scholarly journals The Atmospheric Radiation Measurement Program Cloud Profiling Radars: An Evaluation of Signal Processing and Sampling Strategies

2005 ◽  
Vol 22 (7) ◽  
pp. 930-948 ◽  
Author(s):  
Pavlos Kollias ◽  
Bruce A. Albrecht ◽  
Eugene E. Clothiaux ◽  
Mark A. Miller ◽  
Karen L. Johnson ◽  
...  
Keyword(s):  
Sampling Strategy ◽  
Department Of Energy ◽  
Radiative Properties ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Atmospheric Radiation ◽  
Operational Mode ◽  
Temporal Sampling ◽  
Noise Characteristics ◽  
Moment Estimates

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.

Signal Postprocessing and Reflectivity Calibration of the Atmospheric Radiation Measurement Program 915-MHz Wind Profilers

2013 ◽  
Vol 30 (6) ◽  
pp. 1038-1054 ◽  
Author(s):  
Frédéric Tridon ◽  
Alessandro Battaglia ◽  
Pavlos Kollias ◽  
Edward Luke ◽  
Christopher R. Williams
Keyword(s):  
Department Of Energy ◽  
Dual Wavelength ◽  
Doppler Velocity ◽  
Atmospheric Radiation ◽  
Absolute Calibration ◽  
Wind Profiler ◽  
Radiation Measurement ◽  
High Quality ◽  
Atmospheric Radiation Measurement ◽  
Wind Profilers

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.

scholarly journals Developing and Evaluating Ice Cloud Parameterizations for Forward Modeling of Radar Moments Using in situ Aircraft Observations

2015 ◽  
Vol 32 (5) ◽  
pp. 880-903 ◽  
Author(s):  
Maximilian Maahn ◽  
Ulrich Löhnert ◽  
Pavlos Kollias ◽  
Robert C. Jackson ◽  
Greg M. McFarquhar
Keyword(s):  
Size Distribution ◽  
Power Law ◽  
Particle Mass ◽  
Maximum Diameter ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Radar Observations ◽  
Size Relation ◽  
Particle Area

AbstractObserving ice clouds using zenith pointing millimeter cloud radars is challenging because the transfer functions relating the observables to meteorological quantities are not uniquely defined. Here, the authors use a spectral radar simulator to develop a consistent dataset containing particle mass, area, and size distribution as functions of size. This is an essential prerequisite for radar sensitivity studies and retrieval development. The data are obtained from aircraft in situ and ground-based radar observations during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) campaign in Alaska. The two main results of this study are as follows: 1) An improved method to estimate the particle mass–size relation as a function of temperature is developed and successfully evaluated by combining aircraft in situ and radar observations. The method relies on a functional relation between reflectivity and Doppler velocity. 2) The impact on the Doppler spectrum by replacing measurements of particle area and size distribution by recent analytical expressions is investigated. For this, higher-order moments such as skewness and kurtosis as well as the slopes of the Doppler spectrum are also used as a proxy for the Doppler spectrum. For the area–size relation, it is found that a power law is not sufficient to describe particle area and small deviations from a power law are essential for obtaining consistent higher moments. For particle size distributions, the normalization approach for the gamma distribution of Testud et al., adapted to maximum diameter as size descriptor, is preferred.

scholarly journals On the Optimal Design of Doppler Scatterometers

2018 ◽  
Author(s):  
Ernesto Rodriguez
Keyword(s):  
Radial Velocity ◽  
Incidence Angle ◽  
Pulse Length ◽  
Sampling Strategy ◽  
Doppler Spectrum ◽  
Pencil Beam ◽  
Pulse Separation ◽  
Sensitivity Equations ◽  
Antenna Length ◽  
Selection Of

Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, I present guidelines to optimize the design of these instruments starting from the basic sensitivity equations. Unlike conventional scatterometers or pencil-beam imagers, appropriate sampling of the Doppler spectrum and optimizing the radial velocity error lead naturally to a design that incorporates a pulse-to-pulse separation and pulse length that vary with scan angle. Including this variation can improve radial velocity performance significantly and the optimal selection of system timing and bandwidth is derived. Following this, optimization of the performance based on frequency, incidence angle, antenna length, and spatial sampling strategy are considered. It is shown that antenna length influences the performance most strongly, while the errors depend only on the square root of the transmit transmit power. Finally, a set of example designs and associated performance are presented.

scholarly journals Identifying Insects, Clouds, and Precipitation using Vertically Pointing Polarimetric Radar Doppler Velocity Spectra

2021 ◽  
Author(s):  
Christopher R. Williams ◽  
Karen L. Johnson ◽  
Scott E. Giangrande ◽  
Joseph C. Hardin ◽  
Ruşen Öktem ◽  
...  
Keyword(s):  
Power Spectra ◽  
Department Of Energy ◽  
Cloud Base ◽  
Doppler Velocity ◽  
Polarimetric Radar ◽  
Ka Band ◽  
Ice Particles ◽  
Linear Depolarization Ratio ◽  
Velocity Spectra ◽  
Discrimination Method

Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The insect-hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects versus cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra linear depolarization ratio (LDR) produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives this need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Comparison with a collocated ceilometer indicates that hydrometeor mask column bottoms are within +/-100 meters of simultaneous ceilometer cloud base heights. Forty-seven (47) summer-time days were processed with the insect-hydrometeor discrimination method using U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma (USA). All datasets and images are available on public repositories.

scholarly journals Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E

2019 ◽  
Vol 12 (7) ◽  
pp. 3743-3759 ◽  
Author(s):  
Jingjing Tian ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Christopher R. Williams ◽  
Peng Wu
Keyword(s):  
Liquid Water ◽  
Department Of Energy ◽  
Cloud Base ◽  
Retrieval Algorithm ◽  
Doppler Velocity ◽  
Liquid Water Path ◽  
Water Path ◽  
Melting Layer ◽  
Stratiform Precipitation ◽  
Cloud Particles

Abstract. In this study, the liquid water path (LWP) below the melting layer in stratiform precipitation systems is retrieved, which is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP). The retrieval algorithm uses measurements from the vertically pointing radars (VPRs) at 35 and 3 GHz operated by the US Department of Energy Atmospheric Radiation Measurement (ARM) and National Oceanic and Atmospheric Administration (NOAA) during the field campaign Midlatitude Continental Convective Clouds Experiment (MC3E). The measured radar reflectivity and mean Doppler velocity from both VPRs and spectrum width from the 35 GHz radar are utilized. With the aid of the cloud base detected by a ceilometer, the LWP in the liquid layer is retrieved under two different situations: (I) no cloud exists below the melting base, and (II) cloud exists below the melting base. In (I), LWP is primarily contributed from raindrops only, i.e., RLWP, which is estimated by analyzing the Doppler velocity differences between two VPRs. In (II), cloud particles and raindrops coexist below the melting base. The CLWP is estimated using a modified attenuation-based algorithm. Two stratiform precipitation cases (20 and 11 May 2011) during MC3E are illustrated for two situations, respectively. With a total of 13 h of samples during MC3E, statistical results show that the occurrence of cloud particles below the melting base is low (9 %); however, the mean CLWP value can be up to 0.56 kg m−2, which is much larger than the RLWP (0.10 kg m−2). When only raindrops exist below the melting base, the average RLWP value is larger (0.32 kg m−2) than the with-cloud situation. The overall mean LWP below the melting base is 0.34 kg m−2 for stratiform systems during MC3E.

scholarly journals Merged Cloud and Precipitation Dataset from the HIAPER GV for the Cloud System Evolution in the Trades (CSET) Campaign

2019 ◽  
Vol 36 (6) ◽  
pp. 921-940 ◽  
Author(s):  
M. Christian Schwartz ◽  
Virendra P. Ghate ◽  
Bruce. A. Albrecht ◽  
Paquita Zuidema ◽  
Maria P. Cadeddu ◽  
...  
Keyword(s):  
High Performance ◽  
Environmental Research ◽  
High Spectral Resolution ◽  
Cloud System ◽  
Cloud Base ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
System Evolution ◽  
Microphysical Properties ◽  
Fuzzy Logic Technique

AbstractThe Cloud System Evolution in the Trades (CSET) aircraft campaign was conducted in the summer of 2015 in the northeast Pacific to observe the transition from stratocumulus to cumulus cloud regime. Fourteen transects were made between Sacramento, California, and Kona, Hawaii, using the NCAR’s High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Gulfstream V (GV) aircraft. The HIAPER W-band Doppler cloud radar (HCR) and the high-spectral-resolution lidar (HSRL), in their first deployment together on board the GV, provided crucial cloud and precipitation observations. The HCR recorded the raw in-phase (I) and quadrature (Q) components of the digitized signal, from which the Doppler spectra and its first three moments were calculated. HCR/HSRL data were merged to develop a hydrometeor mask on a uniform georeferenced grid of 2-Hz temporal and 20-m vertical resolutions. The hydrometeors are classified as cloud or precipitation using a simple fuzzy logic technique based on the HCR mean Doppler velocity, HSRL backscatter, and the ratio of HCR reflectivity to HSRL backscatter. This is primarily applied during zenith-pointing conditions under which the lidar can detect the cloud base and the radar is more sensitive to clouds. The microphysical properties of below-cloud drizzle and optically thin clouds were retrieved using the HCR reflectivity, HSRL backscatter, and the HCR Doppler spectrum width after it is corrected for the aircraft speed. These indicate that as the boundary layers deepen and cloud-top heights increase toward the equator, both the cloud and rain fractions decrease.

scholarly journals A temporal sampling strategy for hydraulic tomography analysis

2013 ◽  
Vol 49 (7) ◽  
pp. 3881-3896 ◽  
Author(s):  
Ronglin Sun ◽  
Tian-Chyi Jim Yeh ◽  
Deqiang Mao ◽  
Menggui Jin ◽  
Wenxi Lu ◽  
...  
Keyword(s):  
Sampling Strategy ◽  
Hydraulic Tomography ◽  
Temporal Sampling ◽  
Tomography Analysis

scholarly journals Development and validation of a supervised machine learning radar Doppler spectra peak finding algorithm

2019 ◽  
Author(s):  
Heike Kalesse ◽  
Teresa Vogl ◽  
Cosmin Paduraru ◽  
Edward Luke
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Cloud Particle ◽  
Peak Finding ◽  
Atmospheric Radiation Measurement ◽  
Biogenic Aerosols ◽  
Velocity Spectra ◽  
Development And Validation

Abstract. In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time-height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM Zenith-pointing Radar (KAZR) observations obtained in thick snow fall systems during the Atmospheric Radiation Measurement Program’s (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak finding algorithms. The new algorithm is found to perform well. Its advantage is that the detected features are less noisy and more consistent in time and height than the peak finding results of other algorithms. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

Estimating Large-Scale Precipitation Minus Evapotranspiration from GRACE Satellite Gravity Measurements

2006 ◽  
Vol 7 (2) ◽  
pp. 252-270 ◽  
Author(s):  
Sean Swenson ◽  
John Wahr
Keyword(s):  
Land Surface ◽  
Seasonal Cycle ◽  
Large Scale ◽  
Gravity Data ◽  
Water Storage ◽  
Department Of Energy ◽  
Surface Model ◽  
Satellite Gravity ◽  
Discharge Data ◽  
Temporal Sampling

Abstract Currently, observations of key components of the earth's large-scale water and energy budgets are sparse or even nonexistent. One key component, precipitation minus evapotranspiration (P − ET), remains largely unmeasured due to the absence of observations of ET. Precipitation minus evapotranspiration describes the flux of water between the atmosphere and the earth's surface, and therefore provides important information regarding the interaction of the atmosphere with the land surface. In this paper, large-scale changes in continental water storage derived from satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) project are combined with river discharge data to obtain estimates of areally averaged P − ET. After constructing an equation describing the large-scale terrestrial water balance reflecting the temporal sampling of GRACE water storage estimates, GRACE-derived P − ET estimates are compared to those obtained from a reanalysis dataset [NCEP/Department of Energy (DOE) reanalysis-2] and a land surface model driven with observation-based forcing [Global Land Data Assimilation System (GLDAS)/Noah] for two large U.S. river basins. GRACE-derived P − ET compares quite favorably with the reanalysis-2 output, while P − ET from the Noah model shows significant differences. Because the uncertainties in the GRACE results can be computed rigorously, this comparison may be considered as a validation of the models. In addition to showing how GRACE P − ET estimates may be used to validate model output, the accuracy of GRACE estimates of both the seasonal cycle and the monthly averaged rate of P − ET is examined. Finally, the potential for estimating seasonal evapotranspiration is demonstrated by combining GRACE seasonal P − ET estimates with independent estimates of the seasonal cycle of precipitation.

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