The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
V. Chandrasekar - One of the best experts on this subject based on the ideXlab platform.
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Vertical Air Motions and Raindrop Size Distributions Estimated Using Mean Doppler Velocity Difference From 3- and 35-GHz Vertically Pointing Radars
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Christopher R. Williams, Robert M. Beauchamp, V. ChandrasekarAbstract:Vertical profiles of vertical air motion and raindrop size distributions (DSDs) within stratiform rain are estimated using two collocated vertically pointing radars (VPRs) operating at 3 and 35 GHz. Different raindrop backscattering cross sections occur at 3 and 35 GHz with Rayleigh scattering occurring for all raindrops at 3 GHz and Mie scattering occurring for larger raindrops at 35 GHz. This frequency-dependent backscattering cross section causes differently shaped Reflectivity-weighted Doppler velocity spectra leading to radar transmit frequency-dependent radar moments of intrinsic Reflectivity Factor, mean Doppler velocity, and spectrum variance. The retrieval method described herein uses four radar moments as inputs to retrieve four outputs at each height within a precipitation column. The inputs include 3-GHz VPR mean Doppler velocity and unattenuated Reflectivity Factor and 35-GHz VPR mean Doppler velocity and spectrum variance. The outputs include vertical air motion and three parameters of a gamma-shaped DSD. To account for different VPR sample volumes, radar observations were accumulated over 45 s and over several range gates to represent time-space scales larger than either VPR sample volumes. Observed variability over this common time-space scale is used to estimate retrieval uncertainties. The retrieved air motions and DSD parameters compare well against retrievals from a collocated 449-MHz VPR that estimated air motions from Bragg scattering signals and DSD parameters from Rayleigh scattering signals.
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Radar rainfall estimation from vertical Reflectivity profile using neural network
IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH, 2001Co-Authors: Gang Xu, V. ChandrasekarAbstract:An adaptive radial basis function (RBF) neural network to estimate the ground rainfall from a vertical profile of Reflectivity Factor (Z) is presented in this paper. This RBF network was applied to two months of WSR-88D radar data to estimate rainfall. Results show that the adaptive RBF developed here can estimate rainfall fairly well. Results were also compared with the WSR-88D, Z-R relationship and the Z-R algorithm derived from previous day observations.
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Self-consistency of polarization diversity measurement of rainfall
IEEE Transactions on Geoscience and Remote Sensing, 1996Co-Authors: G. Scarchilli, V. Chandrasekar, V. Gorgucci, A. DobaieAbstract:Polarization diversity measurements of rainfall, namely the Reflectivity Factor, differential Reflectivity, and specific differential propagation phase, vary in a constrained three-dimensional space. Algorithms are derived to quantify this self-consistency of measurements. In particular, estimation of the specific differential propagation phase shift based on Reflectivity and differential Reflectivity is analyzed in detail. Theoretical simulation as well as radar observations of rainfall at S (CSU-CHILL) and C (Polar 55C) bands are used to demonstrate that the range profiles of differential propagation shift can be constructed from measurements of Reflectivity and differential Reflectivity.
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A robust estimator of rainfall rate using differential Reflectivity
Journal of Atmospheric and Oceanic Technology, 1994Co-Authors: Eugenio Gorgucci, Gianfranco Scarchilli, V. ChandrasekarAbstract:Abstract Conventional estimator of rainfall rate using Reflectivity Factor and differential Reflectivity ZDR becomes unstable when the measured values of ZDR are small due to measurement errors. An alternate estimator of rainfall rate using Reflectivity Factor and ZDR is derived, so that this estimator is fairly robust over the full dynamic range of Reflectivity Factor and ZDR. Simulations are used to study the error structure of this robust estimator in comparison with the conventional estimator of rainfall rate. It is shown that the alternate estimator performs better than the conventional estimator of rainfall rate at all rainfall values. In particular the largest improvement of this estimator is proved to be in light rain. The robust estimator is obtained as a direct regression of rainfall rate against Reflectivity Factor and ZDR instead of solving for the drop size distribution.
Emmanuel Fontaine - One of the best experts on this subject based on the ideXlab platform.
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evaluation of radar Reflectivity Factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems
Atmospheric Measurement Techniques, 2016Co-Authors: Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoe, Alain Protat, Fabien Dezitter, Alice Grandin, J W Strapp, Lyle LilieAbstract:This study presents the evaluation of a technique to estimate cloud condensed water content (CWC) in tropical convection from airborne cloud radar Reflectivity Factors at 94 GHz and in situ measurements of particle size distributions (PSDs) and aspect ratios of ice crystal populations. The approach is to calculate from each 5 s mean PSD and flight-level Reflectivity the variability of all possible solutions of m(D) relationships fulfilling the condition that the simulated radar Reflectivity Factor (T-matrix method) matches the measured radar Reflectivity Factor. For the Reflectivity simulations, ice crystals were approximated as oblate spheroids, without using a priori assumptions on the mass–size relationship of ice crystals. The CWC calculations demonstrate that individual CWC values are in the range ±32 % of the retrieved average CWC value over all CWC solutions for the chosen 5 s time intervals. In addition, during the airborne field campaign performed out of Darwin in 2014, as part of the international High Altitude Ice Crystals/High Ice Water Content (HAIC/HIWC) projects, CWCs were measured independently with the new IKP-2 (isokinetic evaporator probe) instrument along with simultaneous particle imagery and radar Reflectivity. Retrieved CWCs from the T-matrix radar Reflectivity simulations are on average 16 % higher than the direct CWCIKP measurements. The differences between the CWCIKP and averaged retrieved CWCs are found to be primarily a function of the total number concentration of ice crystals. Consequently, a correction term is applied (as a function of total number concentration) that significantly improves the retrieved CWC. After correction, the retrieved CWCs have a median relative error with respect to measured values of only −1 %. Uncertainties in the measurements of total concentration of hydrometeors are investigated in order to calculate their contribution to the relative error of calculated CWC with respect to measured CWCIKP. It is shown that an overestimation of the concentration by about +50 % increases the relative errors of retrieved CWCs by only +29 %, while possible shattering, which impacts only the concentration of small hydrometeors, increases the relative error by about +4 %. Moreover, all cloud events with encountered graupel particles were studied and compared to events without observed graupel particles. Overall, graupel particles seem to have the largest impact on high crystal number-concentration conditions and show relative errors in retrieved CWCs that are higher than for events without graupel particles.
Ryo Onishi - One of the best experts on this subject based on the ideXlab platform.
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turbulent enhancement of radar Reflectivity Factor for polydisperse cloud droplets
Atmospheric Chemistry and Physics, 2019Co-Authors: Keigo Matsuda, Ryo OnishiAbstract:Abstract. The radar Reflectivity Factor is important for estimating cloud microphysical properties; thus, in this study, we determine the quantitative influence of microscale turbulent clustering of polydisperse droplets on the radar Reflectivity Factor. The theoretical solution for particulate Bragg scattering is obtained without assuming monodisperse droplet sizes. The scattering intensity is given by an integral function including the cross spectrum of number density fluctuations for two different droplet sizes. We calculate the cross spectrum based on turbulent clustering data, which are obtained by the direct numerical simulation (DNS) of particle-laden homogeneous isotropic turbulence. The results show that the coherence of the cross spectrum is close to unity for small wave numbers and decreases almost exponentially with increasing wave number. This decreasing trend is dependent on the combination of Stokes numbers. A critical wave number is introduced to characterize the exponential decrease of the coherence and parameterized using the Stokes number difference. Comparison with DNS results confirms that the proposed model can reproduce the r p 3 -weighted power spectrum, which is proportional to the clustering influence on the radar Reflectivity Factor to a sufficiently high accuracy. Furthermore, the proposed model is extended to incorporate the gravitational settling influence by modifying the critical wave number based on the analytical equation derived for the bidisperse radial distribution function. The estimate of the modified model also shows good agreement with the DNS results for the case with gravitational droplet settling. The model is then applied to high-resolution cloud-simulation data obtained from a spectral-bin cloud simulation. The result shows that the influence of turbulent clustering can be significant inside turbulent clouds. The large influence is observed at the near-top of the clouds, where the liquid water content and the energy dissipation rate are sufficiently large.
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Turbulent enhancement of radar Reflectivity Factor for polydisperse cloud droplets
2018Co-Authors: Keigo Matsuda, Ryo OnishiAbstract:<p><strong>Abstract.</strong> The radar Reflectivity Factor is important for estimating cloud microphysical properties; thus, in this study, we determine the quantitative influence of microscale turbulent clustering of polydisperse droplets on the radar Reflectivity Factor. The theoretical solution for particulate Bragg scattering is obtained without assuming monodisperse droplet sizes. The scattering intensity is given by an integral function including the cross spectrum of number density fluctuations for two different droplet sizes. We calculate the cross spectrum based on turbulent clustering data, which are obtained by the direct numerical simulation (DNS) of particle-laden homogeneous isotropic turbulence. The results show that the coherence of the cross spectrum is close to unity for small wavenumbers and decreases almost exponentially with increasing wavenumber. This decreasing trend is dependent on the combination of Stokes numbers. A critical wavenumber is introduced to characterize the exponential decrease of the coherence and parametrized using the Stokes number difference. Comparison with DNS results confirms that the proposed model can reproduce the <i>r</i><sub>p</sub><sup>3</sup>-weighted power spectrum, which is proportional to the clustering influence on the radar Reflectivity Factor, to a sufficiently high accuracy. The model is then applied to high-resolution cloud-simulation data obtained from a spectral-bin cloud simulation. The result shows that the influence of turbulent clustering can be significant for the near-top of turbulent clouds.</p>
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Influence of Gravitational Settling on Turbulent Droplet Clustering and Radar Reflectivity Factor
Flow Turbulence and Combustion, 2017Co-Authors: Keigo Matsuda, Ryo Onishi, Keiko TakahashiAbstract:This study investigates the influence of gravitational settling of droplets on turbulent clustering and the radar Reflectivity Factor. A three-dimensional direct numerical simulation (DNS) of particle-laden isotropic turbulence is performed to obtain turbulent droplet clustering data. The turbulent clustering data are then used to calculate the power spectrum of droplet number density fluctuations. The results show that the gravitational settling modulates the power spectrum more significantly as the settling becomes larger. The gravitational settling weakens the intensity of clustering at large wavenumbers for St≤1, whereas it significantly enlarges the intensity for St>1. The dependence on the Taylor-microscale-based Reynolds number is also investigated to discuss the contribution of large-scale eddies to the settling influence. The results show that large-scale eddies modulate the small scale clustering structure of large St droplets. The increment of radar Reflectivity Factor due to turbulent clustering is estimated from the power spectrum for the case of St=1.0. The result shows that the influence of gravitational settling on the radar Reflectivity Factor can be significant for the case of large settling velocity droplets.
Lyle Lilie - One of the best experts on this subject based on the ideXlab platform.
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evaluation of radar Reflectivity Factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems
Atmospheric Measurement Techniques, 2016Co-Authors: Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoe, Alain Protat, Fabien Dezitter, Alice Grandin, J W Strapp, Lyle LilieAbstract:This study presents the evaluation of a technique to estimate cloud condensed water content (CWC) in tropical convection from airborne cloud radar Reflectivity Factors at 94 GHz and in situ measurements of particle size distributions (PSDs) and aspect ratios of ice crystal populations. The approach is to calculate from each 5 s mean PSD and flight-level Reflectivity the variability of all possible solutions of m(D) relationships fulfilling the condition that the simulated radar Reflectivity Factor (T-matrix method) matches the measured radar Reflectivity Factor. For the Reflectivity simulations, ice crystals were approximated as oblate spheroids, without using a priori assumptions on the mass–size relationship of ice crystals. The CWC calculations demonstrate that individual CWC values are in the range ±32 % of the retrieved average CWC value over all CWC solutions for the chosen 5 s time intervals. In addition, during the airborne field campaign performed out of Darwin in 2014, as part of the international High Altitude Ice Crystals/High Ice Water Content (HAIC/HIWC) projects, CWCs were measured independently with the new IKP-2 (isokinetic evaporator probe) instrument along with simultaneous particle imagery and radar Reflectivity. Retrieved CWCs from the T-matrix radar Reflectivity simulations are on average 16 % higher than the direct CWCIKP measurements. The differences between the CWCIKP and averaged retrieved CWCs are found to be primarily a function of the total number concentration of ice crystals. Consequently, a correction term is applied (as a function of total number concentration) that significantly improves the retrieved CWC. After correction, the retrieved CWCs have a median relative error with respect to measured values of only −1 %. Uncertainties in the measurements of total concentration of hydrometeors are investigated in order to calculate their contribution to the relative error of calculated CWC with respect to measured CWCIKP. It is shown that an overestimation of the concentration by about +50 % increases the relative errors of retrieved CWCs by only +29 %, while possible shattering, which impacts only the concentration of small hydrometeors, increases the relative error by about +4 %. Moreover, all cloud events with encountered graupel particles were studied and compared to events without observed graupel particles. Overall, graupel particles seem to have the largest impact on high crystal number-concentration conditions and show relative errors in retrieved CWCs that are higher than for events without graupel particles.
Keigo Matsuda - One of the best experts on this subject based on the ideXlab platform.
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turbulent enhancement of radar Reflectivity Factor for polydisperse cloud droplets
Atmospheric Chemistry and Physics, 2019Co-Authors: Keigo Matsuda, Ryo OnishiAbstract:Abstract. The radar Reflectivity Factor is important for estimating cloud microphysical properties; thus, in this study, we determine the quantitative influence of microscale turbulent clustering of polydisperse droplets on the radar Reflectivity Factor. The theoretical solution for particulate Bragg scattering is obtained without assuming monodisperse droplet sizes. The scattering intensity is given by an integral function including the cross spectrum of number density fluctuations for two different droplet sizes. We calculate the cross spectrum based on turbulent clustering data, which are obtained by the direct numerical simulation (DNS) of particle-laden homogeneous isotropic turbulence. The results show that the coherence of the cross spectrum is close to unity for small wave numbers and decreases almost exponentially with increasing wave number. This decreasing trend is dependent on the combination of Stokes numbers. A critical wave number is introduced to characterize the exponential decrease of the coherence and parameterized using the Stokes number difference. Comparison with DNS results confirms that the proposed model can reproduce the r p 3 -weighted power spectrum, which is proportional to the clustering influence on the radar Reflectivity Factor to a sufficiently high accuracy. Furthermore, the proposed model is extended to incorporate the gravitational settling influence by modifying the critical wave number based on the analytical equation derived for the bidisperse radial distribution function. The estimate of the modified model also shows good agreement with the DNS results for the case with gravitational droplet settling. The model is then applied to high-resolution cloud-simulation data obtained from a spectral-bin cloud simulation. The result shows that the influence of turbulent clustering can be significant inside turbulent clouds. The large influence is observed at the near-top of the clouds, where the liquid water content and the energy dissipation rate are sufficiently large.
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Turbulent enhancement of radar Reflectivity Factor for polydisperse cloud droplets
2018Co-Authors: Keigo Matsuda, Ryo OnishiAbstract:<p><strong>Abstract.</strong> The radar Reflectivity Factor is important for estimating cloud microphysical properties; thus, in this study, we determine the quantitative influence of microscale turbulent clustering of polydisperse droplets on the radar Reflectivity Factor. The theoretical solution for particulate Bragg scattering is obtained without assuming monodisperse droplet sizes. The scattering intensity is given by an integral function including the cross spectrum of number density fluctuations for two different droplet sizes. We calculate the cross spectrum based on turbulent clustering data, which are obtained by the direct numerical simulation (DNS) of particle-laden homogeneous isotropic turbulence. The results show that the coherence of the cross spectrum is close to unity for small wavenumbers and decreases almost exponentially with increasing wavenumber. This decreasing trend is dependent on the combination of Stokes numbers. A critical wavenumber is introduced to characterize the exponential decrease of the coherence and parametrized using the Stokes number difference. Comparison with DNS results confirms that the proposed model can reproduce the <i>r</i><sub>p</sub><sup>3</sup>-weighted power spectrum, which is proportional to the clustering influence on the radar Reflectivity Factor, to a sufficiently high accuracy. The model is then applied to high-resolution cloud-simulation data obtained from a spectral-bin cloud simulation. The result shows that the influence of turbulent clustering can be significant for the near-top of turbulent clouds.</p>
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Influence of Gravitational Settling on Turbulent Droplet Clustering and Radar Reflectivity Factor
Flow Turbulence and Combustion, 2017Co-Authors: Keigo Matsuda, Ryo Onishi, Keiko TakahashiAbstract:This study investigates the influence of gravitational settling of droplets on turbulent clustering and the radar Reflectivity Factor. A three-dimensional direct numerical simulation (DNS) of particle-laden isotropic turbulence is performed to obtain turbulent droplet clustering data. The turbulent clustering data are then used to calculate the power spectrum of droplet number density fluctuations. The results show that the gravitational settling modulates the power spectrum more significantly as the settling becomes larger. The gravitational settling weakens the intensity of clustering at large wavenumbers for St≤1, whereas it significantly enlarges the intensity for St>1. The dependence on the Taylor-microscale-based Reynolds number is also investigated to discuss the contribution of large-scale eddies to the settling influence. The results show that large-scale eddies modulate the small scale clustering structure of large St droplets. The increment of radar Reflectivity Factor due to turbulent clustering is estimated from the power spectrum for the case of St=1.0. The result shows that the influence of gravitational settling on the radar Reflectivity Factor can be significant for the case of large settling velocity droplets.