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William Perrie
and
Liangming Wang

Abstract

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William Perrie
and
Liangming Wang

Abstract

The authors present a simple model for the dynamics that couple the atmospheric boundary layer and wind-generated waves. The model is empirically motivated by parameterizations for the sea state-dependent drag coefficient and sea surface roughness derived by Smith et al. from HEXOS measurements. Estimates are made for the effect the coupling dynamics has on predicted sea state parameters such as spectral wave energy and the air–sea flux of momentum. Results are verified with observations collected during the CAL/VAL experiment of Dobson and Vachon. The authors demonstrate that inclusion of the coupling dynamics systematically improves wave modeling. The effect of the coupling dynamics is particularly important for young waves in the presence of high wind speeds. A tendency to improve estimates of maximum wave heights is achieved.

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Junhong Wang
and
William B. Rossow

Abstract

A method is described to use rawinsonde data to estimate cloud vertical structure, including cloud-top and cloud-base heights, cloud-layer thickness, and the characteristics of multilayered clouds. Cloud-layer base and top locations are identified based on three criteria: maximum relative humidity in a cloud of at least 87%, minimum relative humidity of at least 84%, and relative humidity jumps exceeding 3% at cloud-layer top and base, where relative humidity is with respect to liquid water at temperatures greater than or equal to 0°C and with respect to ice at temperatures less than 0°C. The analysis method is tested at 30 ocean sites by comparing with cloud properties derived from other independent data sources. Comparison of layer-cloud frequencies of occurrence with surface observations shows that rawinsonde observations (RAOBS) usually detect the same number of cloud layers for low and middle clouds as the surface observers, but disagree more for high-level clouds. There is good agreement between the seasonal variations of RAOBS-determined top pressure of the highest cloud and that from the International Satellite Cloud Climate Project (ISCCP) data. RAOBS-determined top pressures of low and middle clouds agree better with ISCCP, but RAOBS often fail to detect very high and thin clouds. The frequency of multilayered clouds is qualitatively consistent with that estimated from surface observations. In cloudy soundings at these ocean sites, multilayered clouds occur 56% of the time and are predominately two layered. Multilayered clouds are most frequent (≈70%) in the Tropics (10°S–10°N) and least frequent at subtropical eastern Pacific stations. The frequency of multilayered clouds is higher in summer than in winter at low-latitude stations (30°S–30°N), but the opposite variation appears at the two subtropical stations. The frequency distributions of cloud top, cloud base, and cloud-layer thickness and cloud occurrence as a function of height are also presented. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer.

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Guohui Wang
and
William K. Dewar

Abstract

A quasigeostrophic point vortex numerical model is used to explore interactions of eddies and seamounts. The ultimate objective of this study is to assess the role of meddy–seamount interaction as an input to Mediterranean salt tongue maintenance. Secondary objectives are to clarify the dynamics of meddy–seamount interaction. The results suggest that meddies survive seamount collisions with 60%–70% of their initial cores remaining intact as coherent vortices. Given observed formation rates, it appears meddies, in their interactions with seamounts, inject between one-quarter and one-half of the salt anomaly necessary to sustain the Mediterranean salt tongue. Other considerations suggest the anomalous mass flux by meddies is comparable to that due to the mean flow. In summary, meddies are important to the maintenance of the salt tongue, although other mechanisms are needed. Coherent vortex transport, of which meddies are one example, is a mesoscale process not well described by the downgradient mixing algorithms normally employed in general circulation models. More sophisticated mesoscale models are thus suggested by this study. In particular, survival by meddies of collisions with seamounts emerges as a potentially important limiting effect on the Mediterranean salt tongue. This effect has climatically significant implications for ocean simulations.

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Hailong Wang
,
William C. Skamarock
, and
Graham Feingold

Abstract

In the Advanced Research Weather Research and Forecasting Model (ARW), versions 3.0 and earlier, advection of scalars was performed using the Runge–Kutta time-integration scheme with an option of using a positive-definite (PD) flux limiter. Large-eddy simulations of aerosol–cloud interactions using the ARW model are performed to evaluate the advection schemes. The basic Runge–Kutta scheme alone produces spurious oscillations and negative values in scalar mixing ratios because of numerical dispersion errors. The PD flux limiter assures positive definiteness but retains the oscillations with an amplification of local maxima by up to 20% in the tests. These numerical dispersion errors contaminate active scalars directly through the advection process and indirectly through physical and dynamical feedbacks, leading to a misrepresentation of cloud physical and dynamical processes. A monotonic flux limiter is introduced to correct the generally accurate but dispersive solutions given by high-order Runge–Kutta scheme. The monotonic limiter effectively minimizes the dispersion errors with little significant enhancement of numerical diffusion errors. The improvement in scalar advection using the monotonic limiter is discussed in the context of how the different advection schemes impact the quantification of aerosol–cloud interactions. The PD limiter results in 20% (10%) fewer cloud droplets and 22% (5%) smaller cloud albedo than the monotonic limiter under clean (polluted) conditions. Underprediction of cloud droplet number concentration by the PD limiter tends to trigger the early formation of precipitation in the clean case, leading to a potentially large impact on cloud albedo change.

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William B. Rossow
,
Yuanchong Zhang
, and
Junhong Wang

Abstract

To diagnose how cloud processes feed back on weather- and climate-scale variations of the atmosphere requires determining the changes that clouds produce in the atmospheric diabatic heating by radiation and precipitation at the same scales of variation. In particular, not only the magnitude of these changes must be quantified but also their correlation with atmospheric temperature variations; hence, the space–time resolution of the cloud perturbations must be sufficient to account for the majority of these variations. Although extensive new global cloud and radiative flux datasets have recently become available, the vertical profiles of clouds and consequent radiative flux divergence have not been systematically measured covering weather-scale variations from about 100 km, 3 h up to climate-scale variations of 10 000 km, decadal inclusive. By combining the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and an analysis of radiosonde humidity profiles, a statistical model has been developed that associates each cloud type, recognizable from satellite measurements, with a particular cloud vertical structure. Application of this model to the ISCCP cloud layer amounts produces estimates of low-level cloud amounts and average cloud-base pressures that are quantitatively closer to observations based on surface weather observations, capturing the variations with latitude and season and land and ocean (results are less good in the polar regions). The main advantage of this statistical model is that the correlations of cloud vertical structure with meteorology are qualitatively similar to “classical” information relating cloud properties to weather. These results can be evaluated and improved with the advent of satellites that can directly probe cloud vertical structures over the globe, providing statistics with changing meteorological conditions.

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Junhong Wang
,
William B. Rossow
, and
Yuanchong Zhang

Abstract

A global cloud vertical structure (CVS) climatic dataset is created by applying an analysis method to a 20-yr collection of twice-daily rawinsonde humidity profiles to estimate the height of cloud layers. The CVS dataset gives the vertical distribution of cloud layers for single and multilayered clouds, as well as the top and base heights and layer thicknesses of each layer, together with the original rawinsonde profiles of temperature, humidity, and winds. The average values are cloud-top height = 4.0 km above mean sea level (MSL), cloud-base height = 2.4 km MSL, cloud-layer thickness = 1.6 km, and separation distance between consecutive layers = 2.2 km. Multilayered clouds occur 42% of the time and are predominately two-layered. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer (below 2-km height MSL). Clouds over the ocean occur more frequently at lower levels and are more often formed in multiple layers than over land. Latitudinal variations of CVS also show maxima and minima that correspond to the locations of the intertropical convergence zone, the summer monsoons, the subtropical subsidence zones, and the midlatitude storm zones. Multilayered clouds exist most frequently in the Tropics and least frequently in the subtropics; there are more multilayered clouds in summer than in winter. Cloud layers are thicker in winter than in summer at mid- and high latitudes, but are thinner in winter in Southeast Asia.

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William B. Bennett
,
Jingfeng Wang
, and
Rafael L. Bras

Abstract

This study investigates the use of a previously published algorithm for estimating ground heat flux (GHF) at the global scale. The method is based on an analytical solution of the diffusion equation for heat transfer in a soil layer and has been shown to be effective at the point scale. The algorithm has several advantageous properties: 1) it only needs a single-level input of surface (skin) temperature, 2) the time-mean GHF can be derived directly from time-mean skin temperature, 3) it has reduced sensitivity to the variability in soil thermal properties and moisture, 4) it does not requires snow depth, and 5) it is computationally effective. A global map of the necessary thermal inertia parameter is derived using reanalysis data as a function of soil type. These parameter estimates are comparable to values obtained from in situ observations. The new global GHF estimates are generally consistent with the reanalysis GHF output simulated using two-layer soil hydrology models. The authors argue that the new algorithm is more robust and trustworthy in regions where they differ. The proposed algorithm offers potential benefits for direct assimilation of observations of surface temperature as well as GHF into the reanalysis models at various time scales.

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Junhong Wang
,
William B. Rossow
,
Taneil Uttal
, and
Margaret Rozendaal

Abstract

The macroscale cloud vertical structure (CVS), including cloud-base and -top heights and layer thickness, and characteristics of multilayered clouds, is studied at Porto Santo Island during the Atlantic Stratocumulus Transition Experiment (ASTEX) by using rawinsonde, radar, ceilometer, and satellite data. The comparisons of CVS parameters obtained from four different approaches show that 1) by using the method developed by Wang and Rossow rawinsonde observations (raob’s) can sample all low clouds and determine their boundaries accurately, but oversample low clouds by about 10%, mistaking clear moist layers for clouds; 2) cloud-base heights less than 200 m in the radar data are ambiguous, but can be replaced by the values measured by ceilometer; and 3) the practical limit on the accuracy of marine boundary layer cloud-top heights retrieved from satellites appears to be about 150–300 m mainly due to errors in specifying the atmospheric temperature and humidity in the inversion layer above the cloud. The vertical distribution of clouds at Porto Santo during ASTEX is dominated by low clouds below 3 km, a cloud-free layer between 3 and 4 km, and ∼20% high clouds with a peak occurrence around 7–8 km. Low clouds have mean base and top heights of 1.0 km and 1.4 km, respectively, and occur as single layers 90% of the time. For double-layered low clouds, the tops of the uppermost layers and the bases of the lowermost layers have similar distributions as those of single-layered clouds. The temporal variations of low clouds during ASTEX are apparently dominated by advecting mesoscale (20–200 km) horizontal variations. Coherent time variations are predominately synoptic (timescale 4.5–6.8 days) and diurnal variability. On the diurnal timescale, all cloud properties show maxima in the early morning (around 0530 LST) decreasing to minima in the late afternoon. Diurnal variations appear to be altered when high clouds are present above low clouds. The general characteristics of CVS in three ASTEX and the First ISCCP Regional Experiment (FIRE87) regions derived from a 20-yr rawinsonde dataset are also presented. The results suggest that CVS characteristics obtained from data collected at Porto Santo during ASTEX (June 1992) are not representative of other marine stratiform cloud regions.

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Guang Wen
,
Alain Protat
,
Peter T. May
,
Xuezhi Wang
, and
William Moran

Abstract

Hydrometeor classification methods using polarimetric radar variables rely on probability density functions (PDFs) or membership functions derived empirically or by using electromagnetic scattering calculations. This paper describes an objective approach based on cluster analysis to deriving the PDFs. An iterative procedure with K-means clustering and expectation–maximization clustering based on Gaussian mixture models is developed to generate a series of prototypes for each hydrometeor type from several radar scans. The prototypes are then grouped together to produce a PDF for each hydrometeor type, which is modeled as a Gaussian mixture. The cluster-based method is applied to polarimetric radar data collected with the CP-2 S-band radar near Brisbane, Queensland, Australia. The results are illustrated and compared with theoretical classification boundaries in the literature. Some notable differences are found. Automated hydrometeor classification algorithms can be built using the PDFs of polarimetric variables associated with each hydrometeor type presented in this paper.

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