numerical weather prediction (NWP) models offer capabilities to simulate radar reflectivity from the output of NWP models, such as single-level radar reflectivity and composite reflectivity (CR) (the maximum reflectivity in a grid column). These simulated radar reflectivity products are not only a means to display more details of the temporal and spatial characteristics of convective weather systems ( Koch et al. 2005 ) and the thickness and height of clouds but also an
predictions in high-resolution configurations. Reduction of the tropical cyclone weak-intensity bias is important for both high-impact weather forecasts and longer-range predictions involving tropical–extratropical interactions ( Keller et al. 2019 ). Tropical cyclones also represent a stress-test for model formulations, with improved predictions an indication that the model better reproduces atmospheric extremes. In combination, these factors suggest that the proposed reduction of numerical dissipation
predictions in high-resolution configurations. Reduction of the tropical cyclone weak-intensity bias is important for both high-impact weather forecasts and longer-range predictions involving tropical–extratropical interactions ( Keller et al. 2019 ). Tropical cyclones also represent a stress-test for model formulations, with improved predictions an indication that the model better reproduces atmospheric extremes. In combination, these factors suggest that the proposed reduction of numerical dissipation
largest sources of uncertainty in general circulations models (GCMs) and numerical weather prediction (NWP) models ( Klein and Jakob 1999 ; Jiang et al. 2012 ; IPCC 2013 ). In atmospheric models, clouds and precipitation are represented by a combination of physical parameterizations that are each targeted at a specific subset of moist processes. These include “implicit” (subgrid-scale) clouds generated by the boundary layer scheme and the convection parameterization—which is often subdivided into
largest sources of uncertainty in general circulations models (GCMs) and numerical weather prediction (NWP) models ( Klein and Jakob 1999 ; Jiang et al. 2012 ; IPCC 2013 ). In atmospheric models, clouds and precipitation are represented by a combination of physical parameterizations that are each targeted at a specific subset of moist processes. These include “implicit” (subgrid-scale) clouds generated by the boundary layer scheme and the convection parameterization—which is often subdivided into
DEPARTMENT OF COUMERCESINCLAIR WEEKS, SecretaryWEATHER BUREAUF. W. REICHELDERFER, ChiefMONTHLY WEATHER REVIEWJAMES E. CASKEY, m., EditorVolume 85 Closed December 15, 1957 Number 10 Issued January 15, 1958 OCTOBER 1957NUMERICAL METHODS IN WEATHER PREDICTION:I. THE BALANCE EQUATION*FREDERICK G. SHUMAN, U. S. Weather BureauJoint Numerical Weather Prediction Unit, Suitland, Md.[Manuscript received June 28, 1957; revised October 25, 1957ABSTRACTTwo methods of solving the balance equation are
DEPARTMENT OF COUMERCESINCLAIR WEEKS, SecretaryWEATHER BUREAUF. W. REICHELDERFER, ChiefMONTHLY WEATHER REVIEWJAMES E. CASKEY, m., EditorVolume 85 Closed December 15, 1957 Number 10 Issued January 15, 1958 OCTOBER 1957NUMERICAL METHODS IN WEATHER PREDICTION:I. THE BALANCE EQUATION*FREDERICK G. SHUMAN, U. S. Weather BureauJoint Numerical Weather Prediction Unit, Suitland, Md.[Manuscript received June 28, 1957; revised October 25, 1957ABSTRACTTwo methods of solving the balance equation are
1. Introduction a. Background and motivation Many government agencies, university groups, and private-sector companies run computationally expensive numerical weather prediction (NWP) models, some in real time. Some universities run daily NWP models under contract to bring in funding that pays for student salaries and hardware, to produce forecasts for field campaigns and experiments, and for teaching. Two examples of universities that run real-time forecasts in the Pacific Northwest are the
1. Introduction a. Background and motivation Many government agencies, university groups, and private-sector companies run computationally expensive numerical weather prediction (NWP) models, some in real time. Some universities run daily NWP models under contract to bring in funding that pays for student salaries and hardware, to produce forecasts for field campaigns and experiments, and for teaching. Two examples of universities that run real-time forecasts in the Pacific Northwest are the
1. Introduction Ensemble prediction has become one of the most important components of numerical weather prediction (NWP) ( Buizza et al. 2018 ). Many methods have been proposed for perturbing initial conditions and models (e.g., Tracton and Kalnay 1993 ; Houtekamer et al. 1996 ; Chen et al. 2002 ; Shutts 2005 ; Ma et al. 2008 , 2015 ; Berner et al. 2009 ; Kazuo et al. 2012 ; Ollinaho et al. 2017 ; Feng et al. 2014 , 2018 ). For a complete review of current ensemble methods, readers
1. Introduction Ensemble prediction has become one of the most important components of numerical weather prediction (NWP) ( Buizza et al. 2018 ). Many methods have been proposed for perturbing initial conditions and models (e.g., Tracton and Kalnay 1993 ; Houtekamer et al. 1996 ; Chen et al. 2002 ; Shutts 2005 ; Ma et al. 2008 , 2015 ; Berner et al. 2009 ; Kazuo et al. 2012 ; Ollinaho et al. 2017 ; Feng et al. 2014 , 2018 ). For a complete review of current ensemble methods, readers
1. Introduction The majority of operational global numerical weather prediction (NWP) models employ the semi-Lagrangian method for advection, which was pioneered by Robert (1981 , 1982) . Staniforth and Temperton (1986) have shown that a semi-implicit treatment of the linear terms in the dynamical equations responsible for the fast waves pertaining to gravitational oscillations permits the use of long time steps with semi-Lagrangian models. This helped the wide adoption of the semi
1. Introduction The majority of operational global numerical weather prediction (NWP) models employ the semi-Lagrangian method for advection, which was pioneered by Robert (1981 , 1982) . Staniforth and Temperton (1986) have shown that a semi-implicit treatment of the linear terms in the dynamical equations responsible for the fast waves pertaining to gravitational oscillations permits the use of long time steps with semi-Lagrangian models. This helped the wide adoption of the semi
1. Introduction In recent years, the increasing demand for accurate weather forecasts has led to a steady improvement of the skill of numerical weather predictions at both global and regional scales. Despite these improvements, such predictions are still affected by imperfect initial conditions, numerical approximations, and simplification (or altogether lack of representation) of the physical and chemical processes that govern the evolution of the atmosphere. These imperfections
1. Introduction In recent years, the increasing demand for accurate weather forecasts has led to a steady improvement of the skill of numerical weather predictions at both global and regional scales. Despite these improvements, such predictions are still affected by imperfect initial conditions, numerical approximations, and simplification (or altogether lack of representation) of the physical and chemical processes that govern the evolution of the atmosphere. These imperfections
