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Aronne Merrelli and David D. Turner

Abstract

The information content of high-spectral-resolution midinfrared (MIR; 650–2300 cm−1) and far-infrared (FIR; 200–685 cm−1) upwelling radiance spectra is calculated for clear-sky temperature and water vapor profiles. The wavenumber ranges of the two spectral bands overlap at the central absorption line in the CO2 ν 2 absorption band, and each contains one side of the full absorption band. Each spectral band also includes a water vapor absorption band; the MIR contains the first vibrational–rotational absorption band, while the FIR contains the rotational absorption band. The upwelling spectral radiances are simulated with the line-by-line radiative transfer model (LBLRTM), and the retrievals and information content analysis are computed using standard optimal estimation techniques. Perturbations in the surface temperature and in the trace gases methane, ozone, and nitrous oxide (CH4, O3, and N2O) are introduced to represent forward-model errors. Each spectrum is observed by a simulated infrared spectrometer, with a spectral resolution of 0.5 cm−1, with realistic spectrally varying sensor noise levels. The modeling and analysis framework is applied identically to each spectral range, allowing a quantitative comparison. The results show that for similar sensor noise levels, the FIR shows an advantage in water vapor profile information content and less sensitivity to forward-model errors. With a higher noise level in the FIR, which is a closer match to current FIR detector technology, the FIR information content drops and shows a disadvantage relative to the MIR.

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Véronique Meunier, David D. Turner, and Pavlos Kollias

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.

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Sergey Y. Matrosov and David D. Turner

Abstract

A remote sensing method to retrieve the mean temperature of cloud liquid using ground-based microwave radiometer measurements is evaluated and tested by comparisons with direct cloud temperature information inferred from ceilometer cloud-base measurements and temperature profiles from radiosonde soundings. The method is based on the dependence of the ratio of cloud optical thicknesses at W-band (~90 GHz) and Ka-band (~30 GHz) frequencies on cloud liquid temperature. This ratio is obtained from total optical thicknesses inferred from radiometer measurements of brightness temperatures after accounting for the contributions from oxygen and water vapor. This accounting is done based on the radiometer-based retrievals of integrated water vapor amount and temperature and pressure measurements at the surface. The W–Ka-band ratio method is applied to the measurements from a three-channel (90, 31.4, and 23.8 GHz) microwave radiometer at the U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility at Oliktok Point, Alaska. The analyzed events span conditions from warm stratus clouds with temperatures above freezing to mixed-phase clouds with supercooled liquid water layers. Intercomparisons of radiometer-based cloud liquid temperature retrievals with estimates from collocated ceilometer and radiosonde measurements indicated on average a standard deviation of about 3.5°C between the two retrieval types in a wide range of cloud temperatures, from warm liquid clouds to mixed-phase clouds with supercooled liquid and liquid water paths greater than 50 g m−2. The three-channel microwave radiometer–based method has a broad applicability, since it requires neither the use of active sensors to locate the boundaries of liquid cloud layers nor information on the vertical profile of temperature.

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Scott N. Paine, David D. Turner, and Nils Küchler

Abstract

An absorbing load in a liquid nitrogen bath is commonly used as a radiance standard for calibrating radiometers operating at microwave to infrared wavelengths. It is generally assumed that the physical temperature of the load is stable and equal to the boiling point temperature of pure N2 at the ambient atmospheric pressure. However, this assumption will fail to hold when air movement, as encountered in outdoor environments, allows O2 gas to condense into the bath. Under typical conditions, initial boiling point drift rates of order 25 mK min−1 can occur, and the boiling point of a bath maintained by repeated refilling with pure N2 can eventually shift by approximately 2 K. Laboratory bench tests of a liquid nitrogen bath under simulated wind conditions are presented together with an example of an outdoor radiometer calibration that demonstrates the effect, and the physical processes involved are explained in detail. A key finding is that in windy conditions, changes in O2 volume fraction are related accurately to fractional changes in bath volume due to boiloff, independent of wind speed. This relation can be exploited to ensure that calibration errors due to O2 contamination remain within predictable bounds.

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Rob K. Newsom, David D. Turner, and John E. M. Goldsmith

Abstract

This study investigates the accuracy and calibration stability of temperature profiles derived from an operational Raman lidar over a 2-yr period from 1 January 2009 to 31 December 2010. The lidar, which uses the rotational Raman technique for temperature measurement, is located at the U.S. Department of Energy's Atmospheric Radiation Measurement site near Billings, Oklahoma. The lidar performance specifications, data processing algorithms, and the results of several test runs are described. Calibration and overlap correction of the lidar is achieved using simultaneous and collocated radiosonde measurements. Results show that the calibration coefficients exhibit no significant long-term or seasonal variation but do show a distinct diurnal variation. When the diurnal variation in the calibration is not resolved the lidar temperature bias exhibits a significant diurnal variation. Test runs in which only nighttime radiosonde measurements are used for calibration show that the lidar exhibits a daytime warm bias that is correlated with the strength of the solar background signal. This bias, which reaches a maximum of ~2.4 K near solar noon, is reduced through the application of a correction scheme in which the calibration coefficients are parameterized in terms of the solar background signal. Comparison between the corrected lidar temperatures and the noncalibration radiosonde temperatures show a negligibly small median bias of −0.013 K for altitudes below 10 km AGL. The corresponding root-mean-square difference profile is roughly constant at ~2 K below 6 km AGL and increases to about 4.5 K at 10 km AGL.

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Timothy J. Wagner, David D. Turner, Larry K. Berg, and Steven K. Krueger

Abstract

While fractional entrainment rates for cumulus clouds have typically been derived from airborne observations, this limits the size and scope of available datasets. To increase the number of continental cumulus entrainment rate observations available for study, an algorithm for retrieving them from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Climate Research Facility as inputs into a Gauss–Newton optimal estimation scheme, in which an assumed guess of the entrainment rate is iteratively adjusted through intercomparison of modeled cloud attributes to their observed counterparts. The forward model in this algorithm is the explicit mixing parcel model (EMPM), a cloud parcel model that treats entrainment as a series of discrete entrainment events. A quantified value for the uncertainty in the retrieved entrainment rate is also returned as part of the retrieval. Sensitivity testing and information content analysis demonstrate the robust nature of this method for retrieving accurate observations of the entrainment rate without the drawbacks of airborne sampling. Results from a test of ERICA on 3 months of shallow cumulus cloud events show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value of 1.06 km−1 for the entrainment rate in this dataset corresponds well with prior observations and simulations of the entrainment rate in cumulus clouds.

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Penny M. Rowe, Larry M. Miloshevich, David D. Turner, and Von P. Walden

Abstract

Middle to upper tropospheric humidity plays a large role in determining terrestrial outgoing longwave radiation. Much work has gone into improving the accuracy of humidity measurements made by radiosondes. Some radiosonde humidity sensors experience a dry bias caused by solar heating. During the austral summers of 2002/03 and 2003/04 at Dome C, Antarctica, Vaisala RS90 radiosondes were launched in clear skies at solar zenith angles (SZAs) near 83° and 62°. As part of this field experiment, the Polar Atmospheric Emitted Radiance Interferometer (PAERI) measured downwelling spectral infrared radiance. The radiosonde humidity profiles are used in the simulation of the downwelling radiances. The radiosonde dry bias is then determined by scaling the humidity profile with a height-independent factor to obtain the best agreement between the measured and simulated radiances in microwindows between strong water vapor lines from 530 to 560 cm−1 and near line centers from 1100 to 1300 cm−1. The dry biases, as relative errors in relative humidity, are 8% ± 5% (microwindows; 1σ) and 9% ± 3% (line centers) for SZAs near 83°; they are 20% ± 6% and 24% ± 5% for SZAs near 62°. Assuming solar heating is minimal at SZAs near 83°, the authors remove errors that are unrelated to solar heating and find the solar-radiation dry bias of 9 RS90 radiosondes at SZAs near 62° to be 12% ± 6% (microwindows) and 15% ± 5% (line centers). Systematic errors in the correction are estimated to be 3% and 2% for microwindows and line centers, respectively. These corrections apply to atmospheric pressures between 650 and 200 mb.

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Tyler J. Thorsen, Qiang Fu, Rob K. Newsom, David D. Turner, and Jennifer M. Comstock

Abstract

A feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part I of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitrogen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio—to identify features using range-dependent detection thresholds. FEX is designed to be context sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities provides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically thin features containing nonspherical particles, such as cirrus clouds. Improvements over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia, site. While the focus is on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.

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Robin L. Tanamachi, Stephen J. Frasier, Joseph Waldinger, Allison LaFleur, David D. Turner, and Francesc Rocadenbosch

Abstract

During spring 2016 and spring 2017, a vertically pointing, S-band Frequency Modulated Continuous Wave radar (UMass FMCW) was deployed in northern Alabama under the auspices of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX)-Southeast. In total, ~14 weeks of data were collected, in conditions ranging from quiescent clear skies to severe thunderstorms. The principal objective of these deployments was to characterize the boundary layer evolution near the VORTEX-Southeast domain. In this paper, we describe intermediate results in service of this objective. Specifically, we describe updates to the UMass FMCW system, document its deployments for VORTEX-Southeast, and apply four automated algorithms: 1) a dealiasing algorithm to the Doppler velocities, 2) a fuzzy logic scatterer classification scheme to separate precipitation from nonprecipitation observations, 3) a brightband/melting-layer identification algorithm for stratiform precipitation, and 4) an extended Kalman filter–based convective boundary layer depth (mixing height) measurement algorithm for nonprecipitation observations. Results from the latter two applications are qualitatively verified against retrieved soundings from a collocated thermodynamic profiling system.

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Tetsu Sakai, David N. Whiteman, Felicita Russo, David D. Turner, Igor Veselovskii, S. Harvey Melfi, Tomohiro Nagai, and Yuzo Mano

Abstract

This paper describes recent work in the Raman lidar liquid water cloud measurement technique. The range-resolved spectral measurements at the National Aeronautics and Space Administration Goddard Space Flight Center indicate that the Raman backscattering spectra measured in and below low clouds agree well with theoretical spectra for vapor and liquid water. The calibration coefficients of the liquid water measurement for the Raman lidar at the Atmospheric Radiation Measurement Program Southern Great Plains site of the U.S. Department of Energy were determined by comparison with the liquid water path (LWP) obtained with Atmospheric Emitted Radiance Interferometer (AERI) and the liquid water content (LWC) obtained with the millimeter wavelength cloud radar and water vapor radiometer (MMCR–WVR) together. These comparisons were used to estimate the Raman liquid water cross-sectional value. The results indicate a bias consistent with an effective liquid water Raman cross-sectional value that is 28%–46% lower than published, which may be explained by the fact that the difference in the detectors' sensitivity has not been accounted for. The LWP of a thin altostratus cloud showed good qualitative agreement between lidar retrievals and AERI. However, the overall ensemble of comparisons of LWP showed considerable scatter, possibly because of the different fields of view of the instruments, the 350-m distance between the instruments, and the horizontal inhomogeneity of the clouds. The LWC profiles for a thick stratus cloud showed agreement between lidar retrievals and MMCR–WVR between the cloud base and 150 m above that where the optical depth was less than 3. Areas requiring further research in this technique are discussed.

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