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The System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) Precipitation Observation Fusion Framework.g., setting a horizontal grid spacing significantly smaller than the gate size of the ground-based scanning radars would not be a wise practice). Generally, we recommend SIMBA column grid spacing be set to at least 500 m in the horizontal and at least 250 m in the vertical planes, and we note that for some applications larger grid spacing on the order of 1 km may be more relevant (e.g., comparisons of ground-based radar and satelliteborne passive and active microwave observations at the pixel scale or
.g., setting a horizontal grid spacing significantly smaller than the gate size of the ground-based scanning radars would not be a wise practice). Generally, we recommend SIMBA column grid spacing be set to at least 500 m in the horizontal and at least 250 m in the vertical planes, and we note that for some applications larger grid spacing on the order of 1 km may be more relevant (e.g., comparisons of ground-based radar and satelliteborne passive and active microwave observations at the pixel scale or
Dual-Polarization Radar Retrievals of Coastal Pacific Northwest Raindrop Size Distribution Parameters Using Random Forest Regression1. Introduction Precipitating weather events can be observed in three dimensions thanks to the continuous spatial and temporal coverage of meteorological ground-based radars, making radar observations a valuable tool for weather analysis and research. Radar products have long been used for microphysical retrievals, such as rain rate and drop size distribution (DSD) characteristics. Perhaps the first such retrieval was described in Marshall and Palmer (1948) , who used reflectivity Z to
1. Introduction Precipitating weather events can be observed in three dimensions thanks to the continuous spatial and temporal coverage of meteorological ground-based radars, making radar observations a valuable tool for weather analysis and research. Radar products have long been used for microphysical retrievals, such as rain rate and drop size distribution (DSD) characteristics. Perhaps the first such retrieval was described in Marshall and Palmer (1948) , who used reflectivity Z to
Impact of Mass–Size Parameterizations of Frozen Hydrometeors on Microphysical Retrievals: Evaluation by Matching Radar to In Situ Observations from GCPEx and OLYMPExthe effect of the PSD from that of the MSP on the radar reflectivity. Moreover, characterizing MSP and PSD by a few scalars (radar observations) is impossible unless one parameterizes the spectra (e.g., gamma models for the PSD, power laws for the MSP) or focuses on mass-weighted bulk moments of the PSD. Here, we examine specifically the potential of a multifrequency radar approach that spans the frequencies traditionally employed in spaceborne cloud and precipitation radars. Efforts to quantify
the effect of the PSD from that of the MSP on the radar reflectivity. Moreover, characterizing MSP and PSD by a few scalars (radar observations) is impossible unless one parameterizes the spectra (e.g., gamma models for the PSD, power laws for the MSP) or focuses on mass-weighted bulk moments of the PSD. Here, we examine specifically the potential of a multifrequency radar approach that spans the frequencies traditionally employed in spaceborne cloud and precipitation radars. Efforts to quantify
A Regression-Free Rainfall Estimation Algorithm for Dual-Polarization Radarsare composed of small spherical raindrops (e.g., low rain-rate events). Therefore, although such radars can detect smaller particles (e.g., cloud drops), they are suitable for observations within a shorter range and for certain environmental conditions. a. CSU-HIDRO The CSU-HIDRO rainfall retrieval algorithm utilizes a combination of four rain-rate estimators as presented in Fig. 1 (reproduced from Cifelli et al. 2011 ). These estimators are , , , and . The empirical formulations for these
are composed of small spherical raindrops (e.g., low rain-rate events). Therefore, although such radars can detect smaller particles (e.g., cloud drops), they are suitable for observations within a shorter range and for certain environmental conditions. a. CSU-HIDRO The CSU-HIDRO rainfall retrieval algorithm utilizes a combination of four rain-rate estimators as presented in Fig. 1 (reproduced from Cifelli et al. 2011 ). These estimators are , , , and . The empirical formulations for these
