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1. Introduction This paper describes a radar-based remote sensing technique called WiRAR, which enables the measurement of the ocean surface wind from towers and ships. A marine radar operating at X band has the capability of measuring the backscatter from the ocean surface in space and time under most weather conditions and independent of lighting conditions. There are no biases due to wind sensor motion and height variations. Since the radar measures the wind from the ocean surface beside the
1. Introduction This paper describes a radar-based remote sensing technique called WiRAR, which enables the measurement of the ocean surface wind from towers and ships. A marine radar operating at X band has the capability of measuring the backscatter from the ocean surface in space and time under most weather conditions and independent of lighting conditions. There are no biases due to wind sensor motion and height variations. Since the radar measures the wind from the ocean surface beside the
1. Introduction The prelaunch calibration of the CloudSat cloud-profiling radar (CPR; Stephens et al. 2002 ), in-flight calibration, and stability over the period of operation has been very recently reported in Tanelli et al. (2008) and Stephens et al. (2008) . This in-flight calibration relies on monthly comparisons of ocean backscatter measured at 10° incidence off-nadir using dedicated CloudSat maneuvers and the corresponding ocean backscatter predicted by different theoretical
1. Introduction The prelaunch calibration of the CloudSat cloud-profiling radar (CPR; Stephens et al. 2002 ), in-flight calibration, and stability over the period of operation has been very recently reported in Tanelli et al. (2008) and Stephens et al. (2008) . This in-flight calibration relies on monthly comparisons of ocean backscatter measured at 10° incidence off-nadir using dedicated CloudSat maneuvers and the corresponding ocean backscatter predicted by different theoretical
1. Introduction Observations made using a weather radar are usually physically located above the ground level, as the earth curvature causes radar beams to ascend with distance. When radar is used for ground level precipitation estimation, observations need to be corrected for the effects of beam height. Even though radar precipitation estimates are operationally corrected using schemes such as the vertical profile of reflectivity (VPR), advection of precipitation between the radar contributing
1. Introduction Observations made using a weather radar are usually physically located above the ground level, as the earth curvature causes radar beams to ascend with distance. When radar is used for ground level precipitation estimation, observations need to be corrected for the effects of beam height. Even though radar precipitation estimates are operationally corrected using schemes such as the vertical profile of reflectivity (VPR), advection of precipitation between the radar contributing
1. Introduction One of the goals of the Engineering Research Center for Collaborative Adapting Sensing of the Atmosphere (CASA) is to develop a paradigm of networked radar systems to improve the coverage of the lowest portion of the atmosphere through coordinated scanning of low-power, short-range, networked radars [referred to as distributed collaborative adaptive sensing (DCAS; McLaughlin et al. 2005 ; Chandrasekar and Jayasumana 2001 )]. The first DCAS technology demonstration test bed was
1. Introduction One of the goals of the Engineering Research Center for Collaborative Adapting Sensing of the Atmosphere (CASA) is to develop a paradigm of networked radar systems to improve the coverage of the lowest portion of the atmosphere through coordinated scanning of low-power, short-range, networked radars [referred to as distributed collaborative adaptive sensing (DCAS; McLaughlin et al. 2005 ; Chandrasekar and Jayasumana 2001 )]. The first DCAS technology demonstration test bed was
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
1. Introduction Knowledge regarding the three-dimensional variability of precipitation is essential in the development of precipitation retrieval algorithms from satellite radiometer observations. This is because satellite radiometer observations cannot be uniquely associated with precipitation, and statistical information is required to determine optimal precipitation estimates. Spaceborne radar observations may be used to derive such information. For example, it is anticipated that in the
1. Introduction Knowledge regarding the three-dimensional variability of precipitation is essential in the development of precipitation retrieval algorithms from satellite radiometer observations. This is because satellite radiometer observations cannot be uniquely associated with precipitation, and statistical information is required to determine optimal precipitation estimates. Spaceborne radar observations may be used to derive such information. For example, it is anticipated that in the
1. Introduction Conventional meteorological radars provide coverage over long ranges (often hundreds of kilometers) and support weather surveillance and hydrological monitoring applications by using high-power transmitters and mechanically scanned antennas. These systems operate at wavelengths in the 5–10-cm range to propagate through precipitation, and necessitate the use of physically large antennas to achieve high resolution at the distant ranges. As an alternate solution, a networked radar
1. Introduction Conventional meteorological radars provide coverage over long ranges (often hundreds of kilometers) and support weather surveillance and hydrological monitoring applications by using high-power transmitters and mechanically scanned antennas. These systems operate at wavelengths in the 5–10-cm range to propagate through precipitation, and necessitate the use of physically large antennas to achieve high resolution at the distant ranges. As an alternate solution, a networked radar
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
1. Introduction Accurate rainfall estimation is fundamental for many applications in hydrology, nowcasting, and so on. Weather radars are the most adequate instruments to reveal the high-resolution spatial and temporal variation of the rainfall fields, and their potential value for quantitative precipitation estimation (QPE) was recognized since practically the beginning of the field ( Marshall et al. 1947 ). However, radar measurements are subject to multiple uncertainty sources, which can
1. Introduction Accurate rainfall estimation is fundamental for many applications in hydrology, nowcasting, and so on. Weather radars are the most adequate instruments to reveal the high-resolution spatial and temporal variation of the rainfall fields, and their potential value for quantitative precipitation estimation (QPE) was recognized since practically the beginning of the field ( Marshall et al. 1947 ). However, radar measurements are subject to multiple uncertainty sources, which can
1. Introduction Currently flying spaceborne precipitation and cloud radars, including the Ku-band precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) ( Kummerow et al. 2000 ) and the W-band CloudSat ( Stephens et al. 2008 ), provide global coverage of a variety of weather and cloud systems. With the success of these radars, the next spaceborne meteorologically oriented radars to be launched are the Global Precipitation Mission (GPM) dual-frequency (Ku and Ka band
1. Introduction Currently flying spaceborne precipitation and cloud radars, including the Ku-band precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) ( Kummerow et al. 2000 ) and the W-band CloudSat ( Stephens et al. 2008 ), provide global coverage of a variety of weather and cloud systems. With the success of these radars, the next spaceborne meteorologically oriented radars to be launched are the Global Precipitation Mission (GPM) dual-frequency (Ku and Ka band
