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. The lateral boundary conditions on the 27-km domain are specified from operational forecasts of the Naval Operational Global Atmospheric Prediction System (NOGAPS) and perturbed for each ensemble member using the fixed-covariance-perturbation method described in Torn et al. (2006) . The perturbations are Gaussian with zero mean and a covariance that is balanced for synoptic-scale motions. The covariance relations are obtained from the Weather Research and Forecasting-Variational (WRF-VAR) data
. The lateral boundary conditions on the 27-km domain are specified from operational forecasts of the Naval Operational Global Atmospheric Prediction System (NOGAPS) and perturbed for each ensemble member using the fixed-covariance-perturbation method described in Torn et al. (2006) . The perturbations are Gaussian with zero mean and a covariance that is balanced for synoptic-scale motions. The covariance relations are obtained from the Weather Research and Forecasting-Variational (WRF-VAR) data
crest by 9% relative to that in the continuous solution. Representative dimensional parameters for this problem are given on line 2 of Table 2 and are typical of resolutions that may be present in operational NWP forecasts. The gridpoint locations relative to the mountain are shown in Fig. 1 and are identical to those in the previously considered Ī“ = 1.8 case. A less well-resolved case is shown in Fig. 4c , in which the normalized horizontal resolution is Ī x ā² = 1.35, corresponding to
crest by 9% relative to that in the continuous solution. Representative dimensional parameters for this problem are given on line 2 of Table 2 and are typical of resolutions that may be present in operational NWP forecasts. The gridpoint locations relative to the mountain are shown in Fig. 1 and are identical to those in the previously considered Ī“ = 1.8 case. A less well-resolved case is shown in Fig. 4c , in which the normalized horizontal resolution is Ī x ā² = 1.35, corresponding to
1. Introduction The Sierra Nevada range is a well-known source of strong mountain waves, downslope windstorms, and turbulence associated with lee-wave rotors, which represent hazards to aviation, residents, and property and are difficult for forecasters to predict ( Holmboe and Klieforth 1957 ; Grubisic and Lewis 2004 ). Continued increase in the resolution of operational numerical weather prediction (NWP) models is expected to improve forecasts as the phenomena become more explicitly resolved
1. Introduction The Sierra Nevada range is a well-known source of strong mountain waves, downslope windstorms, and turbulence associated with lee-wave rotors, which represent hazards to aviation, residents, and property and are difficult for forecasters to predict ( Holmboe and Klieforth 1957 ; Grubisic and Lewis 2004 ). Continued increase in the resolution of operational numerical weather prediction (NWP) models is expected to improve forecasts as the phenomena become more explicitly resolved
. Lateral boundary conditions for the outer most grid mesh are based on the Navy Operational Global Analysis and Prediction System (NOGAPS) forecast fields. Two types of real data forecasts and simulations are performed in this study. The first set of COAMPS forecasts was performed in real time using three horizontally nested grid meshes of 91 Ć 91, 133 Ć 133, and 157 Ć 157 grid points with horizontal grid increments on the computational meshes of 18 km, 6 km, and 2 km, respectively. The real
. Lateral boundary conditions for the outer most grid mesh are based on the Navy Operational Global Analysis and Prediction System (NOGAPS) forecast fields. Two types of real data forecasts and simulations are performed in this study. The first set of COAMPS forecasts was performed in real time using three horizontally nested grid meshes of 91 Ć 91, 133 Ć 133, and 157 Ć 157 grid points with horizontal grid increments on the computational meshes of 18 km, 6 km, and 2 km, respectively. The real
western Nevada that resulted in extensive damage. For both cases, the model was able to capture the mountain waves believed to be responsible for the high winds. The operational Eta Model, in comparison, failed to forecast these high wind events, leading to the conclusion that a grid spacing of 5 km or less is necessary to predict high wind events in the complex terrain of the Sierra Nevada. The severe windstorms in the lee of the Sierra Nevada are generally believed to be associated with mountain
western Nevada that resulted in extensive damage. For both cases, the model was able to capture the mountain waves believed to be responsible for the high winds. The operational Eta Model, in comparison, failed to forecast these high wind events, leading to the conclusion that a grid spacing of 5 km or less is necessary to predict high wind events in the complex terrain of the Sierra Nevada. The severe windstorms in the lee of the Sierra Nevada are generally believed to be associated with mountain
Harshvardhan et al. (1987) . The initial fields for the model are created from multivariate optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data that are quality controlled and blended with the 12-h COAMPS forecast fields. Lateral boundary conditions for the outermost grid mesh are derived from Navy Operational Global Analysis and Prediction System (NOGAPS) forecast fields. The computational domain contains four horizontally nested grid meshes (i.e., one
Harshvardhan et al. (1987) . The initial fields for the model are created from multivariate optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data that are quality controlled and blended with the 12-h COAMPS forecast fields. Lateral boundary conditions for the outermost grid mesh are derived from Navy Operational Global Analysis and Prediction System (NOGAPS) forecast fields. The computational domain contains four horizontally nested grid meshes (i.e., one
operational WRF-NMM at NCEP . Preprints, 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, Washington, DC, Amer. Meteor. Soc., 4B.4. [Available online at http://ams.confex.com/ams/pdfpapers/94734.pdf .] Bretherton , F. P. , 1969 : Momentum transport by gravity waves . Quart. J. Roy. Meteor. Soc. , 95 , 213 ā 243 . Brinkman , W. A. R. , 1974 : Strong downslope winds at Boulder . Mon. Wea. Rev. , 102 , 592 ā 602 . Bryan , G. H. , and J. M. Fritsch
operational WRF-NMM at NCEP . Preprints, 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, Washington, DC, Amer. Meteor. Soc., 4B.4. [Available online at http://ams.confex.com/ams/pdfpapers/94734.pdf .] Bretherton , F. P. , 1969 : Momentum transport by gravity waves . Quart. J. Roy. Meteor. Soc. , 95 , 213 ā 243 . Brinkman , W. A. R. , 1974 : Strong downslope winds at Boulder . Mon. Wea. Rev. , 102 , 592 ā 602 . Bryan , G. H. , and J. M. Fritsch
optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data sources that are quality controlled and blended with the 12-h COAMPS forecast fields. Lateral boundary conditions for the outermost grid mesh are derived from Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast fields. The 36-h forecast period runs from 0000 UTC 25 March to 1200 UTC 26 March with a data output frequency of 5 min. b. Aerosol model A dust microphysical aerosol model
optimum interpolation analysis of upper-air sounding, surface, commercial aircraft, and satellite data sources that are quality controlled and blended with the 12-h COAMPS forecast fields. Lateral boundary conditions for the outermost grid mesh are derived from Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast fields. The 36-h forecast period runs from 0000 UTC 25 March to 1200 UTC 26 March with a data output frequency of 5 min. b. Aerosol model A dust microphysical aerosol model
forecasters, climatologists, mesoscale and regional climate modelers, and for selecting optimal sites and timing of observational field campaigns, 1 existence of such climatologies is clearly important. Additionally, availability of climatologies for different mountain ranges that are known for the generation of lee waves ( Auer 1992 ; Mitchell et al. 1990 ; Smith 1976 ; Vosper and Mobbs 1996 ) would allow for easier generalization of physical process study findings obtained in one region to other
forecasters, climatologists, mesoscale and regional climate modelers, and for selecting optimal sites and timing of observational field campaigns, 1 existence of such climatologies is clearly important. Additionally, availability of climatologies for different mountain ranges that are known for the generation of lee waves ( Auer 1992 ; Mitchell et al. 1990 ; Smith 1976 ; Vosper and Mobbs 1996 ) would allow for easier generalization of physical process study findings obtained in one region to other
accurately deriving the VV from the sonde data. First, the high-resolution (preferably 1 s) radiosonde data should be recorded in all operational stations to accurately derive the riseāfall rates using the pressure tendency. Note that all current radiosonde archives only contain data at mandatory and significant pressure levels. The high-resolution radiosonde data are recorded for field or special projects, such as the U.S. high-resolution radiosonde dataset as part of the Stratospheric Processes and
accurately deriving the VV from the sonde data. First, the high-resolution (preferably 1 s) radiosonde data should be recorded in all operational stations to accurately derive the riseāfall rates using the pressure tendency. Note that all current radiosonde archives only contain data at mandatory and significant pressure levels. The high-resolution radiosonde data are recorded for field or special projects, such as the U.S. high-resolution radiosonde dataset as part of the Stratospheric Processes and
