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Validation of a Water Vapor Micropulse Differential Absorption Lidar (DIAL)1996 ; Weckwerth et al. 1996 ; Weckwerth 2000 ; Lin et al. 2011 ). Forecasters have noted the value of real-time GPS precipitable water vapor (PWV) observations in detecting rapid moisture increases to improve their high-impact weather forecasting skill (e.g., Moore et al. 2015 ). When humidity profiles from a microwave radiometer profiler (MWRP) and Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004a , b ) were assimilated, along with temperature and wind profiles
1996 ; Weckwerth et al. 1996 ; Weckwerth 2000 ; Lin et al. 2011 ). Forecasters have noted the value of real-time GPS precipitable water vapor (PWV) observations in detecting rapid moisture increases to improve their high-impact weather forecasting skill (e.g., Moore et al. 2015 ). When humidity profiles from a microwave radiometer profiler (MWRP) and Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004a , b ) were assimilated, along with temperature and wind profiles
event is improved compared to assimilating these observations using rawinsonde errors (not shown). Geer and Bauer (2011) and Minamide and Zhang (2017) use a similar approach to inflate observation error covariances for microwave imager radiances. This technique is only meant as a preliminary method for assimilating the AERI and Doppler lidar observations. In the future, we plan to further develop an optimal method for determining observation errors for these instruments. c. Design of the model
event is improved compared to assimilating these observations using rawinsonde errors (not shown). Geer and Bauer (2011) and Minamide and Zhang (2017) use a similar approach to inflate observation error covariances for microwave imager radiances. This technique is only meant as a preliminary method for assimilating the AERI and Doppler lidar observations. In the future, we plan to further develop an optimal method for determining observation errors for these instruments. c. Design of the model
; Mueller et al. 2017 ) and microwave radiometers ( Knupp 2006 ; Coleman and Knupp 2011 ) are quite useful. The AERIs measure downwelling spectral infrared radiance, from which profiles of temperature and humidity are retrieved ( Turner and Löhnert 2014 ). Similarly, thermodynamic profiles can also be retrieved from the observations made by microwave radiometers, which measure downwelling microwave radiation at multiple microwave frequencies ( Solheim et al. 1998 ). Both of these passive remote sensors
; Mueller et al. 2017 ) and microwave radiometers ( Knupp 2006 ; Coleman and Knupp 2011 ) are quite useful. The AERIs measure downwelling spectral infrared radiance, from which profiles of temperature and humidity are retrieved ( Turner and Löhnert 2014 ). Similarly, thermodynamic profiles can also be retrieved from the observations made by microwave radiometers, which measure downwelling microwave radiation at multiple microwave frequencies ( Solheim et al. 1998 ). Both of these passive remote sensors
: Mesoscale weather prediction with the RUC hybrid isentropic–terrain-following coordinate model . Mon. Wea. Rev. , 132 , 473 – 494 , doi: 10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2 . 10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2 Blumberg , W. G. , D. D. Turner , U. Löhnert , and S. Castleberry , 2015 : Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part II: Actual retrieval performance in clear-sky and cloudy conditions . J. Appl
: Mesoscale weather prediction with the RUC hybrid isentropic–terrain-following coordinate model . Mon. Wea. Rev. , 132 , 473 – 494 , doi: 10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2 . 10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2 Blumberg , W. G. , D. D. Turner , U. Löhnert , and S. Castleberry , 2015 : Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part II: Actual retrieval performance in clear-sky and cloudy conditions . J. Appl
, and NLLJs. Primary data sources for NLLJ cases were the boundary layer profiles measured by mobile and fixed PECAN Integrated Sounding Arrays (PISAs). These datasets included profiles of dynamic and thermodynamic parameters obtained at high temporal and vertical resolution using Doppler lidars, Atmospheric Emitted Radiance Interferometers (AERIs), radar wind profilers, radiosondes, and microwave radiometers (MWRs), providing observations to describe the SBL and NLLJ evolution. There were four
, and NLLJs. Primary data sources for NLLJ cases were the boundary layer profiles measured by mobile and fixed PECAN Integrated Sounding Arrays (PISAs). These datasets included profiles of dynamic and thermodynamic parameters obtained at high temporal and vertical resolution using Doppler lidars, Atmospheric Emitted Radiance Interferometers (AERIs), radar wind profilers, radiosondes, and microwave radiometers (MWRs), providing observations to describe the SBL and NLLJ evolution. There were four
bores will change their characteristics over the course of their lifetimes. Koch et al. (2008) used a combination of observations and numerical simulations to identify changes in the turbulent nature of the bore over the course of its life cycle. They identified that the majority of turbulent kinetic energy is generated by the shear stress from the strong along-bore flow associated with the low-level jet (LLJ). Additionally, they found that early in the life cycle of the bore, in what they called
bores will change their characteristics over the course of their lifetimes. Koch et al. (2008) used a combination of observations and numerical simulations to identify changes in the turbulent nature of the bore over the course of its life cycle. They identified that the majority of turbulent kinetic energy is generated by the shear stress from the strong along-bore flow associated with the low-level jet (LLJ). Additionally, they found that early in the life cycle of the bore, in what they called
The PECAN field campaign assembled a rich array of observations from lower-tropospheric profiling systems, mobile radars and mesonets, and aircraft over the Great Plains during June–July 2015 to better understand nocturnal mesoscale convective systems and their relationship with the stable boundary layer, the low-level jet, and atmospheric bores. Large parts of the central Great Plains witness a nocturnal maximum in the frequency of thunder storms and convective precipitation in summer ( Kincer
The PECAN field campaign assembled a rich array of observations from lower-tropospheric profiling systems, mobile radars and mesonets, and aircraft over the Great Plains during June–July 2015 to better understand nocturnal mesoscale convective systems and their relationship with the stable boundary layer, the low-level jet, and atmospheric bores. Large parts of the central Great Plains witness a nocturnal maximum in the frequency of thunder storms and convective precipitation in summer ( Kincer
develop in sequence and appear as an amplitude-ordered group of waves, referred to as a soliton (e.g., Knupp 2006 ). An unambiguous distinction of the passage of an outflow boundary as a density current, bore, or soliton from radar reflectivity maps and surface observations alone is difficult ( Haghi et al. 2017 ). A density current appears as a single radar “fine line,” while an undular bore usually reveals multiple parallel fine lines from additional regions of convergence along solitary waves. The
develop in sequence and appear as an amplitude-ordered group of waves, referred to as a soliton (e.g., Knupp 2006 ). An unambiguous distinction of the passage of an outflow boundary as a density current, bore, or soliton from radar reflectivity maps and surface observations alone is difficult ( Haghi et al. 2017 ). A density current appears as a single radar “fine line,” while an undular bore usually reveals multiple parallel fine lines from additional regions of convergence along solitary waves. The
Differential Reflectivity Calibration and Antenna Temperature1984 ); the radome, especially a wet radome, or a wet antenna can affect the microwaves; ground clutter from sidelobes can contaminate the radar signals; and the antenna shape, when pointing vertically as compared to pointing horizontally, may be different. Other issues, such as receiver saturation or hardware and signal processing errors, can also bias measurements. Despite all this, most of these possible error sources can be mitigated through careful measurements and by rotating the vertically
1984 ); the radome, especially a wet radome, or a wet antenna can affect the microwaves; ground clutter from sidelobes can contaminate the radar signals; and the antenna shape, when pointing vertically as compared to pointing horizontally, may be different. Other issues, such as receiver saturation or hardware and signal processing errors, can also bias measurements. Despite all this, most of these possible error sources can be mitigated through careful measurements and by rotating the vertically
