Search Results
1. Introduction Accurate rainfall estimates are crucial for numerous applications in hydrology, nowcasting, and mesoscale model validation. Ground-based operational weather radar networks are currently considered the only instruments capable of providing the requested high-resolution (1 km 2 ) and frequent (5 min) precipitation fields over mesoscale or even synoptic areas. The density of automated rain gauge networks is in general too scarce, especially in complex terrain, to yield the same
1. Introduction Accurate rainfall estimates are crucial for numerous applications in hydrology, nowcasting, and mesoscale model validation. Ground-based operational weather radar networks are currently considered the only instruments capable of providing the requested high-resolution (1 km 2 ) and frequent (5 min) precipitation fields over mesoscale or even synoptic areas. The density of automated rain gauge networks is in general too scarce, especially in complex terrain, to yield the same
climatological studies weighing factors such as mean wind speed and direction. Unfortunately, the impact on nearby radar sites is often not considered and there are few regulations requiring such an investigation. In fact, the only regulation in place is the Interim Policy on Proposed Windmill Farm Locations issued by the Department of Defense and the Department of Homeland Security ( American Wind Energy Association 2006 ). Current wind farms can be collections of 100 or more individual wind turbines
climatological studies weighing factors such as mean wind speed and direction. Unfortunately, the impact on nearby radar sites is often not considered and there are few regulations requiring such an investigation. In fact, the only regulation in place is the Interim Policy on Proposed Windmill Farm Locations issued by the Department of Defense and the Department of Homeland Security ( American Wind Energy Association 2006 ). Current wind farms can be collections of 100 or more individual wind turbines
1. Introduction The current generation of Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars in the United States is more than 20 years of age ( Yussouf and Stensrud 2008 ). Despite recent major improvements to the network (such as dual-polarization capabilities; Doviak et al. 2000 ), there are a number of potential enhancements that are currently being explored as researchers look toward the future of weather radar observations. Of key importance to National Weather Service
1. Introduction The current generation of Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars in the United States is more than 20 years of age ( Yussouf and Stensrud 2008 ). Despite recent major improvements to the network (such as dual-polarization capabilities; Doviak et al. 2000 ), there are a number of potential enhancements that are currently being explored as researchers look toward the future of weather radar observations. Of key importance to National Weather Service
1. Introduction Close proximity to quickly evolving severe weather phenomena for high-resolution data collection is difficult to achieve with traditional fixed-site radars. Thus, mobile weather radar systems are used to increase the likelihood of near-storm observations. Mobile radars are a common sight in midwestern regions when the potential for high-impact weather events is significant. One of the original mobile systems was the Center for Severe Weather Research (CSWR) Doppler on Wheels
1. Introduction Close proximity to quickly evolving severe weather phenomena for high-resolution data collection is difficult to achieve with traditional fixed-site radars. Thus, mobile weather radar systems are used to increase the likelihood of near-storm observations. Mobile radars are a common sight in midwestern regions when the potential for high-impact weather events is significant. One of the original mobile systems was the Center for Severe Weather Research (CSWR) Doppler on Wheels
1. Introduction Near-surface atmospheric refractivity was first retrieved using conventional weather radar by Fabry et al. (1997) and Fabry (2004) on McGill University’s S-band radar. Since that innovation, radar refractivity experiments have been conducted in the Oklahoma Panhandle ( Weckwerth et al. 2005 ; Fabry 2006 ; Wakimoto and Murphey 2009 ), northeast Colorado ( Roberts et al. 2008 ), and southwest and central Oklahoma ( Cheong et al. 2008 ; Heinselman et al. 2009 ; Bodine et al
1. Introduction Near-surface atmospheric refractivity was first retrieved using conventional weather radar by Fabry et al. (1997) and Fabry (2004) on McGill University’s S-band radar. Since that innovation, radar refractivity experiments have been conducted in the Oklahoma Panhandle ( Weckwerth et al. 2005 ; Fabry 2006 ; Wakimoto and Murphey 2009 ), northeast Colorado ( Roberts et al. 2008 ), and southwest and central Oklahoma ( Cheong et al. 2008 ; Heinselman et al. 2009 ; Bodine et al
Estimating Rain Microphysical Characteristics Using S-Band Dual-Polarization Radar in South Korea1. Introduction Raindrop size distributions (DSDs) are highly useful for understanding rain microphysics, estimating rainfall, and improving microphysical parameterization in numerical weather prediction (NWP) models ( Steiner et al. 2004 ). Rain DSDs can be characterized through disdrometer and dual-polarization radar measurements. DSDs observed with a disdrometer can then be represented by various DSD models, such as exponential or gamma models. However, exponential DSD models containing two
1. Introduction Raindrop size distributions (DSDs) are highly useful for understanding rain microphysics, estimating rainfall, and improving microphysical parameterization in numerical weather prediction (NWP) models ( Steiner et al. 2004 ). Rain DSDs can be characterized through disdrometer and dual-polarization radar measurements. DSDs observed with a disdrometer can then be represented by various DSD models, such as exponential or gamma models. However, exponential DSD models containing two
1. Introduction In recent years, an effort has been made to assimilate radar observations (of both reflectivity and radial velocity) into numerical weather prediction (NWP) models (see the reviews of Errico et al. 2000 ; MacPherson et al. 2003 ; Sun and Wilson 2003 ; Sun 2005a ). Moreover, as the resolution of NWP models increases, denser observations are required for assimilation, and the resolution and coverage of data from radar networks make them very attractive for this purpose. From
1. Introduction In recent years, an effort has been made to assimilate radar observations (of both reflectivity and radial velocity) into numerical weather prediction (NWP) models (see the reviews of Errico et al. 2000 ; MacPherson et al. 2003 ; Sun and Wilson 2003 ; Sun 2005a ). Moreover, as the resolution of NWP models increases, denser observations are required for assimilation, and the resolution and coverage of data from radar networks make them very attractive for this purpose. From
1. Introduction By applying radar to atmospheric remote sensing, atmospheric parameters can be derived after processing the received signals. Return power variations and Doppler shifts are caused by fluctuations in the atmospheric refractive index that are in turn affected by humidity, pressure, temperature, and mass density ( Doviak and Zrnić 1993 ). Atmospheric remote sensing using radar has been extensively studied for many years by using conventional pulsed radars and Doppler radar
1. Introduction By applying radar to atmospheric remote sensing, atmospheric parameters can be derived after processing the received signals. Return power variations and Doppler shifts are caused by fluctuations in the atmospheric refractive index that are in turn affected by humidity, pressure, temperature, and mass density ( Doviak and Zrnić 1993 ). Atmospheric remote sensing using radar has been extensively studied for many years by using conventional pulsed radars and Doppler radar
1. Introduction Dual-polarization radar provides the capability to discriminate between meteorological and nonmeteorological scatterers (e.g., Zrnić and Ryzhkov 1999 ), which has an important application for tornado detection. The random orientations, irregular shapes, and wide range of dielectric constants and sizes of lofted tornadic debris produce a unique polarimetric signature called the tornadic debris signature (TDS; Ryzhkov et al. 2002 , 2005 ). These scattering characteristics
1. Introduction Dual-polarization radar provides the capability to discriminate between meteorological and nonmeteorological scatterers (e.g., Zrnić and Ryzhkov 1999 ), which has an important application for tornado detection. The random orientations, irregular shapes, and wide range of dielectric constants and sizes of lofted tornadic debris produce a unique polarimetric signature called the tornadic debris signature (TDS; Ryzhkov et al. 2002 , 2005 ). These scattering characteristics
1. Introduction As the current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network approaches the end of its expected lifetime ( Yussouf and Stensrud 2008 ), numerous studies have raised several opportunities for improvement in a future network. Of principle interest is decreasing the time needed to complete a full volume scan in order to provide forecasters with more time and data for issuing warnings. Multiple proposals for a new Multimission Phased Array Radar (MPAR) network have
1. Introduction As the current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network approaches the end of its expected lifetime ( Yussouf and Stensrud 2008 ), numerous studies have raised several opportunities for improvement in a future network. Of principle interest is decreasing the time needed to complete a full volume scan in order to provide forecasters with more time and data for issuing warnings. Multiple proposals for a new Multimission Phased Array Radar (MPAR) network have
