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- Author or Editor: P. Kollias x
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Abstract
The retrieval of cloud, drizzle, and turbulence parameters using radar Doppler spectra is challenged by the convolution of microphysical and dynamical influences and the overall uncertainty introduced by turbulence. A new technique that utilizes recorded radar Doppler spectra from profiling cloud radars is presented here. The technique applies to areas in clouds where drizzle is initially produced by the autoconversion process and is detected by a positive skewness in the radar Doppler spectrum. Using the Gaussian-shape property of cloud Doppler spectra, the cloud-only radar Doppler spectrum is estimated and used to separate the cloud and drizzle contributions. Once separated, the cloud spectral peak can be used to retrieve vertical air motion and eddy dissipation rates, while the drizzle peak can be used to estimate the three radar moments of the drizzle particle size distribution. The technique works for nearly 50% of spectra found near cloud top, with efficacy diminishing to roughly 15% of spectra near cloud base. The approach has been tested on a large dataset collected in the Azores during the Atmospheric Radiation Measurement Program (ARM) Mobile Facility deployment on Graciosa Island from May 2009 through December 2010. Validation of the proposed technique is achieved using the cloud base as a natural boundary between radar Doppler spectra with and without cloud droplets. The retrieval algorithm has the potential to characterize the dynamical and microphysical conditions at cloud scale during the transition from cloud to precipitation. This has significant implications for improving the understanding of drizzle onset in liquid clouds and for improving model parameterization schemes of autoconversion of cloud water into drizzle.
Abstract
The retrieval of cloud, drizzle, and turbulence parameters using radar Doppler spectra is challenged by the convolution of microphysical and dynamical influences and the overall uncertainty introduced by turbulence. A new technique that utilizes recorded radar Doppler spectra from profiling cloud radars is presented here. The technique applies to areas in clouds where drizzle is initially produced by the autoconversion process and is detected by a positive skewness in the radar Doppler spectrum. Using the Gaussian-shape property of cloud Doppler spectra, the cloud-only radar Doppler spectrum is estimated and used to separate the cloud and drizzle contributions. Once separated, the cloud spectral peak can be used to retrieve vertical air motion and eddy dissipation rates, while the drizzle peak can be used to estimate the three radar moments of the drizzle particle size distribution. The technique works for nearly 50% of spectra found near cloud top, with efficacy diminishing to roughly 15% of spectra near cloud base. The approach has been tested on a large dataset collected in the Azores during the Atmospheric Radiation Measurement Program (ARM) Mobile Facility deployment on Graciosa Island from May 2009 through December 2010. Validation of the proposed technique is achieved using the cloud base as a natural boundary between radar Doppler spectra with and without cloud droplets. The retrieval algorithm has the potential to characterize the dynamical and microphysical conditions at cloud scale during the transition from cloud to precipitation. This has significant implications for improving the understanding of drizzle onset in liquid clouds and for improving model parameterization schemes of autoconversion of cloud water into drizzle.
Abstract
Automated retrievals of vertical air motion and the drop size distribution (DSD) slope parameter from the surface to the base of the melting layer are presented using a technique for W-band (95 GHz) profiling radars. The technique capitalizes on non-Rayleigh resonance signatures found in the observed Doppler spectra to estimate the mean vertical air motion. The slope parameter of the DSD for an assumed exponential form is retrieved through an inversion of the Doppler spectra. Extended testing is performed in central Oklahoma for a monthlong period of observation that includes several midlatitude convective line trailing stratiform events featuring low to moderate rainfall rates (<1 to 30 mm h−1). Low-level DSD slope parameter retrievals are shown in agreement (bias of −1.48 cm−1 and rms error of 4.38 cm−1) with collocated surface disdrometer DSD observations. Velocity retrievals indicate a net downward motion in stratiform rain of 0.05 m s−1 with a standard deviation of 0.24–0.3 m s−1. Time–height examples drawn from the available dataset illustrate finescale structures, as well as evidence of drop sorting due to differential terminal velocity and wind shear.
Abstract
Automated retrievals of vertical air motion and the drop size distribution (DSD) slope parameter from the surface to the base of the melting layer are presented using a technique for W-band (95 GHz) profiling radars. The technique capitalizes on non-Rayleigh resonance signatures found in the observed Doppler spectra to estimate the mean vertical air motion. The slope parameter of the DSD for an assumed exponential form is retrieved through an inversion of the Doppler spectra. Extended testing is performed in central Oklahoma for a monthlong period of observation that includes several midlatitude convective line trailing stratiform events featuring low to moderate rainfall rates (<1 to 30 mm h−1). Low-level DSD slope parameter retrievals are shown in agreement (bias of −1.48 cm−1 and rms error of 4.38 cm−1) with collocated surface disdrometer DSD observations. Velocity retrievals indicate a net downward motion in stratiform rain of 0.05 m s−1 with a standard deviation of 0.24–0.3 m s−1. Time–height examples drawn from the available dataset illustrate finescale structures, as well as evidence of drop sorting due to differential terminal velocity and wind shear.
Abstract
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates 35-GHz millimeter-wavelength cloud radars (MMCRs) in several climatologically distinct regions. The MMCRs, which are centerpiece instruments for the observation of clouds and precipitation, provide continuous, vertically resolved information on all hydrometeors above the ARM Climate Research Facilities (ACRF). However, their ability to observe clouds in the lowest 2–3 km of the atmosphere is often obscured by the presence of strong echoes from insects, especially during the warm months at the continental midlatitude Southern Great Plains (SGP) ACRF. Here, a new automated technique for the detection and elimination of insect-contaminated echoes from the MMCR observations is presented. The technique is based on recorded MMCR Doppler spectra, a feature extractor that conditions insect spectral signatures, and the use of a neural network algorithm for the generation of an insect (clutter) mask. The technique exhibits significant skill in the identification of insect radar returns (more than 92% of insect-induced returns are identified) when the sole input to the classifier is the MMCR Doppler spectrum. The addition of circular polarization observations by the MMCR and ceilometer cloud-base measurements further improve the performance of the technique and form an even more reliable method for the removal of insect radar echoes at the ARM site. Recently, a 94-GHz Doppler polarimetric radar was installed next to the MMCR at the ACRF SGP site. Observations by both radars are used to evaluate the potential of the 94-GHz radar as being insect free and to show that dual wavelength radar reflectivity measurements can be used to identify insect radar returns.
Abstract
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates 35-GHz millimeter-wavelength cloud radars (MMCRs) in several climatologically distinct regions. The MMCRs, which are centerpiece instruments for the observation of clouds and precipitation, provide continuous, vertically resolved information on all hydrometeors above the ARM Climate Research Facilities (ACRF). However, their ability to observe clouds in the lowest 2–3 km of the atmosphere is often obscured by the presence of strong echoes from insects, especially during the warm months at the continental midlatitude Southern Great Plains (SGP) ACRF. Here, a new automated technique for the detection and elimination of insect-contaminated echoes from the MMCR observations is presented. The technique is based on recorded MMCR Doppler spectra, a feature extractor that conditions insect spectral signatures, and the use of a neural network algorithm for the generation of an insect (clutter) mask. The technique exhibits significant skill in the identification of insect radar returns (more than 92% of insect-induced returns are identified) when the sole input to the classifier is the MMCR Doppler spectrum. The addition of circular polarization observations by the MMCR and ceilometer cloud-base measurements further improve the performance of the technique and form an even more reliable method for the removal of insect radar echoes at the ARM site. Recently, a 94-GHz Doppler polarimetric radar was installed next to the MMCR at the ACRF SGP site. Observations by both radars are used to evaluate the potential of the 94-GHz radar as being insect free and to show that dual wavelength radar reflectivity measurements can be used to identify insect radar returns.
Abstract
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program operates millimeter-wavelength cloud radars (MMCRs) in several specific locations within different climatological regimes. These vertically pointing cloud profiling radars supply the three most important Doppler spectrum moment estimates, which are the radar reflectivity (or zero moment), the mean Doppler velocity (or first moment), and the Doppler spectrum width (or second moment), as a function of time and height. The ARM MMCR Doppler moment estimates form the basis of a number of algorithms for retrieving cloud microphysical and radiative properties. The retrieval algorithms are highly sensitive to the quality and accuracy of the MMCR Doppler moment estimates. The significance of these sensitivities should not be underestimated, because the inherent physical variability of clouds, instrument-induced noise, and sampling strategy limitations all potentially introduce errors into the Doppler moment estimates. In this article, the accuracies of the first three Doppler moment estimates from the ARM MMCRs are evaluated for a set of typical cloud conditions from the three DOE ARM program sites. Results of the analysis suggest that significant errors in the Doppler moment estimates are possible in the current configurations of the ARM MMCRs. In particular, weakly reflecting clouds with low signal-to-noise ratios (SNRs), as well as turbulent clouds with nonzero updraft and downdraft velocities that are coupled with high SNR, are shown to produce degraded Doppler moment estimates in the current ARM MMCR operational mode processing strategies. Analysis of the Doppler moment estimates and MMCR receiver noise characteristics suggests that the introduction of a set of quality control criteria is necessary for identifying periods of degraded receiver performance that leads to larger uncertainties in the Doppler moment estimates. Moreover, the temporal sampling of the ARM MMCRs was found to be insufficient for representing the actual dynamical states in many types of clouds, especially boundary layer clouds. New digital signal processors (DSPs) are currently being developed for the ARM MMCRs. The findings presented in this study will be used in the design of a new set of operational strategies for the ARM MMCRs once they have been upgraded with the new DSPs.
Abstract
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program operates millimeter-wavelength cloud radars (MMCRs) in several specific locations within different climatological regimes. These vertically pointing cloud profiling radars supply the three most important Doppler spectrum moment estimates, which are the radar reflectivity (or zero moment), the mean Doppler velocity (or first moment), and the Doppler spectrum width (or second moment), as a function of time and height. The ARM MMCR Doppler moment estimates form the basis of a number of algorithms for retrieving cloud microphysical and radiative properties. The retrieval algorithms are highly sensitive to the quality and accuracy of the MMCR Doppler moment estimates. The significance of these sensitivities should not be underestimated, because the inherent physical variability of clouds, instrument-induced noise, and sampling strategy limitations all potentially introduce errors into the Doppler moment estimates. In this article, the accuracies of the first three Doppler moment estimates from the ARM MMCRs are evaluated for a set of typical cloud conditions from the three DOE ARM program sites. Results of the analysis suggest that significant errors in the Doppler moment estimates are possible in the current configurations of the ARM MMCRs. In particular, weakly reflecting clouds with low signal-to-noise ratios (SNRs), as well as turbulent clouds with nonzero updraft and downdraft velocities that are coupled with high SNR, are shown to produce degraded Doppler moment estimates in the current ARM MMCR operational mode processing strategies. Analysis of the Doppler moment estimates and MMCR receiver noise characteristics suggests that the introduction of a set of quality control criteria is necessary for identifying periods of degraded receiver performance that leads to larger uncertainties in the Doppler moment estimates. Moreover, the temporal sampling of the ARM MMCRs was found to be insufficient for representing the actual dynamical states in many types of clouds, especially boundary layer clouds. New digital signal processors (DSPs) are currently being developed for the ARM MMCRs. The findings presented in this study will be used in the design of a new set of operational strategies for the ARM MMCRs once they have been upgraded with the new DSPs.
Abstract
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates millimeter-wavelength cloud radars in several climatologically distinct regions. The digital signal processors for these radars were recently upgraded and allow for enhancements in the operational parameters running on them. Recent evaluations of millimeter-wavelength cloud radar signal processing performance relative to the range of cloud dynamical and microphysical conditions encountered at the ARM Program sites have indicated that improvements are necessary, including significant improvement in temporal resolution (i.e., less than 1 s for dwell and 2 s for dwell and processing), wider Nyquist velocities, operational dealiasing of the recorded spectra, removal of pulse compression while sampling the boundary layer, and continuous recording of Doppler spectra. A new set of millimeter-wavelength cloud radar operational modes that incorporate these enhancements is presented. A significant change in radar sampling is the introduction of an uneven mode sequence with 50% of the sampling time dedicated to the lower atmosphere, allowing for detailed characterization of boundary layer clouds. The changes in the operational modes have a substantial impact on the postprocessing algorithms that are used to extract cloud information from the radar data. New methods for postprocessing of recorded Doppler spectra are presented that result in more accurate identification of radar clutter (e.g., insects) and extraction of turbulence and microphysical information. Results of recent studies on the error characteristics of derived Doppler moments are included so that uncertainty estimates are now included with the moments. The microscale data product based on the increased temporal resolution of the millimeter-wavelength cloud radars is described. It contains the number of local maxima in each Doppler spectrum, the Doppler moments of the primary peak, uncertainty estimates for the Doppler moments of the primary peak, Doppler moment shape parameters (e.g., skewness and kurtosis), and clear-air clutter flags.
Abstract
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates millimeter-wavelength cloud radars in several climatologically distinct regions. The digital signal processors for these radars were recently upgraded and allow for enhancements in the operational parameters running on them. Recent evaluations of millimeter-wavelength cloud radar signal processing performance relative to the range of cloud dynamical and microphysical conditions encountered at the ARM Program sites have indicated that improvements are necessary, including significant improvement in temporal resolution (i.e., less than 1 s for dwell and 2 s for dwell and processing), wider Nyquist velocities, operational dealiasing of the recorded spectra, removal of pulse compression while sampling the boundary layer, and continuous recording of Doppler spectra. A new set of millimeter-wavelength cloud radar operational modes that incorporate these enhancements is presented. A significant change in radar sampling is the introduction of an uneven mode sequence with 50% of the sampling time dedicated to the lower atmosphere, allowing for detailed characterization of boundary layer clouds. The changes in the operational modes have a substantial impact on the postprocessing algorithms that are used to extract cloud information from the radar data. New methods for postprocessing of recorded Doppler spectra are presented that result in more accurate identification of radar clutter (e.g., insects) and extraction of turbulence and microphysical information. Results of recent studies on the error characteristics of derived Doppler moments are included so that uncertainty estimates are now included with the moments. The microscale data product based on the increased temporal resolution of the millimeter-wavelength cloud radars is described. It contains the number of local maxima in each Doppler spectrum, the Doppler moments of the primary peak, uncertainty estimates for the Doppler moments of the primary peak, Doppler moment shape parameters (e.g., skewness and kurtosis), and clear-air clutter flags.
