Search Results
1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a
1980 ; Hallikainen et al. 1986 , 1987 ). Hence, snow cover has a time-varying effect on snowfall upwelling signal. Physical and empirical approaches have been developed for microwave retrievals of snowfall. Skofronick-Jackson et al. (2004) presented a physical method to retrieve snowfall during a blizzard over the eastern United States using high-frequency observations from the Advanced Microwave Sounding Unit B (AMSU-B) instrument. Kim et al. (2008) simulated atmospheric profiles of a
is underestimating reflectivity in ice-producing regions aloft, though likely not substantially, because the observed mean profiles decrease with altitude and do not exceed 30 dB. 4. Conclusions The Global Precipitation Measurement (GPM) satellite provides a powerful tool for evaluating moist physics in numerical weather prediction models, particularly over water and complex terrain where observations are difficult to acquire. In this study, we use observations from the GPM Microwave Imager (GMI
is underestimating reflectivity in ice-producing regions aloft, though likely not substantially, because the observed mean profiles decrease with altitude and do not exceed 30 dB. 4. Conclusions The Global Precipitation Measurement (GPM) satellite provides a powerful tool for evaluating moist physics in numerical weather prediction models, particularly over water and complex terrain where observations are difficult to acquire. In this study, we use observations from the GPM Microwave Imager (GMI
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
radar observations. The PSD data used in this study are available from the OLYMPEX and IPHEX data portals. The PSD data are integrated to derive the associated equivalent reflectivity factors at Ku, Ka, and W band. This procedure is described next. b. Radar backscattering The main challenge in quantifying the electromagnetic-scattering properties of realistic snowflakes and ice particles at microwave frequencies is that they exhibit complex shapes that make the numerical solutions to Maxwell
radar observations. The PSD data used in this study are available from the OLYMPEX and IPHEX data portals. The PSD data are integrated to derive the associated equivalent reflectivity factors at Ku, Ka, and W band. This procedure is described next. b. Radar backscattering The main challenge in quantifying the electromagnetic-scattering properties of realistic snowflakes and ice particles at microwave frequencies is that they exhibit complex shapes that make the numerical solutions to Maxwell
comparisons with radar rainfall estimates (e.g., Stampoulis et al. 2013 ; Gebregiorgis et al. 2017 ), gauge observations (e.g., Mei et al. 2014 ; Prat and Nelson 2015 ; Miao et al. 2015 ), and merged radar and gauge rainfall estimates such as the National Centers for Environmental Prediction (NCEP) Stage IV ( Lin and Mitchell 2005 ) products (e.g., Gourley et al. 2010 ; Mehran and AghaKouchak 2014 ). Radar precipitation estimates are subject to errors from, for example, radar calibration, beam
comparisons with radar rainfall estimates (e.g., Stampoulis et al. 2013 ; Gebregiorgis et al. 2017 ), gauge observations (e.g., Mei et al. 2014 ; Prat and Nelson 2015 ; Miao et al. 2015 ), and merged radar and gauge rainfall estimates such as the National Centers for Environmental Prediction (NCEP) Stage IV ( Lin and Mitchell 2005 ) products (e.g., Gourley et al. 2010 ; Mehran and AghaKouchak 2014 ). Radar precipitation estimates are subject to errors from, for example, radar calibration, beam
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
. With its onboard Dual-Frequency Precipitation Radar (DPR) and 13-channel GPM Microwave Imager (GMI), the GPM satellite extends into future decades the global surveillance of precipitation provided until 2014 by the Tropical Rainfall Measuring Mission (TRMM) satellite and broadens coverage to higher latitudes, where many of Earth’s snow-covered mountain ranges are located. GPM also serves as a reference for other satellites carrying a variety of microwave imaging or sounding radiometers [see Hou et
. With its onboard Dual-Frequency Precipitation Radar (DPR) and 13-channel GPM Microwave Imager (GMI), the GPM satellite extends into future decades the global surveillance of precipitation provided until 2014 by the Tropical Rainfall Measuring Mission (TRMM) satellite and broadens coverage to higher latitudes, where many of Earth’s snow-covered mountain ranges are located. GPM also serves as a reference for other satellites carrying a variety of microwave imaging or sounding radiometers [see Hou et
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
. The two methods employed here to develop Z e – S relationships for multiple wavelengths are described in section 2 together with the regional and global datasets used. The results are presented in section 3 . Section 4 discusses the findings, and the conclusions are summarized in section 5 . 2. Methodology a. Aircraft-based snowfall-rate estimates and radar measurements The most direct method [observations (OBS)] to develop snowfall-rate–radar reflectivity relationships at multiple
. The two methods employed here to develop Z e – S relationships for multiple wavelengths are described in section 2 together with the regional and global datasets used. The results are presented in section 3 . Section 4 discusses the findings, and the conclusions are summarized in section 5 . 2. Methodology a. Aircraft-based snowfall-rate estimates and radar measurements The most direct method [observations (OBS)] to develop snowfall-rate–radar reflectivity relationships at multiple
measurements. The difficulty of representing spatial rainfall variability from ground-based observations highlights the need to use multisatellite precipitation datasets—that is, datasets that combine infrared (IR) radiances and passive microwave (PMW) precipitation retrievals—which can represent the space–time variability of rainfall with quasi-global coverage ( Huffman et al. 2007 , 2010 ; Joyce et al. 2004 ; Kubota et al. 2007 ; Ushio et al. 2009 ). However, the effective use of satellite
measurements. The difficulty of representing spatial rainfall variability from ground-based observations highlights the need to use multisatellite precipitation datasets—that is, datasets that combine infrared (IR) radiances and passive microwave (PMW) precipitation retrievals—which can represent the space–time variability of rainfall with quasi-global coverage ( Huffman et al. 2007 , 2010 ; Joyce et al. 2004 ; Kubota et al. 2007 ; Ushio et al. 2009 ). However, the effective use of satellite
