Search Results

You are looking at 1 - 10 of 30 items for

  • Author or Editor: P. Kollias x
  • Refine by Access: All Content x
Clear All Modify Search
Edward P. Luke
and
Pavlos Kollias

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.

Full access
Virendra P. Ghate
and
Pavlos Kollias

Abstract

The Amazon plays an important role in the global energy and hydrological budgets. The precipitation during the dry season (June–September) plays a critical role in maintaining the extent of the rain forest. The deployment of the first Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF-1) in the context of the Green Ocean Amazon (GOAmazon) field campaign at Manacapuru, Brazil, provided comprehensive measurements of surface, cloud, precipitation, radiation, and thermodynamic properties for two complete dry seasons (2014 and 2015). The precipitation events occurring during the nighttime were associated with propagating storm systems (nonlocal effects), while the daytime precipitation events were primarily a result of local land–atmosphere interactions. During the two dry seasons, precipitation was recorded at the surface on 106 days (43%) from 158 rain events with 82 daytime precipitation events occurring on 64 days (60.37%). Detailed comparisons between the diurnal cycles of surface and profile properties between days with and without daytime precipitation suggested the increased moisture at low and midlevels to be responsible for lowering the lifting condensation level, reducing convective inhibition and entrainment, and thus triggering the transition from shallow to deep convection. Although the monthly accumulated rainfall decreased during the progression of the dry season, the contribution of daytime precipitation to it increased, suggesting the decrease to be mainly due to reduction in propagating squall lines. The control of daytime precipitation during the dry season on large-scale moisture advection above the boundary layer and the total rainfall on propagating squall lines suggests that coarse-resolution models should be able to accurately simulate the dry season precipitation over the Amazon basin.

Full access
Scott E. Giangrande
,
Edward P. Luke
, and
Pavlos Kollias

Abstract

Extended, high-resolution measurements of vertical air motion and median volume drop diameter D0 in widespread precipitation from three diverse Atmospheric Radiation Measurement Program (ARM) locations [Lamont, Oklahoma, Southern Great Plains site (SGP); Niamey, Niger; and Black Forest, Germany] are presented. The analysis indicates a weak (0–10 cm−1) downward air motion beneath the melting layer for all three regions, a magnitude that is to within the typical uncertainty of the retrieval methods. On average, the hourly estimated standard deviation of the vertical air motion is 0.25 m s−1 with no pronounced vertical structure. Profiles of D0 vary according to region and rainfall rate. The standard deviation of 1-min-averaged D0 profiles for isolated rainfall rate intervals is 0.3–0.4 mm. Additional insights into the form of the raindrop size distribution are provided using available dual-frequency Doppler velocity observations at SGP. The analysis suggests that gamma functions better explain paired velocity observations and radar retrievals for the Oklahoma dataset. This study will be useful in assessing uncertainties introduced in the measurement of precipitation parameters from ground-based and spaceborne remote sensors that are due to small-scale variability.

Full access
P. Kollias
,
B. A. Albrecht
,
R. Lhermitte
, and
A. Savtchenko

Abstract

Observations from a 94-GHz radar are used to define the vertical structure of marine fair-weather cumuli. Doppler spectra obtained from the radar provide mean vertical velocities as well as detailed spectral shapes that can be used to infer small-scale vertical velocity shear, illuminate cloud microphysical processes, and provide estimates of turbulence dissipation rates. These new observations facilitate the analysis and understanding of in-cloud circulations and the physical processes involved, since the cloud boundaries and dimensions are mapped along with the internal structure of the clouds. Coincident observations from a 915-MHz radar (wind profiler) were used to further define the turbulence structure in and around the clouds. The observations document the detailed vertical and horizontal dimensions of updraft and downdraft circulations in the clouds observed. The two cumuli studied in detail have similar circulation patterns—an updraft core surrounded by downdrafts. Although the clouds have a horizontal depth of only about 700 m, updraft velocities of about 5.5 m s−1 were observed. These updrafts, which are only about 400 m across, exhibit characteristics that are consistent with adiabatic ascent, and penetrate about 150 m into the capping inversion. No penetrating downdrafts are observed within the updraft cores. The downdrafts that flank the updraft on the downwind side of the cloud are relatively narrow (less than 100 m) and extend from cloud top to cloud base. The downdraft on the upwind side of the cloud is about 150 m across and penetrates about 200 m into the detraining cloud mass observed in this part of the cloud. This downdraft appears to be driven by the cooling associated with entrainment mixing at cloud top penetrating through detraining, dynamically inactive parts of the cloud matter. Analysis of the Doppler spectrum at the updraft–downdraft interfaces indicates large Doppler spectral widths due to turbulence and sharp shear zones in the radar sampling volume. Large Doppler spectral widths in the detraining upwind part of the cloud are consistent with the presence of larger droplets. The updraft core structure in one of the clouds has a structure that is consistent with the idea that cumulus clouds are composed of successive bubbles that emerge from the subcloud layer. Thus these small cumuli should be considered as convective complexes rather than simple growing elements that later decay into passive clouds. This study illustrates the potential of millimeter-wavelength radars for studying small cumuli.

Full access
Paloma Borque
,
Edward P. Luke
,
Pavlos Kollias
, and
Fan Yang

Abstract

Turbulence and drizzle-rate measurements from a large dataset of marine and continental low stratiform clouds are presented. Turbulence peaks at cloud base over land and near cloud top over the ocean. For both regions, eddy dissipation rate values of 10−5–10−2 m2 s−3 are observed. Surface-based measurements of cloud condensation nuclei number concentration N CCN and liquid water path (LWP) are used to estimate the precipitation susceptibility S 0. Results show that positive S 0 values are found at low turbulence, consistent with the principle that aerosols suppress precipitation formation, whereas S 0 is smaller, and can be negative, in a more turbulent environment. Under similar macrophysical conditions, especially for medium to high LWP, high (low) turbulence is likely to lessen (promote) the suppression effect of high N CCN on precipitation. Overall, the turbulent effect on S 0 is stronger in continental than marine stratiform clouds. These observational findings are consistent with recent analytical prediction for a turbulence-broadening effect on cloud droplet size distribution.

Full access
Scott E. Giangrande
,
Edward P. Luke
, and
Pavlos Kollias

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.

Full access
Katia Lamer
,
Pavlos Kollias
,
Edward P. Luke
,
Bernat P. Treserras
,
Mariko Oue
, and
Brenda Dolan

Abstract

Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

Restricted access
Matthew D. Shupe
,
Pavlos Kollias
,
P. Ola G. Persson
, and
Greg M. McFarquhar

Abstract

The characteristics of Arctic mixed-phase stratiform clouds and their relation to vertical air motions are examined using ground-based observations during the Mixed-Phase Arctic Cloud Experiment (MPACE) in Barrow, Alaska, during fall 2004. The cloud macrophysical, microphysical, and dynamical properties are derived from a suite of active and passive remote sensors. Low-level, single-layer, mixed-phase stratiform clouds are typically topped by a 400–700-m-deep liquid water layer from which ice crystals precipitate. These clouds are strongly dominated (85% by mass) by liquid water. On average, an in-cloud updraft of 0.4 m s−1 sustains the clouds, although cloud-scale circulations lead to a variability of up to ±2 m s−1 from the average. Dominant scales-of-variability in both vertical air motions and cloud microphysical properties retrieved by this analysis occur at 0.5–10-km wavelengths. In updrafts, both cloud liquid and ice mass grow, although the net liquid mass growth is usually largest. Between updrafts, nearly all ice falls out and/or sublimates while the cloud liquid diminishes but does not completely evaporate. The persistence of liquid water throughout these cloud cycles suggests that ice-forming nuclei, and thus ice crystal, concentrations must be limited and that water vapor is plentiful. These details are brought together within the context of a conceptual model relating cloud-scale dynamics and microphysics.

Full access
Edward P. Luke
,
Pavlos Kollias
,
Karen L. Johnson
, and
Eugene E. Clothiaux

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.

Full access
Pavlos Kollias
,
Mark A. Miller
,
Edward P. Luke
,
Karen L. Johnson
,
Eugene E. Clothiaux
,
Kenneth P. Moran
,
Kevin B. Widener
, and
Bruce A. Albrecht

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.

Full access