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-tropical cyclone development . Meteor. Appl. , 4 , 317 – 324 . Courant, R. , and Hilbert D. , 1962 : Methods of Mathematical Physics. Vol. 2. Wiley-Interscience 830 pp . Demirtas, M. , and Thorpe A. J. , 1999 : Sensitivity of short-range weather forecasts to local potential vorticity modifications . Mon. Wea. Rev. , 127 , 922 – 939 . Desroziers, G. , Berre L. , Chapnik B. , and Poli P. , 2005 : Diagnosis of observation, background and analysis-error statistics in observation space
-tropical cyclone development . Meteor. Appl. , 4 , 317 – 324 . Courant, R. , and Hilbert D. , 1962 : Methods of Mathematical Physics. Vol. 2. Wiley-Interscience 830 pp . Demirtas, M. , and Thorpe A. J. , 1999 : Sensitivity of short-range weather forecasts to local potential vorticity modifications . Mon. Wea. Rev. , 127 , 922 – 939 . Desroziers, G. , Berre L. , Chapnik B. , and Poli P. , 2005 : Diagnosis of observation, background and analysis-error statistics in observation space
Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS; Bauer et al. 2013 ). Case studies of severe weather events during the first operational year are described in section 5 . The last section presents a summary along with future perspectives. 2. Model configuration and modifications a. Model configuration The AROME-MetCoOp model covers large parts of the Nordic countries with a horizontal resolution of 2.5 km. More than half of its domain is over open water, that is, the
Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS; Bauer et al. 2013 ). Case studies of severe weather events during the first operational year are described in section 5 . The last section presents a summary along with future perspectives. 2. Model configuration and modifications a. Model configuration The AROME-MetCoOp model covers large parts of the Nordic countries with a horizontal resolution of 2.5 km. More than half of its domain is over open water, that is, the
, Kepert (2012) discussed the advantages and disadvantages of a variety of PBL schemes for TC simulations and concluded that caution needs to be taken when results are interpreted and that there might be need for tuning to compare with observations. The PBL scheme in the NCEP operational Hurricane Weather Research and Forecast (HWRF) Model has its roots in the PBL scheme of the NCEP Global Forecast System (GFS). The scheme uses a parametric profile of eddy diffusivity K matching the value in the
, Kepert (2012) discussed the advantages and disadvantages of a variety of PBL schemes for TC simulations and concluded that caution needs to be taken when results are interpreted and that there might be need for tuning to compare with observations. The PBL scheme in the NCEP operational Hurricane Weather Research and Forecast (HWRF) Model has its roots in the PBL scheme of the NCEP Global Forecast System (GFS). The scheme uses a parametric profile of eddy diffusivity K matching the value in the
of various physical processes are additional problems that contribute to reduced predictability of the NWP and call for additional processing by MOS. A major obstacle in implementing an MOS prediction system is the ongoing modification of NWP models. Improvements in the dynamic and data assimilation schemes, changes in the observation system, and refinements of the temporal and spatial resolution of the numerical solutions all contribute to changes in the NWP model characteristics. The weather
of various physical processes are additional problems that contribute to reduced predictability of the NWP and call for additional processing by MOS. A major obstacle in implementing an MOS prediction system is the ongoing modification of NWP models. Improvements in the dynamic and data assimilation schemes, changes in the observation system, and refinements of the temporal and spatial resolution of the numerical solutions all contribute to changes in the NWP model characteristics. The weather
” feed of base data. For example, the FAA would like to have its products generated from base data that have been processed with nationally consistent, aggressive clutter filtering parameters, while the NWS requires flexibility in these settings to prevent any degradation of rainfall estimation or weather detection through overly aggressive clutter filtering. In addition to such benefits as those discussed above, the ORDA is a necessary step to the possible future modification of adding a
” feed of base data. For example, the FAA would like to have its products generated from base data that have been processed with nationally consistent, aggressive clutter filtering parameters, while the NWS requires flexibility in these settings to prevent any degradation of rainfall estimation or weather detection through overly aggressive clutter filtering. In addition to such benefits as those discussed above, the ORDA is a necessary step to the possible future modification of adding a
work. 2. Modifications to PBL schemehereafter The various PBL scheme modifications as evaluated here are described below. All are implemented within the framework of the MYNN PBL scheme in the Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model version 3.8. a. New closure parameters for the SBL To define the mixing effects of turbulent eddy covariance in a mesoscale model, the MYNN PBL scheme uses a set of closure equations, in which a set of parameters determine the relative
work. 2. Modifications to PBL schemehereafter The various PBL scheme modifications as evaluated here are described below. All are implemented within the framework of the MYNN PBL scheme in the Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model version 3.8. a. New closure parameters for the SBL To define the mixing effects of turbulent eddy covariance in a mesoscale model, the MYNN PBL scheme uses a set of closure equations, in which a set of parameters determine the relative
1. Introduction Cumulus convection significantly influences the large-scale atmospheric circulation through latent heat release, vertical transports of heat, moisture, and momentum, and interaction with radiation ( Tiedtke 1988 ). However, most global atmospheric models for weather forecasts and climate predictions have horizontal grid spacing greater than the horizontal scale of cumulus convection; consequently, the effect of subgrid-scale convection on the large-scale flow should be
1. Introduction Cumulus convection significantly influences the large-scale atmospheric circulation through latent heat release, vertical transports of heat, moisture, and momentum, and interaction with radiation ( Tiedtke 1988 ). However, most global atmospheric models for weather forecasts and climate predictions have horizontal grid spacing greater than the horizontal scale of cumulus convection; consequently, the effect of subgrid-scale convection on the large-scale flow should be
1. Introduction As forecast technology advances, the challenges and opportunities associated with providing information about forecast uncertainty become both more germane and more complicated. While meteorologists have substantial information about forecast uncertainty—both in general and in specific situations—much of it is not easily accessible by the public ( Morss et al. 2008 ). In theory, uncertainty information is very useful to both weather forecasters and to the general public as
1. Introduction As forecast technology advances, the challenges and opportunities associated with providing information about forecast uncertainty become both more germane and more complicated. While meteorologists have substantial information about forecast uncertainty—both in general and in specific situations—much of it is not easily accessible by the public ( Morss et al. 2008 ). In theory, uncertainty information is very useful to both weather forecasters and to the general public as
1. Introduction One of the biggest challenges facing the current generation of high-resolution numerical weather prediction models is accurately forecasting the structure and evolution of the planetary boundary layer (PBL), which has direct impacts on forecasting sensible weather like low-level temperature, moisture, and winds, as well as instability and convective initiation (e.g., Marshall et al. 2003 ; Roebber et al. 2004 ; Hu et al. 2010 ; Coniglio et al. 2013 ). Because current models
1. Introduction One of the biggest challenges facing the current generation of high-resolution numerical weather prediction models is accurately forecasting the structure and evolution of the planetary boundary layer (PBL), which has direct impacts on forecasting sensible weather like low-level temperature, moisture, and winds, as well as instability and convective initiation (e.g., Marshall et al. 2003 ; Roebber et al. 2004 ; Hu et al. 2010 ; Coniglio et al. 2013 ). Because current models
1. Introduction The National Hurricane Center (NHC) of the National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) uses input from various operational hurricane prediction models to produce its official tropical cyclone (TC) forecasts. For track forecasts, these range from relatively simple statistical models such as Climatology and Persistence (CLIPER5) to sophisticated global and regional forecast models such as NOAA’s Global Forecast System (GFS), the NOAA
1. Introduction The National Hurricane Center (NHC) of the National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) uses input from various operational hurricane prediction models to produce its official tropical cyclone (TC) forecasts. For track forecasts, these range from relatively simple statistical models such as Climatology and Persistence (CLIPER5) to sophisticated global and regional forecast models such as NOAA’s Global Forecast System (GFS), the NOAA
