Search Results
-polarization radar observations of snow growth processes of a severe winter storm: Case of 12 December 2013 in South Korea . J. Atmos. Oceanic Technol. , 36 , 1217 – 1235 , https://doi.org/10.1175/JTECH-D-18-0076.1 . Austin , R. T. , and G. L. Stephens , 2001 : Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat: 1. Algorithm formulation . J. Geophys. Res. , 106 , 28 233 – 28 242 , https://doi.org/10.1029/2000JD
-polarization radar observations of snow growth processes of a severe winter storm: Case of 12 December 2013 in South Korea . J. Atmos. Oceanic Technol. , 36 , 1217 – 1235 , https://doi.org/10.1175/JTECH-D-18-0076.1 . Austin , R. T. , and G. L. Stephens , 2001 : Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat: 1. Algorithm formulation . J. Geophys. Res. , 106 , 28 233 – 28 242 , https://doi.org/10.1029/2000JD
of MAXHT20 and MAXHT30 over tropical (20°S–20°N) (top) land and (bottom) ocean. With different wavelengths at Ku and Ka bands, the electromagnetic waves interact with particles differently. The combination of the two channels would help in the retrieval of microphysical parameters. Typically, based on the radar equation, there are three likely influences on DFR profiles: 1) the Mie scattering effect, 2) the path-integrated attenuation, and 3) multiple scattering. Based on the Mie scattering
of MAXHT20 and MAXHT30 over tropical (20°S–20°N) (top) land and (bottom) ocean. With different wavelengths at Ku and Ka bands, the electromagnetic waves interact with particles differently. The combination of the two channels would help in the retrieval of microphysical parameters. Typically, based on the radar equation, there are three likely influences on DFR profiles: 1) the Mie scattering effect, 2) the path-integrated attenuation, and 3) multiple scattering. Based on the Mie scattering
Detection of Intense Ice Precipitation with GPM/DPRestimate the particle size more accurately than a single-frequency radar so that we can improve the estimates of rainfall rate and identify snow precipitation regions. In fact, by using the difference in the scattering and attenuation properties of liquid and solid water particles between Ku- and Ka-band electromagnetic (EM) waves, it is possible to estimate the mean diameter of precipitation particles once an appropriate particle size distribution (PSD) model is chosen. Since the mean particle size
estimate the particle size more accurately than a single-frequency radar so that we can improve the estimates of rainfall rate and identify snow precipitation regions. In fact, by using the difference in the scattering and attenuation properties of liquid and solid water particles between Ku- and Ka-band electromagnetic (EM) waves, it is possible to estimate the mean diameter of precipitation particles once an appropriate particle size distribution (PSD) model is chosen. Since the mean particle size
. 3 , right column). Two-dimensional space–time PSDs are commonly used in climate and Earth science to analyze the propagation of atmospheric and oceanic waves along longitudes ( Wheeler and Kiladis 1999 ; Lin et al. 2006 ; Orbe et al. 2020 ). The left half of the PSD (negative wavelengths) corresponds to eastward propagating waves and the right half (positive wavelengths) corresponds to westward propagating waves. The two-dimensional space–time PSD shows the energy of the eastward and westward
. 3 , right column). Two-dimensional space–time PSDs are commonly used in climate and Earth science to analyze the propagation of atmospheric and oceanic waves along longitudes ( Wheeler and Kiladis 1999 ; Lin et al. 2006 ; Orbe et al. 2020 ). The left half of the PSD (negative wavelengths) corresponds to eastward propagating waves and the right half (positive wavelengths) corresponds to westward propagating waves. The two-dimensional space–time PSD shows the energy of the eastward and westward
mountain range with permanent snow cover. Figure 1 shows the terrain of the Olympic Mountain range, which occupies the Olympic Peninsula of the state of Washington. The peninsula has a north–south coastline on the Pacific Ocean and is separated from Canada’s Vancouver Island on its north side by the narrow Strait of Juan de Fuca. Fig . 1. Map of the region where the OLYMPEX campaign occurred, including the mountainous terrain of the Olympic Peninsula. The motivation for OLYMPEX was not only to better
mountain range with permanent snow cover. Figure 1 shows the terrain of the Olympic Mountain range, which occupies the Olympic Peninsula of the state of Washington. The peninsula has a north–south coastline on the Pacific Ocean and is separated from Canada’s Vancouver Island on its north side by the narrow Strait of Juan de Fuca. Fig . 1. Map of the region where the OLYMPEX campaign occurred, including the mountainous terrain of the Olympic Peninsula. The motivation for OLYMPEX was not only to better
TRMM Version 8 Reprocessing Improvements and Incorporation into the GPM Data Suiteimproved precipitation detection and bias corrections (K. Kanemaru 2017, personal communication). The 17+-yr record of TRMM PR showed a time-dependent drift of approximately 0.2 dB in the surface cross section over ocean, which has also been mitigated in GPM V05 ( Takahashi and Iguchi 2004 ). Further details on the calibration changes for GPM V05 processing of PR are outside the scope of this work and will be detailed in another publication. This paper provides information about the work undertaken to
improved precipitation detection and bias corrections (K. Kanemaru 2017, personal communication). The 17+-yr record of TRMM PR showed a time-dependent drift of approximately 0.2 dB in the surface cross section over ocean, which has also been mitigated in GPM V05 ( Takahashi and Iguchi 2004 ). Further details on the calibration changes for GPM V05 processing of PR are outside the scope of this work and will be detailed in another publication. This paper provides information about the work undertaken to
–liquid phases). LH and its vertical distribution not only have a strong influence on a variety of tropical circulations, including tropical waves and tropical cyclone intensity, but also on midlatitude cyclones and weather systems. Moreover, the processes associated with LH result in significant nonlinear changes in atmospheric radiation through the creation, dissipation, and modulation of clouds and precipitation. Although more recent efforts have been made to estimate the LH associated with weakly
–liquid phases). LH and its vertical distribution not only have a strong influence on a variety of tropical circulations, including tropical waves and tropical cyclone intensity, but also on midlatitude cyclones and weather systems. Moreover, the processes associated with LH result in significant nonlinear changes in atmospheric radiation through the creation, dissipation, and modulation of clouds and precipitation. Although more recent efforts have been made to estimate the LH associated with weakly
processes. For example, at synoptic time scales, variability of the rainfall is observed linking to tropical wave dynamics ( Mounier et al. 2007 ) and the extratropical intrusion of dry air ( Roca et al. 2005 ). The dominant mode of synoptic variability of precipitation is correlated with African easterly waves (AEWs) through the dynamic relationship to organized convection systems ( Kiladis et al. 2006 ; Skinner and Diffenbaugh 2013 ). Satellite observations and numerical model simulations are
processes. For example, at synoptic time scales, variability of the rainfall is observed linking to tropical wave dynamics ( Mounier et al. 2007 ) and the extratropical intrusion of dry air ( Roca et al. 2005 ). The dominant mode of synoptic variability of precipitation is correlated with African easterly waves (AEWs) through the dynamic relationship to organized convection systems ( Kiladis et al. 2006 ; Skinner and Diffenbaugh 2013 ). Satellite observations and numerical model simulations are
Imager (prior to May 2014). These correlations are computed at 20° × 20° regions (ocean) or 10° latitude bands (land) every month over a 3-month period that is either centered on (Final) or trailing (Late and Early) that month. The spatial and temporal coarseness is needed to ensure sufficient sample sizes, with additional postprocessing to reduce fluctuations due to noise. Therefore, the Kalman correlations give us a quantitative measure at every location of the skill of, say, an estimate from
Imager (prior to May 2014). These correlations are computed at 20° × 20° regions (ocean) or 10° latitude bands (land) every month over a 3-month period that is either centered on (Final) or trailing (Late and Early) that month. The spatial and temporal coarseness is needed to ensure sufficient sample sizes, with additional postprocessing to reduce fluctuations due to noise. Therefore, the Kalman correlations give us a quantitative measure at every location of the skill of, say, an estimate from
. 1991 ). Specifically, the emissivity of liquid hydrometeors is greater than that of the ocean surface, so warmer brightness temperatures (BTs) are measured above liquid precipitation than above a precipitation-free ocean surface (e.g., Spencer et al. 1989 ; Adler et al. 1991 ; Wilheit et al. 1991 ; McGaughey et al. 1996 ). The emission signal from liquid precipitation depends on several factors including characteristics of the background, frequency, liquid mass, rain rate, and thickness of the
. 1991 ). Specifically, the emissivity of liquid hydrometeors is greater than that of the ocean surface, so warmer brightness temperatures (BTs) are measured above liquid precipitation than above a precipitation-free ocean surface (e.g., Spencer et al. 1989 ; Adler et al. 1991 ; Wilheit et al. 1991 ; McGaughey et al. 1996 ). The emission signal from liquid precipitation depends on several factors including characteristics of the background, frequency, liquid mass, rain rate, and thickness of the
