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( 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
-cloud-base height (hereinafter termed LCBH) observations, coupled with the cloud-base restrictions on flying, ensure that forecasting cloud properties in support of aviation remains a challenge. This study uses balloon-launched radiosonde data and surface observations from the three Australian East Antarctic coastal stations of Mawson, Davis, and Casey, along with near-coincident numerical weather prediction (NWP) forecasts, in order to determine the accuracy of the NWP relative humidity RH profile by comparing
-cloud-base height (hereinafter termed LCBH) observations, coupled with the cloud-base restrictions on flying, ensure that forecasting cloud properties in support of aviation remains a challenge. This study uses balloon-launched radiosonde data and surface observations from the three Australian East Antarctic coastal stations of Mawson, Davis, and Casey, along with near-coincident numerical weather prediction (NWP) forecasts, in order to determine the accuracy of the NWP relative humidity RH profile by comparing
1. Introduction Quantitative precipitation forecast (QPF) is an important goal of numerical weather prediction (NWP). Over the past decades, precipitation forecasts by NWP models have been remarkably improved owing to the increased number of atmospheric observations, improved NWP models, and advanced data assimilation (DA) methods. Precipitation nowcasting based on spatiotemporal extrapolation (hereafter, simply “extrapolation”) is also known to be a computationally feasible QPF method, which
1. Introduction Quantitative precipitation forecast (QPF) is an important goal of numerical weather prediction (NWP). Over the past decades, precipitation forecasts by NWP models have been remarkably improved owing to the increased number of atmospheric observations, improved NWP models, and advanced data assimilation (DA) methods. Precipitation nowcasting based on spatiotemporal extrapolation (hereafter, simply “extrapolation”) is also known to be a computationally feasible QPF method, which
success of these probabilistic forecast models, some questions remain about the usefulness of logistic models. Most importantly, neither study attempted to determine the potential impacts of random measurement error on the quality of the forecasts. In this paper, we assess the ability of logistic models to provide a valuable and accurate diagnosis/prediction of persistent contrail occurrence via numerical weather models under typical random errors expected in meteorological measurements. The next
success of these probabilistic forecast models, some questions remain about the usefulness of logistic models. Most importantly, neither study attempted to determine the potential impacts of random measurement error on the quality of the forecasts. In this paper, we assess the ability of logistic models to provide a valuable and accurate diagnosis/prediction of persistent contrail occurrence via numerical weather models under typical random errors expected in meteorological measurements. The next
-term forecasts at larger geographical scales that are required by energy grid managers to manage energy demand up to several days in advance. These forecasts require the use of numerical weather prediction (NWP) models of mesoscale size or greater, and therefore their development and implementation is usually carried about by national weather centers. Solar irradiance is a key surface flux for an NWP model. Correct computation of downward shortwave radiation is necessary for overall forecast accuracy and
-term forecasts at larger geographical scales that are required by energy grid managers to manage energy demand up to several days in advance. These forecasts require the use of numerical weather prediction (NWP) models of mesoscale size or greater, and therefore their development and implementation is usually carried about by national weather centers. Solar irradiance is a key surface flux for an NWP model. Correct computation of downward shortwave radiation is necessary for overall forecast accuracy and
. , 93 , 811 – 829 , https://doi.org/10.1175/BAMS-D-11-00052.1 . 10.1175/BAMS-D-11-00052.1 Rodwell , M. J. , D. S. Richardson , T. D. Hewson , and T. Haiden , 2010 : A new equitable score suitable for verifying precipitation in numerical weather prediction . Quart. J. Roy. Meteor. Soc. , 136 , 1344 – 1363 , https://doi.org/10.1002/qj.656 . 10.1002/qj.656 Schirmer , M. , and B. Jamieson , 2015 : Verification of analysed and forecasted winter precipitation in complex terrain
. , 93 , 811 – 829 , https://doi.org/10.1175/BAMS-D-11-00052.1 . 10.1175/BAMS-D-11-00052.1 Rodwell , M. J. , D. S. Richardson , T. D. Hewson , and T. Haiden , 2010 : A new equitable score suitable for verifying precipitation in numerical weather prediction . Quart. J. Roy. Meteor. Soc. , 136 , 1344 – 1363 , https://doi.org/10.1002/qj.656 . 10.1002/qj.656 Schirmer , M. , and B. Jamieson , 2015 : Verification of analysed and forecasted winter precipitation in complex terrain
1. Introduction There is a strong history of active collaboration between European meteorological institutes on numerical weather prediction (NWP) in order to develop and maintain numerical short-range weather forecasting systems for operational use. The international research program High Resolution Limited Area Model (HIRLAM) was initiated in 1985 and consists today of the National Meteorological Services (NMSs) from 10 countries: Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the
1. Introduction There is a strong history of active collaboration between European meteorological institutes on numerical weather prediction (NWP) in order to develop and maintain numerical short-range weather forecasting systems for operational use. The international research program High Resolution Limited Area Model (HIRLAM) was initiated in 1985 and consists today of the National Meteorological Services (NMSs) from 10 countries: Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the
potential for improving the simulated accuracy of a mesoscale numerical weather prediction model via this physics-based scheme has been illustrated through the cases of the one-dimensional viscous Burgers equation and the one-dimensional diffusion equation, as well as a series of simulations of successive severe weather events and their consecutive 365 twenty-four-hour simulations. In nature, the new scheme suggested in this paper resulted in decreased root-mean-square errors and improved forecasts in
potential for improving the simulated accuracy of a mesoscale numerical weather prediction model via this physics-based scheme has been illustrated through the cases of the one-dimensional viscous Burgers equation and the one-dimensional diffusion equation, as well as a series of simulations of successive severe weather events and their consecutive 365 twenty-four-hour simulations. In nature, the new scheme suggested in this paper resulted in decreased root-mean-square errors and improved forecasts in
1. Introduction Weather forecasting has improved dramatically since the introduction of numerical weather prediction (NWP) nearly six decades ago ( Bauer et al. 2015 ). This has been accomplished through ever-increasing computing power, improved models running at ever-increasing resolution with more accurate representation of atmospheric physical processes, and more sophisticated four-dimensional data assimilating algorithms that can better ingest ever-increasing volumes and quality of in situ
1. Introduction Weather forecasting has improved dramatically since the introduction of numerical weather prediction (NWP) nearly six decades ago ( Bauer et al. 2015 ). This has been accomplished through ever-increasing computing power, improved models running at ever-increasing resolution with more accurate representation of atmospheric physical processes, and more sophisticated four-dimensional data assimilating algorithms that can better ingest ever-increasing volumes and quality of in situ
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
