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Observations of Convective Thermals with Weather Radar) and Wilson et al. (1994) discussed Bragg scatter and insects as sources of radar echoes from the convergence lines and came to a conclusion that insects are the main source of radar echoes, although Bragg scatter was not excluded. All the abovementioned observations used nonpolarimetric radar data in which distinguishing Bragg from insect echoes in not possible. Turbulent eddies with sizes of about 5-cm scatter S-band radiation (e.g., Doviak and Zrnić 2006 , section 11.6) and produce
) and Wilson et al. (1994) discussed Bragg scatter and insects as sources of radar echoes from the convergence lines and came to a conclusion that insects are the main source of radar echoes, although Bragg scatter was not excluded. All the abovementioned observations used nonpolarimetric radar data in which distinguishing Bragg from insect echoes in not possible. Turbulent eddies with sizes of about 5-cm scatter S-band radiation (e.g., Doviak and Zrnić 2006 , section 11.6) and produce
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
given here. Table 1. Precipitation type identifiers from NOAA’s NEXRAD reanalysis. 1) ROQPE Radar-only precipitation rates are obtained by applying Z – R relationships to the mosaicked hybrid scan reflectivity field pixel by pixel. Zhang et al. (2011) provide the overview of precipitation rate generation. 2) GCQPE Bias correction of radar-only QPEs is based on an additive radar rainfall error model. The details can be found in Zhang et al. (2011 , 2014) . Rain gauge observations used in this
given here. Table 1. Precipitation type identifiers from NOAA’s NEXRAD reanalysis. 1) ROQPE Radar-only precipitation rates are obtained by applying Z – R relationships to the mosaicked hybrid scan reflectivity field pixel by pixel. Zhang et al. (2011) provide the overview of precipitation rate generation. 2) GCQPE Bias correction of radar-only QPEs is based on an additive radar rainfall error model. The details can be found in Zhang et al. (2011 , 2014) . Rain gauge observations used in this
compared to measurements from nonpolarimetric radars. To determine the benefits offered by polarimetry on improving the quality of rainfall accumulation products, an algorithm is developed that uses polarimetric variables alone. It is envisioned that future algorithms designed to improve data quality will integrate polarimetric observations into techniques that have already been developed using nonpolarimetric observations (e.g., Steiner and Smith 2002 ). Algorithms designed to discriminate scatterers
compared to measurements from nonpolarimetric radars. To determine the benefits offered by polarimetry on improving the quality of rainfall accumulation products, an algorithm is developed that uses polarimetric variables alone. It is envisioned that future algorithms designed to improve data quality will integrate polarimetric observations into techniques that have already been developed using nonpolarimetric observations (e.g., Steiner and Smith 2002 ). Algorithms designed to discriminate scatterers
scales of the radar wavelength. If the scales of these structures are in resonance with the wavelength in a direction orthogonal to the direction of propagation of the transmitted wave, JK10a have already argued that coherent backscatter likely occurs. But what about in the direction of propagation? Some investigators have argued against the presence of coherent scatter by combining observations in neighboring range bins. In statistically homogeneous conditions and when only incoherent scatter is
scales of the radar wavelength. If the scales of these structures are in resonance with the wavelength in a direction orthogonal to the direction of propagation of the transmitted wave, JK10a have already argued that coherent backscatter likely occurs. But what about in the direction of propagation? Some investigators have argued against the presence of coherent scatter by combining observations in neighboring range bins. In statistically homogeneous conditions and when only incoherent scatter is
features in the model initial conditions. In view of the fact that both the extrapolation-based and NWP-based nowcasting techniques have their shortcomings, the nowcasting and numerical weather prediction (NWP) communities still face many scientific and technical challenges in predicting severe weather events in the time frame of 0–6 h. Owing to the high spatial resolution and frequent update rate, Doppler radar observations are a rich source of information about the three-dimensional meso- and
features in the model initial conditions. In view of the fact that both the extrapolation-based and NWP-based nowcasting techniques have their shortcomings, the nowcasting and numerical weather prediction (NWP) communities still face many scientific and technical challenges in predicting severe weather events in the time frame of 0–6 h. Owing to the high spatial resolution and frequent update rate, Doppler radar observations are a rich source of information about the three-dimensional meso- and
observations near the ground, to mast wind observations below ~400-m height, and to radiosonde wind observations in the upper atmosphere. However, the spatial and temporal resolution of these conventional wind observations is relatively poor. Doppler weather radars provide a complementary source of wind information. Observations are available with high spatial (0.5–1 km) and temporal (5–15 min) resolution, and their applicability to NWP model validation has been successfully demonstrated (e.g., Salonen et
observations near the ground, to mast wind observations below ~400-m height, and to radiosonde wind observations in the upper atmosphere. However, the spatial and temporal resolution of these conventional wind observations is relatively poor. Doppler weather radars provide a complementary source of wind information. Observations are available with high spatial (0.5–1 km) and temporal (5–15 min) resolution, and their applicability to NWP model validation has been successfully demonstrated (e.g., Salonen et
where this variability occurs. This paper presents one possible method using radars. Radars measure the reflectivity factor Z derived from observations of backscattered intensities I in sample volumes. For stationary antennas, the signal fluctuations are determined by random coherent summations of the electromagnetic waves reflected by the individual particles as described by Rayleigh statistics. The variance then is equal to the mean squared (i.e., the relative dispersion of the intensity σ I
where this variability occurs. This paper presents one possible method using radars. Radars measure the reflectivity factor Z derived from observations of backscattered intensities I in sample volumes. For stationary antennas, the signal fluctuations are determined by random coherent summations of the electromagnetic waves reflected by the individual particles as described by Rayleigh statistics. The variance then is equal to the mean squared (i.e., the relative dispersion of the intensity σ I
field. In our case, for analyzing radar observations, the basis function can be shifted in the azimuthal and radial directions. The basis function itself also can be stretched or compressed, as described by Eqs. (2) and (3) . By using (1) or its discrete version in (4) , the wavelet transform quantifies the local match of the wavelet function with the original function or field f  ( x ). When the wavelet matches the shape of the function well at a specific scale and location, a large wavelet
field. In our case, for analyzing radar observations, the basis function can be shifted in the azimuthal and radial directions. The basis function itself also can be stretched or compressed, as described by Eqs. (2) and (3) . By using (1) or its discrete version in (4) , the wavelet transform quantifies the local match of the wavelet function with the original function or field f  ( x ). When the wavelet matches the shape of the function well at a specific scale and location, a large wavelet
radars not only observe target backscatter power but also target velocities. This yields information on convective flows in clouds but may also improve the retrieval of cloud particle sizes. Because of the stochastic nature of these observations, both reflectivity and Doppler speed have an intrinsic inaccuracy that is not related to calibration issues or atmospheric attenuation. This inaccuracy represents the maximum accuracy achievable by the radar given specific radar parameters and cloud (target
radars not only observe target backscatter power but also target velocities. This yields information on convective flows in clouds but may also improve the retrieval of cloud particle sizes. Because of the stochastic nature of these observations, both reflectivity and Doppler speed have an intrinsic inaccuracy that is not related to calibration issues or atmospheric attenuation. This inaccuracy represents the maximum accuracy achievable by the radar given specific radar parameters and cloud (target
