Search Results

You are looking at 1 - 10 of 15 items for

  • Author or Editor: Roger Marchand x
  • Refine by Access: All Content x
Clear All Modify Search
Roger Marchand
Full access
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.

Full access
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.

Full access
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.

Full access
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.

Full access
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.

Full access
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.

Full access
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.

Full access
Sergey Y. Matrosov, Gerald G. Mace, Roger Marchand, Matthew D. Shupe, Anna G. Hallar, and Ian B. McCubbin

Abstract

Scanning polarimetric W-band radar data were evaluated for the purpose of identifying predominant ice hydrometeor habits. Radar and accompanying cloud microphysical measurements were conducted during the Storm Peak Laboratory Cloud Property Validation Experiment held in Steamboat Springs, Colorado, during the winter season of 2010/11. The observed ice hydrometeor habits ranged from pristine and rimed dendrites/stellars to aggregates, irregulars, graupel, columns, plates, and particle mixtures. The slant 45° linear depolarization ratio (SLDR) trends as a function of the radar elevation angle are indicative of the predominant hydrometeor habit/shape. For planar particles, SLDR values increase from values close to the radar polarization cross coupling of about −21.8 dB at zenith viewing to maximum values at slant viewing. These maximum values depend on predominant aspect ratio and bulk density of hydrometeors and also show some sensitivity to particle characteristic size. The highest observed SLDRs were around −8 dB for pristine dendrites. Unlike planar-type hydrometeors, columnar-type particles did not exhibit pronounced depolarization trends as a function of viewing direction. A difference in measured SLDR values between zenith and slant viewing can be used to infer predominant aspect ratios of planar hydrometeors if an assumption about their bulk density is made. For columnar hydrometeors, SLDR offsets from the cross-coupling value are indicative of aspect ratios. Experimental data were analyzed for a number of events with prevalence of planar-type hydrometeors and also for observations when columnar particles were the dominant species. A relatively simple spheroidal model and accompanying T-matrix calculations were able to approximate most radar depolarization changes with viewing angle observed for different hydrometeor types.

Full access
Yi Huang, Steven T. Siems, Michael J. Manton, Daniel Rosenfeld, Roger Marchand, Greg M. McFarquhar, and Alain Protat

Abstract

This study employs four years of spatiotemporally collocated A-Train satellite observations to investigate cloud and precipitation characteristics in relation to the underlying properties of the Southern Ocean (SO). Results show that liquid-phase cloud properties strongly correlate with the sea surface temperature (SST). In summer, ubiquitous supercooled liquid water (SLW) is observed over SSTs less than about 4°C. Cloud-top temperature (CTT) and effective radius of liquid-phase clouds generally decrease for colder SSTs, whereas the opposite trend is observed for cloud-top height, cloud optical thickness, and liquid water path. The deduced cloud depth is larger over the colder oceans. Notable differences are observed between “precipitating” and “nonprecipitating” clouds and between different ocean sectors. Using a novel joint SST–CTT histogram, two distinct liquid-phase cloud types are identified, where the retrieved particle size appears to increase with decreasing CTT over warmer water (SSTs >~7°C), while the opposite is true over colder water. A comparison with the Northern Hemisphere (NH) storm-track regions suggests that the ubiquitous SLW with markedly smaller droplet size is a unique feature for the cold SO (occurring where SSTs <~4°C), while the presence of this cloud type is much less frequent over the NH counterparts, where the SSTs are rarely colder than about 4°C at any time of the year. This study also suggests that precipitation, which has a profound influence on cloud properties, remains poorly observed over the SO with the current spaceborne sensors. Large uncertainties in precipitation properties are associated with the ubiquitous boundary layer clouds within the lowest kilometer of the atmosphere.

Full access