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  • Author or Editor: P. A. Miller x
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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.

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E. E. Clothiaux
,
M. A. Miller
,
B. A. Albrecht
,
T. P. Ackerman
,
J. Verlinde
,
D. M. Babb
,
R. M. Peters
, and
W. J. Syrett

Abstract

The performance of a 94-GHz radar is evaluated for a variety of cloud conditions. Descriptions of the radar hardware, signal processing, and calibration provide an overview of the radar's capabilities. An important component of the signal processing is the application of two cloud-mask schemes to the data to provide objective estimates of cloud boundaries and to detect significant returns that would otherwise be discarded if a simple threshold method for delectability was applied to the return power. Realistic profiles of atmospheric pressure, temperature, and water vapor are used in a radiative transfer model to address clear-sky attenuation. A physically relevant study of beam extinction and backscattering by clouds is attempted by modeling cloud drop size distributions with a gamma distribution over a range of number concentrations, particle mean diameters, and distribution shape factors; cloud liquid water contents and mean drop size diameters reported in the literature are analyzed in this context. Results of observations of a number of cloud structures, including marine strato- cumulus, cirrus, and stratus and cirrus associated with a midlatitude cyclone are described.

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Pavlos Kollias
,
Bruce A. Albrecht
,
Eugene E. Clothiaux
,
Mark A. Miller
,
Karen L. Johnson
, and
Kenneth P. Moran

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.

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Anna P. M. Michel
,
David J. Miller
,
Kang Sun
,
Lei Tao
,
Levi Stanton
, and
Mark A. Zondlo

Abstract

A long-path methane (CH4) sensor was developed and field deployed using an 8-μm quantum cascade laser. The high optical power (40 mW) of the laser allowed for path-integrated measurements of ambient CH4 at total pathlengths from 100 to 1200 m with the use of a retroreflector. Wavelength modulation spectroscopy was used to make high-precision measurements of atmospheric pressure–broadened CH4 absorption over these long distances. An in-line reference cell with higher harmonic detection provided metrics of system stability in rapidly changing and harsh environments. The system consumed less than 100 W of power and required no consumables. The measurements intercompared favorably (typically less than 5% difference) with a commercial in situ methane sensor when accounting for the different spatiotemporal scales of the measurements. The sensor was field deployed for 2 weeks at an arctic lake to examine the robustness of the approach in harsh field environments. Short-term precision over a 458-m pathlength was 10 ppbv at 1 Hz, equivalent to a signal from a methane enhancement above background of 5 ppmv in a 1-m length. The sensor performed well in a range of harsh environmental conditions, including snow, rain, wind, and changing temperatures. These field measurements demonstrate the capabilities of the approach for use in detecting large but highly variable emissions in arctic environments.

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Eugene E. Clothiaux
,
Kenneth P. Moran
,
Brooks E. Martner
,
Thomas P. Ackerman
,
Gerald G. Mace
,
Taneil Uttal
,
James H. Mather
,
Kevin B. Widener
,
Mark A. Miller
, and
Daniel J. Rodriguez

Abstract

During the past decade, the U.S. Department of Energy (DOE), through the Atmospheric Radiation Measurement (ARM) Program, has supported the development of several millimeter-wavelength radars for the study of clouds. This effort has culminated in the development and construction of a 35-GHz radar system by the Environmental Technology Laboratory (ETL) of the National Oceanic and Atmospheric Administration (NOAA). Radar systems based on the NOAA ETL design are now operating at the DOE ARM Southern Great Plains central facility in central Oklahoma and the DOE ARM North Slope of Alaska site near Barrow, Alaska. Operational systems are expected to come online within the next year at the DOE ARM tropical western Pacific sites located at Manus, Papua New Guinea, and Nauru. In order for these radars to detect the full range of atmospheric hydrometeors, specific modes of operation must be implemented on them that are tuned to accurately detect the reflectivities of specific types of hydrometeors. The set of four operational modes that are currently in use on these radars are presented and discussed. The characteristics of the data produced by these modes of operation are also presented in order to illustrate the nature of the cloud products that are, and will be, derived from them on a continuous basis.

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B. L. Weber
,
D. B. Wuertz
,
R. G. Strauch
,
D. A. Merritt
,
K. P. Moran
,
D. C. Law
,
D. van de Kamp
,
R. B. Chadwick
,
M. H. Ackley
,
M. F. Barth
,
N. L. Abshire
,
P. A. Miller
, and
T. W. Schlatter

Abstract

The first wind profiler for a demonstration network of wind profilers recently passed the milestone of 300 h of continuous operation. The horizontal wind component measurements taken during that period are compared with the WPL Platteville wind profiler and the NWS Denver rawinsonde. The differences between the network and WPL wind profilers have standard deviations of 2.30 m s−1 and 2.16 m s−1 for the u- and v-components, respectively. However, the WPL wind profiler ignores vertical velocity, whereas the network radar measures it and removes its effects from the u- and v-component measurements. The differences between the network wind profiler and the NWS rawinsonde (separated spatially by about 50 km) have standard deviations of 3.65 m s−1 and 3.06 m s−1 for the u- and v-components, respectively. These results are similar to those found in earlier comparison studies. Finally, the new network wind profiler demonstrates excellent sensitivity, consistently reporting measurements at all heights msl from 2 to nearly 18 km with very few outages.

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Theodore M. McHardy
,
James R. Campbell
,
David A. Peterson
,
Simone Lolli
,
Anne Garnier
,
Arunas P. Kuciauskas
,
Melinda L. Surratt
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Jared W. Marquis
,
Steven D. Miller
,
Erica K. Dolinar
, and
Xiquan Dong

Abstract

This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

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Theodore M. McHardy
,
James R. Campbell
,
David A. Peterson
,
Simone Lolli
,
Richard L. Bankert
,
Anne Garnier
,
Arunas P. Kuciauskas
,
Melinda L. Surratt
,
Jared W. Marquis
,
Steven D. Miller
,
Erica K. Dolinar
, and
Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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Jerald A. Brotzge
,
J. Wang
,
C. D. Thorncroft
,
E. Joseph
,
N. Bain
,
N. Bassill
,
N. Farruggio
,
J. M. Freedman
,
K. Hemker Jr.
,
D. Johnston
,
E. Kane
,
S. McKim
,
S. D. Miller
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J. R. Minder
,
P. Naple
,
S. Perez
,
James J. Schwab
,
M. J. Schwab
, and
J. Sicker

Abstract

The New York State Mesonet (NYSM) is a network of 126 standard environmental monitoring stations deployed statewide with an average spacing of 27 km. The primary goal of the NYSM is to provide high-quality weather data at high spatial and temporal scales to improve atmospheric monitoring and prediction, especially for extreme weather events. As compared with other statewide networks, the NYSM faced considerable deployment obstacles with New York’s complex terrain, forests, and very rural and urban areas; its wide range of weather extremes; and its harsh winter conditions. To overcome these challenges, the NYSM adopted a number of innovations unique among statewide monitoring systems, including 1) strict adherence to international siting standards and metadata documentation; 2) a hardened system design to facilitate continued operations during extreme, high-impact weather; 3) a station design optimized to monitor winter weather conditions; and 4) a camera installed at every site to aid situational awareness. The network was completed in spring of 2018 and provides data and products to a variety of sectors including weather monitoring and forecasting, emergency management, agriculture, transportation, utilities, and education. This paper focuses on the standard network of the NYSM and reviews the network siting, site configuration, sensors, site communications and power, network operations and maintenance, data quality control, and dissemination. A few example analyses are shown that highlight the benefits of the NYSM.

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