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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 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 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
: BNOC Operations Bulletin Number 105: APS2 upgrade to the ACCESS-G numerical weather prediction system. Tech. Rep. 105, Bureau National Operations Centre, 32 pp., http://www.bom.gov.au/australia/charts/bulletins/APOB105.pdf . Bauer , P. , A. Thorpe , and G. Brunet , 2015 : The quiet revolution of numerical weather prediction . Nature , 525 , 47 – 55 , https://doi.org/10.1038/nature14956 . 10.1038/nature14956 Beggs , H. , 2008 : GAMSSA–A new Global Australian Multi-Sensor SST Analysis
: BNOC Operations Bulletin Number 105: APS2 upgrade to the ACCESS-G numerical weather prediction system. Tech. Rep. 105, Bureau National Operations Centre, 32 pp., http://www.bom.gov.au/australia/charts/bulletins/APOB105.pdf . Bauer , P. , A. Thorpe , and G. Brunet , 2015 : The quiet revolution of numerical weather prediction . Nature , 525 , 47 – 55 , https://doi.org/10.1038/nature14956 . 10.1038/nature14956 Beggs , H. , 2008 : GAMSSA–A new Global Australian Multi-Sensor SST Analysis
of water vapor along the ray path ( Bengtsson et al. 2003 ). Generally, slow variations in the ZTD are due to the hydrostatic component, whereas more rapid changes are due to water vapor variations ( Poli et al. 2007 ). The impact of assimilating ZTD observations in numerical weather prediction (NWP) models has previously been described by authors such as Yan et al. (2009) , Boniface et al. (2009) , Macpherson et al. (2008) , Poli et al. (2007) , Faccani et al. (2005) , and Vedel and Huang
of water vapor along the ray path ( Bengtsson et al. 2003 ). Generally, slow variations in the ZTD are due to the hydrostatic component, whereas more rapid changes are due to water vapor variations ( Poli et al. 2007 ). The impact of assimilating ZTD observations in numerical weather prediction (NWP) models has previously been described by authors such as Yan et al. (2009) , Boniface et al. (2009) , Macpherson et al. (2008) , Poli et al. (2007) , Faccani et al. (2005) , and Vedel and Huang
1. Introduction Verification of numerical weather prediction (NWP) models serves several purposes, some direct, others indirect. One direct purpose is to identify and quantify a model’s deficiencies so they can be understood and remedied. Even deficiencies that are not understood can be at least partially neutralized if they are systematic. For instance, a model’s biased temperature can be automatically adjusted during the postprocessing of a forecast. Adjustments or compensations such as this
1. Introduction Verification of numerical weather prediction (NWP) models serves several purposes, some direct, others indirect. One direct purpose is to identify and quantify a model’s deficiencies so they can be understood and remedied. Even deficiencies that are not understood can be at least partially neutralized if they are systematic. For instance, a model’s biased temperature can be automatically adjusted during the postprocessing of a forecast. Adjustments or compensations such as this
( Williams 2017 ), understanding turbulence and properly predicting its occurrence has become an important goal of the aviation industry and meteorology in order to minimize turbulence-related damage. Current operational aviation turbulence forecasting methods, including the Graphical Turbulence Guidance (GTG; Sharman et al. 2006 ; Sharman and Pearson 2017 ) and Korean aviation Turbulence Guidance (KTG; Kim and Chun 2012a ; Lee and Chun 2018 ), have been developed using numerical weather prediction
( Williams 2017 ), understanding turbulence and properly predicting its occurrence has become an important goal of the aviation industry and meteorology in order to minimize turbulence-related damage. Current operational aviation turbulence forecasting methods, including the Graphical Turbulence Guidance (GTG; Sharman et al. 2006 ; Sharman and Pearson 2017 ) and Korean aviation Turbulence Guidance (KTG; Kim and Chun 2012a ; Lee and Chun 2018 ), have been developed using numerical weather prediction
During the past 70 years, numerical weather prediction (NWP) has advanced from infancy to a mature, highly skillful technology that is essential for both the economy and the protection of life and property. It is also a crucial tool for dealing with extreme weather. Operational NWP was first realized in the United States, and the nation was the world leader for several decades. But during recent decades, U.S. operational NWP efforts have fallen behind the state of the science and leading
During the past 70 years, numerical weather prediction (NWP) has advanced from infancy to a mature, highly skillful technology that is essential for both the economy and the protection of life and property. It is also a crucial tool for dealing with extreme weather. Operational NWP was first realized in the United States, and the nation was the world leader for several decades. But during recent decades, U.S. operational NWP efforts have fallen behind the state of the science and leading
