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Roger Marchand
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Roger Marchand
and
Thomas Ackerman

Abstract

Accurate cloud-resolving model simulations of cloud cover and cloud water content for boundary layer clouds are difficult to achieve without vertical grid spacing well below 100 m, especially for inversion-topped stratocumulus. The need for fine vertical grid spacing presents a significant impediment to global or large regional simulations using cloud-resolving models, including the Multiscale Modeling Framework (MMF), in which a two-dimensional or small three-dimensional cloud-resolving model is embedded into each grid cell of a global climate model in place of more traditional cloud parameterizations. One potential solution to this problem is to use a model with an adaptive vertical grid (i.e., a model that is able to add vertical layers where and when needed) rather than trying to use a fixed grid with fine vertical spacing throughout the boundary layer. This article examines simulations with an adaptive vertical grid for three well-studied stratocumulus cases based on observations from the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) experiment, the Atlantic Stratocumulus Transition Experiment (ASTEX), and the Atlantic Trade Cumulus Experiment (ATEX). For each case, three criteria are examined for determining where to add or remove vertical layers. One criterion is based on the domain-averaged potential temperature profile; the other two are based on the ratio of the estimated subgrid-scale to total water flux and turbulent kinetic energy. The results of the adaptive vertical grid simulations are encouraging in that these simulations are able to produce results similar to simulations using fine vertical grid spacing throughout the boundary layer, while using many fewer vertical layers.

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Stuart Evans
,
Roger Marchand
, and
Thomas Ackerman

Abstract

An atmospheric classification for northwestern Australia is used to define periods of monsoon activity and investigate the interannual and intraseasonal variability of the Australian monsoon, as well as long-term precipitation trends at Darwin. The classification creates a time series of atmospheric states, which two correspond to the active monsoon and the monsoon break. Occurrence of these states is used to define onset, retreat, seasonal intensity, and individual active periods within seasons. The authors demonstrate the quality of their method by showing it consistently identifies extended periods of precipitation as part of the monsoon season and recreates well-known relationships between Australian monsoon onset, intensity, and ENSO. The authors also find that onset and seasonal intensity are significantly correlated with ENSO as early as July.

Previous studies have investigated the role of the Madden–Julian oscillation (MJO) during the monsoon by studying the frequency and duration of active periods, but these studies disagree on whether the MJO creates a characteristic period or duration. The authors use their metrics of monsoon activity and the Wheeler–Hendon MJO index to examine the timing of active periods relative to the phase of the MJO. It is shown that active periods preferentially begin during MJO phases 3 and 4, as the convective anomaly approaches Darwin, and end during phases 7 and 8, as the anomaly departs Darwin. Finally, the causes of the multidecadal positive precipitation trend at Darwin over the last few decades are investigated. It is found that an increase in the number of days classified as active, rather than changes in the daily rainfall rate during active monsoon periods, is responsible.

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Mikhail Ovtchinnikov
,
Thomas Ackerman
,
Roger Marchand
, and
Marat Khairoutdinov

Abstract

In a recently developed approach to climate modeling, called the multiscale modeling framework (MMF), a two-dimensional cloud-resolving model (CRM) is embedded into each grid column of the Community Atmospheric Model (CAM), replacing traditional cloud and radiation parameterizations. This study presents an evaluation of the MMF through a comparison of its output with the output from the CAM and with data from two observational sites operated by the Atmospheric Radiation Measurement Program, one at the Southern Great Plains (SGP) in Oklahoma and one at the island of Nauru in the tropical western Pacific (TWP) region.

Two sets of one-year-long simulations are considered: one using climatological sea surface temperatures (SSTs) and another using 1999 SST. Each set includes a run with the MMF as well as a CAM run with traditional or standard cloud and radiation treatments. Time series of cloud fraction, precipitation intensity, and downwelling solar radiation flux at the surface are analyzed. For the TWP site, the distributions of these variables from the MMF run are shown to be more consistent with observation than those from the CAM run. This change is attributed to the improved representation of convective clouds in the MMF compared to the conventional climate model. For the SGP, the MMF shows little to no improvement in predicting the same quantities. Possible causes of this lack of improvement are discussed.

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Roger Marchand
,
Nathaniel Beagley
, and
Thomas P. Ackerman

Abstract

Vertical profiles of hydrometeor occurrence from the multiscale modeling framework (MMF) climate model are compared with profiles observed by a vertically pointing millimeter wavelength cloud radar (located in the U.S. southern Great Plains) as a function of the large-scale atmospheric state. The atmospheric state is determined by classifying (or clustering) the large-scale (synoptic) fields produced by the MMF and a numerical weather prediction model using a neural network approach. The comparison shows that for cold-frontal and post-cold-frontal conditions the MMF produces profiles of hydrometeor occurrence that compare favorably with radar observations, while for warm-frontal conditions the model tends to produce hydrometeor fractions that are too large with too much cloud (nonprecipitating hydrometeors) above 7 km and too much precipitating hydrometeor coverage below 7 km. It is also found that the MMF has difficulty capturing the formation of low clouds and that, for all atmospheric states that occur during June, July, and August, the MMF produces too much high and thin cloud, especially above 10 km.

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Roger Marchand
,
Gerald G. Mace
,
Thomas Ackerman
, and
Graeme Stephens

Abstract

In late April 2006, NASA launched Cloudsat, an earth-observing satellite that uses a near-nadir-pointing millimeter-wavelength radar to probe the vertical structure of clouds and precipitation. The first step in using Cloudsat measurements is to distinguish clouds and other hydrometeors from radar noise. In this article the operational Cloudsat hydrometeor detection algorithm is described, difficulties due to surface clutter are discussed, and several examples from the early mission are shown. A preliminary comparison of the Cloudsat hydrometeor detection algorithm with lidar-based results from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also provided.

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Roger Marchand
,
Nathaniel Beagley
,
Sandra E. Thompson
,
Thomas P. Ackerman
, and
David M. Schultz

Abstract

A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique.

A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

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Xiquan Dong
,
Gerald G. Mace
,
Patrick Minnis
,
William L. Smith Jr.,
,
Michael Poellot
,
Roger T. Marchand
, and
Anita D. Rapp

Abstract

Low-level stratus cloud microphysical properties derived from surface and Geostationary Operational Environmental Satellite (GOES) data during the March 2000 cloud intensive observational period (IOP) at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site are compared with aircraft in situ measurements. For the surface retrievals, the cloud droplet effective radius and optical depth are retrieved from a δ2-stream radiative transfer model with the input of ground-based measurements, and the cloud liquid water path (LWP) is retrieved from ground-based microwave-radiometer-measured brightness temperature. The satellite results, retrieved from GOES visible, solar-infrared, and infrared radiances, are averaged in a 0.5° × 0.5° box centered on the ARM SGP site. The forward scattering spectrometer probe (FSSP) on the University of North Dakota Citation aircraft provided in situ measurements of the cloud microphysical properties. During the IOP, four low-level stratus cases were intensively observed by the ground- and satellite-based remote sensors and aircraft in situ instruments resulting in a total of 10 h of simultaneous data from the three platforms. In spite of the large differences in temporal and spatial resolution between surface, GOES, and aircraft, the surface retrievals have excellent agreement with the aircraft data overall for the entire 10-h period, and the GOES results agree reasonably well with the surface and aircraft data and have similar trends and magnitudes except for the GOES-derived effective radii, which are typically larger than the surface- and aircraft-derived values. The means and standard deviations of the differences between the surface and aircraft effective radius, LWP, and optical depth are −4% ± 20.1%, −1% ± 31.2%, and 8% ± 29.3%, respectively; while their correlation coefficients are 0.78, 0.92, and 0.89, respectively, during the 10-h period. The differences and correlations between the GOES-8 and aircraft results are of a similar magnitude, except for the droplet sizes. The averaged GOES-derived effective radius is 23% or 1.8 μm greater than the corresponding aircraft values, resulting in a much smaller correlation coefficient of 0.18. Additional surface–satellite datasets were analyzed for time periods when the aircraft was unavailable. When these additional results are combined with the retrievals from the four in situ cases, the means and standard deviations of the differences between the satellite-derived cloud droplet effective radius, LWP, and optical depth and their surface-based counterparts are 16% ± 31.2%, 4% ± 31.6%, and −6% ± 39.9%, respectively. The corresponding correlation coefficients are 0.24, 0.88, and 0.73. The frequency distributions of the two datasets are very similar indicating that the satellite retrieval method should be able to produce reliable statistics of boundary layer cloud properties for use in climate and cloud process models.

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Roger Marchand
,
Gerald G. Mace
,
A. Gannet Hallar
,
Ian B. McCubbin
,
Sergey Y. Matrosov
, and
Matthew D. Shupe

Abstract

Nonspherical atmospheric ice particles can enhance radar backscattering and attenuation above that expected from spheres of the same mass. An analysis of scanning 95-GHz radar data collected during the Storm Peak Laboratory Cloud Property Validation Experiment (StormVEx) shows that at a least a small amount of enhanced backscattering was present in most radar scans, with a median enhancement of 2.4 dB at zenith. This enhancement will cause an error (bias) in ice water content (IWC) retrievals that neglect particle orientation, with a value of 2.4 dB being roughly equivalent to a relative error in IWC of 43%. Of the radar scans examined, 25% had a zenith-enhanced backscattering exceeding 3.5 dB (equivalent to a relative error in IWC in excess of 67%) and 10% of the scans had a zenith-enhanced backscattering exceeding 6.4 dB (equivalent to a relative error in IWC in excess of 150%). Cloud particle images indicate that large enhancement typically occurred when planar crystals (e.g., plates and dendrites) were present, with the largest enhancement occurring when large planar crystals were falling out of a supercooled liquid-water layer. More modest enhancement was sometimes due to planar crystals, but it was also sometimes likely a result of horizontally oriented nonspherical irregularly shaped particles. The analysis also shows there is a strong correlation (about −0.79) between the change in slant 45° depolarization ratio with radar scan elevation angle and the magnitude of the zenith-enhanced backscattering, suggesting that measurements of the slant depolarization ratio can be used to improve radar-based cloud microphysical property retrievals.

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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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