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A. R. Robinson
,
H. G. Arango
,
A. J. Miller
,
A. Warn-Varnas
,
P.-M. Poulain
, and
W. G. Leslie

Real-time operational shipboard forecasts of Iceland–Faeroe frontal variability were executed for the first time with a primitive equation model. High quality, intensive hydrographic surveys during August 1993 were used for initialization, updating, and validation of the forecasts. Vigorous and rapid synoptic events occurred over several-day timescales including a southeastward reorientation of the Iceland–Faeroe Front and the development of a strong, cold deep-sock meander. A qualitative and quantitative assessment of the skill of these forecasts shows they captured the essential features of both events. The anomaly pattern correlation coefficient and the rms error between forecast and observed fields are particularly impressive (and substantially superior to persistence) for the forecast of the cold meander.

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Eugene E. Clothiaux
,
Thomas P. Ackerman
,
Gerald G. Mace
,
Kenneth P. Moran
,
Roger T. Marchand
,
Mark A. Miller
, and
Brooks E. Martner

Abstract

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program is deploying sensitive, millimeter-wave cloud radars at its Cloud and Radiation Test Bed (CART) sites in Oklahoma, Alaska, and the tropical western Pacific Ocean. The radars complement optical devices, including a Belfort or Vaisala laser ceilometer and a micropulse lidar, in providing a comprehensive source of information on the vertical distribution of hydrometeors overhead at the sites. An algorithm is described that combines data from these active remote sensors to produce an objective determination of hydrometeor height distributions and estimates of their radar reflectivities, vertical velocities, and Doppler spectral widths, which are optimized for accuracy. These data provide fundamental information for retrieving cloud microphysical properties and assessing the radiative effects of clouds on climate. The algorithm is applied to nine months of data from the CART site in Oklahoma for initial evaluation. Much of the algorithm’s calculations deal with merging and optimizing data from the radar’s four sequential operating modes, which have differing advantages and limitations, including problems resulting from range sidelobes, range aliasing, and coherent averaging. Two of the modes use advanced phase-coded pulse compression techniques to yield approximately 10 and 15 dB more sensitivity than is available from the two conventional pulse modes. Comparison of cloud-base heights from the Belfort ceilometer and the micropulse lidar confirms small biases found in earlier studies, but recent information about the ceilometer brings the agreement to within 20–30 m. Merged data of the radar’s modes were found to miss approximately 5.9% of the clouds detected by the laser systems. Using data from only the radar’s two less-sensitive conventional pulse modes would increase the missed detections to 22%–34%. A significant remaining problem is that the radar’s lower-altitude data are often contaminated with echoes from nonhydrometeor targets, such as insects.

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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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Ralph A. Kahn
,
John A. Ogren
,
Thomas P. Ackerman
,
Jens Bösenberg
,
Robert J. Charlson
,
David J. Diner
,
Brent N. Holben
,
Robert T. Menzies
,
Mark A. Miller
, and
John H. Seinfeld

We briefly but systematically review major sources of aerosol data, emphasizing suites of measurements that seem most likely to contribute to assessments of global aerosol climate forcing. The strengths and limitations of existing satellite, surface, and aircraft remote sensing systems are described, along with those of direct sampling networks and ship-based stations. It is evident that an enormous number of aerosol-related observations have been made, on a wide range of spatial and temporal sampling scales, and that many of the key gaps in this collection of data could be filled by technologies that either exist or are expected to be available in the near future. Emphasis must be given to combining remote sensing and in situ active and passive observations and integrating them with aerosol chemical transport models, in order to create a more complete environmental picture, having sufficient detail to address current climate forcing questions. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) initiative would provide an organizational framework to meet this goal.

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Matt C. Wilbanks
,
Sandra E. Yuter
,
Simon P. de Szoeke
,
W. Alan Brewer
,
Matthew A. Miller
,
Andrew M. Hall
, and
Casey D. Burleyson

Abstract

Density currents (i.e., cold pools or outflows) beneath marine stratocumulus clouds are characterized using 30 days of ship-based observations obtained during the 2008 Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) in the southeast Pacific. An air density increase criterion applied to the Improved Meteorological (IMET) sensor data identified 71 density current front, core (peak density), and tail (dissipating) zones. The similarity in speeds of the mean density current propagation speed (1.8 m s−1) and the mean cloud-level advection relative to the surface layer wind (1.9 m s−1) allowed drizzle cells to deposit elongated density currents in their wakes. Scanning Doppler lidar captured prefrontal updrafts with a mean intensity of 0.91 m s−1 and an average vertical extent of 800 m. Updrafts were often surmounted by low-lying shelf clouds not connected to the overlying stratocumulus cloud. The observed density currents were 5–10 times thinner and weaker than typical continental thunderstorm cold pools. Nearly 90% of density currents were identified when C-band radar estimated areal average rain rates exceeded 1 mm day−1 over a 30-km diameter. Rather than peaking when rain rates were highest overnight, density current occurrence peaks between 0600 and 0800 local solar time when enhanced local drizzle co-occurred with shallow subcloud dry and stable layers. The dry layers may have contributed to density current formation by enhancing subcloud evaporation of drizzle. Density currents preferentially occurred in a large region of predominantly open cells but also occurred in regions of closed cells.

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Ben C. Bernstein
,
Frank McDonough
,
Marcia K. Politovich
,
Barbara G. Brown
,
Thomas P. Ratvasky
,
Dean R. Miller
,
Cory A. Wolff
, and
Gary Cunning

Abstract

The “current icing potential” (CIP) algorithm combines satellite, radar, surface, lightning, and pilot-report observations with model output to create a detailed three-dimensional hourly diagnosis of the potential for the existence of icing and supercooled large droplets. It uses a physically based situational approach that is derived from basic and applied cloud physics, combined with forecaster and onboard flight experience from field programs. Both fuzzy logic and decision-tree logic are applied in this context. CIP determines the locations of clouds and precipitation and then estimates the potential for the presence of supercooled liquid water and supercooled large droplets within a given airspace. First developed in the winter of 1997/98, CIP became an operational National Weather Service and Federal Aviation Administration product in 2002, providing real-time diagnoses that allow users to make route-specific decisions to avoid potentially hazardous icing. The CIP algorithm, its individual components, and the logic behind them are described.

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David B. Mechem
,
Carly S. Wittman
,
Matthew A. Miller
,
Sandra E. Yuter
, and
Simon P. de Szoeke

Abstract

Marine boundary layer clouds are modified by processes at different spatial and temporal scales. To isolate the processes governing aerosol–cloud–precipitation interactions, multiday synoptic variability of the environment must be accounted for. Information on the location of low clouds relative to the ridge–trough pattern gives insight into how cloud properties vary as a function of environmental subsidence and stability. The technique of self-organizing maps (SOMs) is employed to objectively classify the 500-hPa geopotential height patterns for 33 years of reanalysis fields (ERA-Interim) into pretrough, trough, posttrough, ridge, and zonal-flow categories. The SOM technique is applied to a region of prevalent marine low cloudiness over the eastern North Atlantic Ocean that is centered on the Azores island chain, the location of a long-term U.S. Department of Energy observation site. The Azores consistently lie in an area of substantial variability in synoptic configuration, thermodynamic environment, and cloud properties. The SOM method was run in two ways to emphasize multiday and seasonal variability separately. Over and near the Azores, there is an east-to-west sloshing back and forth of the western edge of marine low clouds associated with different synoptic states. The different synoptic states also exhibit substantial north–south variability in the position of high clouds. For any given month of the year, there is large year-to-year variability in the occurrence of different synoptic states. Hence, estimating the climatological behavior of clouds from short-term field campaigns has large uncertainties. This SOM approach is a robust method that is broadly applicable to characterizing synoptic regimes for any location.

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Richard D. Rosen
,
David A. Salstein
,
Thomas Nehrkorn
,
Margaret R.P. McCalla
,
Alvin J. Miller
,
Jean O. Dickey
,
T. Marshall Eubanks
, and
J. Alan Steppe

Abstract

Forecasts of zonal wind fields produced by the medium-range forecast (MRF) model of the National Meteorological Center are used to create predictions of the atmosphere's angular momentum at lead times of 1–10 days. Forecasts of this globally integrated quantity are of interest to geodesists and others concerned with monitoring changes in the earth's orientation for navigational purposes. Based on momentum forecasts archived for the period December 1985–November 1986, we find that, on average, the MRF exhibits positive skill relative to persistence-based forecasts at all lead times. Over our entire one-year study period, the improvement over persistence exceeds 20% for 2–6-day forecasts and remains as large as 10% even for 10-day forecasts. On the other hand, skill scores for the MRF momentum predictions vary considerably from month to month, and for a sizeable fraction of our study period the MRF is less skillful than persistence. Thus, although our initial impression of the overall quality of the MRF momentum forecasts is favorable, further improvement is certainly desirable.

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