Abstract

An error budget analysis is performed for retrieval of along-track winds based on the design of a spaceborne Doppler radar using polarization diversity. The analysis is conducted within the framework of a case study of an Atlantic hurricane. The proposed concept consists of either a Ka-band or W-band stereoradar mounted on an LEO satellite equipped with both nadir- and forward-viewing beams and with an optional cross-scanning capability. Such a radar design is intended for observing the microphysical and dynamical structures of cloud systems, including disturbed mesoscale convective systems. Because of the high winds involved in such weather phenomena and because of the Doppler fading introduced by platform motion, polarization diversity is adopted. The simulation framework enables a breakdown of the Doppler velocity measurement error budget into its most important components, that is, nonuniform beamfilling, multiple scattering, and inherent signal noise. The impact of each of these error terms on the total error depends on the adopted integration length, the number of scanned tracks, and the specifics of the radar. This allows for optimally selecting an integration length suitable for minimizing the total rms velocity error. The analysis shows that the use of a large antenna could achieve impressive measurement accuracy of the along-line-of-sight wind velocities. Notably, this would be the case for integration lengths longer than 3 km, even when carrying out cross-track scanning for up to 17 separate tracks. Examples of retrieved along-track wind fields also reveal that the large antenna configurations are capable of identifying and quantifying the foremost dynamic features (e.g., vertical wind shear and convergence/divergence regions).

Abstract

Spaceborne Doppler radars have the potential to provide key missing observations of convective vertical air motions especially over the tropical oceans. Such measurements can improve understanding of the role of tropical convection in vertical energy transport and its interaction with the environment. Several millimeter wavelength Doppler radar concepts have been proposed since the 1990s. The Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) Cloud Profiling Radar (CPR) will be the first Dopplerized atmospheric radar in space but has not been optimized for Doppler measurements in deep convective clouds.

The key challenge that constrains the CPR performance in convective clouds is the range–Doppler dilemma. Polarization diversity (PD) offers a solution to this constraint by decoupling the coherency (Doppler) requirement from the unambiguous range requirement. Careful modeling of the radar signal depolarization and its impact on radar receiver channel cross talk is needed to accurately assess the performance of the PD approach.

The end-to-end simulator presented in this work allows reproduction of the signal sensed by a Doppler radar equipped with polarization diversity when overpassing realistic three-dimensional convective cells, with all relevant cross-talk sources accounted for. The notional study highlights that multiple scattering is the primary source of cross talk, highly detrimental for millimeter Doppler velocity accuracy. The ambitious scientific requirement of 1 m s⁻¹ accuracy at 500-m integration for reflectivities above −15 dBZ are within reach for a W-band radar with a 2.5-m antenna with optimal values of the pulse-pair interval between 20 and 30 μs but only once multiple scattering and ghost-contaminated regions are screened out. The identification of such areas is key for Doppler accuracies and can be achieved by employing an interlaced pulse-pair mode that measures the cross and the copolar reflectivities. To mitigate the impact of attenuation and multiple scattering, the Ka band has been considered as either alternative or additional to the W band. However, a Ka system produces worse Doppler performances than a W-band system with the same 2.5-m antenna size. Furthermore, in deep convection it results in similar levels of multiple scattering and therefore it does not increase significantly the depth of penetration. In addition, the larger footprint causes stronger nonuniform beam-filling effects. One advantage of the Ka-band option is the larger Nyquist velocity that tends to reduce the Doppler accuracies. More significant benefits are derived from the Ka band when observing precipitation not as intense as the deep convection is considered here.

This study demonstrates that polarization diversity indeed represents a very promising methodology capable of significantly reducing aliasing and Doppler moment estimate errors, two main error sources for Doppler velocity estimates in deep convective systems and a key step to achieving typical mission requirements for convection-oriented millimeter radar-based spaceborne missions.

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

Light shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol–cloud–precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Jülich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better.

Abstract

The joint European Space Agency–Japan Aerospace Exploration Agency (ESA–JAXA) Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is scheduled for launch in 2016 and features the first atmospheric Cloud Profiling Radar (CPR) with Doppler capability in space. Here, the uncertainty of the CPR Doppler velocity measurements in cirrus clouds and large-scale precipitation areas is discussed. These regimes are characterized by weak vertical motion and relatively horizontally homogeneous conditions and thus represent optimum conditions for acquiring high-quality CPR Doppler measurements. A large dataset of radar reflectivity observations from ground-based radars is used to examine the homogeneity of the cloud fields at the horizontal scales of interest. In addition, a CPR instrument model that uses as input ground-based radar observations and outputs simulations of CPR Doppler measurements is described. The simulator accurately accounts for the beam geometry, nonuniform beam-filling, and signal integration effects, and it is applied to representative cases of cirrus cloud and stratiform precipitation. The simulated CPR Doppler velocities are compared against those derived from the ground-based radars. The unfolding of the CPR Doppler velocity is achieved using simple conditional rules and a smoothness requirement for the CPR Doppler measurements. The application of nonuniform beam-filling Doppler velocity bias-correction algorithms is found necessary even under these optimum conditions to reduce the CPR Doppler biases. Finally, the analysis indicates that a minimum along-track integration of 5000 m is needed to reduce the uncertainty in the CPR Doppler measurements to below 0.5 m s⁻¹ and thus enable the detection of the melting layer and the characterization of the rain- and ice-layer Doppler velocities.

Abstract

Two-dimensional water vapor fields were retrieved by simulated measurements from multiple ground-based microwave radiometers using a tomographic approach. The goal of this paper was to investigate how the various aspects of the instrument setup (number and spacing of elevation angles and of instruments, number of frequencies, etc.) affected the quality of the retrieved field. This was done for two simulated atmospheric water vapor fields: 1) an exaggerated turbulent boundary layer and 2) a simplified water vapor front. An optimal estimation algorithm was used to obtain the tomographic field from the microwave radiometers and to evaluate the fidelity and information content of this retrieved field.

While the retrieval of the simplified front was reasonably successful, the retrieval could not reproduce the details of the turbulent boundary layer field even using up to nine instruments and 25 elevation angles. In addition, the vertical profile of the variability of the water vapor field could not be captured. An additional set of tests was performed using simulated data from a Raman lidar. Even with the detailed lidar measurements, the retrieval did not succeed except when the lidar data were used to define the a priori covariance matrix. This suggests that the main limitation to obtaining fine structures in a retrieved field using tomographic retrievals is the definition of the a priori covariance matrix.

Abstract

A method for deriving vertical air motions from cloud radar Doppler spectrum measurements is introduced. The method is applicable to cloud volumes containing small particles, in this case liquid droplets, which are assumed to trace vertical air motions because of their limited size. The presence of liquid droplets is confirmed using multiple ground-based remote sensors. Corrections for Doppler spectrum broadening due to turbulence, wind shear, and radar beamwidth are applied. As a result of the turbulence broadening correction, the turbulent dissipation rate can also be estimated. This retrieval is demonstrated using measurements from the Department of Energy (DOE) Atmospheric Radiation Measurement Program’s (ARM) site in Barrow, Alaska, during the Mixed-Phase Arctic Cloud Experiment (MPACE) of autumn 2004. Comparisons of the retrievals with measurements by research aircraft near Barrow indicate that, on the whole, the retrievals perform well. A small bias in vertical velocity between the retrievals and aircraft measurements is found, based on a statistical comparison of four cases comprising nearly 6 h of data. Turbulent dissipation rate comparisons suggest that the radar-retrieved vertical velocity might be slightly underestimated because of an underestimate of the turbulence broadening correction. However, large uncertainties in aircraft vertical velocity measurements likely impact the comparison.

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 acquisition of scanning cloud radars by the Atmospheric Radiation Measurement (ARM) program and research institutions around the world generates the need for developing operational scan strategies for cloud radars. Here, the first generation of sampling strategies for the scanning ARM cloud radars (SACRs) is presented. These scan strategies are designed to address the scientific objectives of ARM; however, they introduce an initial framework for operational scanning cloud radars. While the weather community uses scan strategies that are based on a sequence of scans at constant elevations, the SACR scan strategies are based on a sequence of scans at constant azimuth. This is attributed to the cloud geometrical properties, which are vastly different from the rain and snow shafts that are the primary targets of precipitation radars; the need to cover the cone of silence; and the scanning limitations of the SACRs. A “cloud surveillance” scan strategy is introduced that is based on a sequence of horizon-to-horizon range–height indicator (RHI) scans that sample the hemispherical sky (HS) every 30° azimuth (HSRHI). The HSRHI scan strategy is complimented with a low-elevation plan position indicator (PPI) scan. The HSRHI and PPI are repeated every 30 min to provide a static view of the cloud conditions around the SACR location. Between the HSRHI and PPI scan strategies, other scan strategies are introduced depending on the cloud conditions. In the future, information about the atmospheric cloud state will be used in a closed-loop process to optimize the selection of the SACR scan strategy.

Abstract

The Department of Energy Atmospheric Radiation Measurement (ARM) Program has recently initiated a new research avenue toward a better characterization of the transition from cloud to precipitation. Dual-wavelength techniques applied to millimeter-wavelength radars and a Rayleigh reference have a great potential for rain-rate retrievals directly from dual-wavelength ratio measurements. In this context, the recent reconfiguration of the ARM 915-MHz wind profilers in a vertically pointing mode makes these instruments the ideal candidate for providing the Rayleigh reflectivity/Doppler velocity reference. Prior to any scientific study, the wind profiler data must be carefully quality checked. This work describes the signal postprocessing steps that are essential for the delivery of high-quality reflectivity and mean Doppler velocity products—that is, the estimation of the noise floor from clear-air echoes, the absolute calibration with a collocated disdrometer, the dealiasing of Doppler velocities, and the merging of the different modes of the wind profiler. The improvement added by the proposed postprocessing is confirmed by comparison with a high-quality S-band profiler deployed at the ARM Southern Great Plains site during the Midlatitude Continental Convective Clouds Experiment. With the addition of a vertically pointing mode and with the postprocessing described in this work in place, besides being a key asset for wind research wind profilers observations may therefore become a centerpiece for rain studies in the years to come.