scholarly journals Evaluation of a Statistical Model of Cloud Vertical Structure Using Combined CloudSat and CALIPSO Cloud Layer Profiles

2010 ◽  
Vol 23 (24) ◽  
pp. 6641-6653 ◽  
Author(s):  
William B. Rossow ◽  
Yuanchong Zhang
Keyword(s):  
Statistical Model ◽  
Vertical Structure ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Cloud Amount ◽  
Cloud Layer ◽  
Middle Level ◽  
Polar Regions ◽  
Total Cloud Amount ◽  
Low Level

Abstract A model of the three-dimensional distribution of clouds was developed from the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and the statistics of cloud vertical structure (CVS) from an analysis of radiosonde humidity profiles. The CVS model associates each cloud type, defined by cloud-top pressure of the topmost cloud layer and total column optical thickness, with a particular CVS. The advent of satellite cloud radar (CloudSat) and lidar [Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements (together C&C) of CVS allows for a quantitative evaluation of this statistical model. The zonal monthly-mean cloud layer distribution from the ISCCP CVS agrees with that from C&C to within 10% (when normalized to the same total cloud amount). The largest differences are an overestimate of middle-level cloudiness in winter polar regions, an overestimate of cloud-top pressures of the highest-level clouds, especially in the tropics, and an underestimate of low-level cloud amounts over southern midlatitude oceans. A more severe test of the hypothesized relationship is made by comparing CVS for individual satellite pixels. The agreement of CVS is good for isolated low-level clouds and reasonably good when the uppermost cloud layer is a high-level cloud; however, the agreement is not good when the uppermost cloud layer is a middle-level cloud, even when ISCCP correctly locates cloud top. An improved CVS model combining C&C and ISCCP may require classification at spatial scales larger than individual satellite pixels.

A Statistical Model of Cloud Vertical Structure Based on Reconciling Cloud Layer Amounts Inferred from Satellites and Radiosonde Humidity Profiles

2005 ◽  
Vol 18 (17) ◽  
pp. 3587-3605 ◽  
Author(s):  
William B. Rossow ◽  
Yuanchong Zhang ◽  
Junhong Wang
Keyword(s):  
Statistical Model ◽  
Vertical Structure ◽  
Atmospheric Temperature ◽  
Radiative Flux ◽  
Cloud Layer ◽  
Cloud Base ◽  
Polar Regions ◽  
Flux Divergence ◽  
Climate Scale ◽  
Humidity Profiles

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.

scholarly journals An Investigation of the Goshen County, Wyoming, Tornadic Supercell of 5 June 2009 Using EnKF Assimilation of Mobile Mesonet and Radar Observations Collected during VORTEX2. Part II: Mesocyclone-Scale Processes Affecting Tornado Formation, Maintenance, and Decay

2016 ◽  
Vol 144 (9) ◽  
pp. 3441-3463 ◽  
Author(s):  
James Marquis ◽  
Yvette Richardson ◽  
Paul Markowski ◽  
Joshua Wurman ◽  
Karen Kosiba ◽  
...  
Keyword(s):  
Life Cycle ◽  
Ensemble Kalman Filter ◽  
Vertical Structure ◽  
Spatial Scales ◽  
Near Surface ◽  
Low Level ◽  
Structure Changes ◽  
In Situ Observations ◽  
Negatively Buoyant

Storm-scale and mesocyclone-scale processes occurring contemporaneously with a tornado in the Goshen County, Wyoming, supercell observed on 5 June 2009 during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) are examined using ensemble analyses produced by assimilating mobile radar and in situ observations into a high-resolution convection-resolving model. This paper focuses on understanding the evolution of the vertical structure of the storm, the outflow buoyancy, and processes affecting the vertical vorticity and circulation within the mesocyclone that correspond to changes in observed tornado intensity. Tornadogenesis occurs when the low-level mesocyclone is least negatively buoyant relative to the environment, possesses its largest circulation, and is collocated with the largest azimuthally averaged convergence during the analysis period. The average buoyancy, circulation, and convergence within the near-surface mesocyclone (on spatial scales resolved by the model) all decrease as the tornado intensifies and matures. The tornado and its parent low-level mesocyclone both dissipate surrounded by a weakening rear-flank downdraft. The decreasing buoyancy of parcels within the low-level mesocyclone may partly be responsible for the weakening of the updraft surrounding the tornado and decoupling of the mid- and low-level circulation. Although the supply of horizontal vorticity generated in the forward flank of the storm increases throughout the life cycle of the tornado, it is presumably less easily tilted and stretched on the mesocyclone-scale during tornado maturity owing to the disruption of the low-level updraft/downdraft structure. Changes in radar-measured tornado intensity lag those of ensemble Kalman filter (EnKF) mesocyclone vorticity and circulation.

An Estimation of Turbulent Characteristics in the Low-Level Region of Intense Hurricanes Allen (1980) and Hugo (1989)

2011 ◽  
Vol 139 (5) ◽  
pp. 1447-1462 ◽  
Author(s):  
Jun A. Zhang ◽  
Frank D. Marks ◽  
Michael T. Montgomery ◽  
Sylvie Lorsolo
Keyword(s):  
Small Sample Size ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Eddy Diffusivity ◽  
Small Sample ◽  
Length Scale ◽  
Low Level ◽  
Turbulent Eddies ◽  
Turbulent Characteristics

This study analyzes the flight-level data collected by research aircraft that penetrated the eyewalls of category 5 Hurricane Hugo (1989) and category 4 Hurricane Allen (1980) between 1 km and the sea surface. Estimates of turbulent momentum flux, turbulent kinetic energy (TKE), and vertical eddy diffusivity are obtained before and during the eyewall penetrations. Spatial scales of turbulent eddies are determined through a spectral analysis. The turbulence parameters estimated for the eyewall penetration leg are found to be nearly an order of magnitude larger than those for the leg outside the eyewall at similar altitudes. In the low-level intense eyewall region, the horizontal length scale of the dominant turbulent eddies is found to be between 500 and 3000 m, and the corresponding vertical length scale is approximately 100 m. The results suggest also that it is unwise to include eyewall vorticity maxima (EVM) in the turbulence parameter estimation because the EVMs are likely to be quasi-two-dimensional vortex structures that are embedded within the three-dimensional turbulence on the inside edge of the eyewall. This study is a first attempt at estimating the characteristics of turbulent flow in the low-level troposphere of an intense eyewall using in situ aircraft observations. The authors believe that the results can offer useful guidance in numerical weather prediction efforts aimed at improving the forecast of hurricane intensity. Because of the small sample size analyzed in this study, further analyses of the turbulent characteristics in the high-wind region of hurricanes are imperative.

scholarly journals Cloud climatologies from the InfraRed Sounders AIRS and IASI: Strengths, Weaknesses and Applications

2017 ◽  
Author(s):  
Claudia J. Stubenrauch ◽  
Artem G. Feofilov ◽  
Sofia E. Protopapadaki ◽  
Raymond Armante
Keyword(s):  
Cloud Amount ◽  
Co2 Concentration ◽  
Boreal Summer ◽  
Cloud Base ◽  
Cloud Detection ◽  
Ancillary Data ◽  
Total Cloud Amount ◽  
Low Level ◽  
Cloud Properties ◽  
High Level

Abstract. The cloud retrieval scheme developed at the Laboratoire de Météorologie Dynamique (LMD) can now be easily adapted to any Infrared (IR) sounder: the CIRS (Clouds from IR Sounders) retrieval applies improved radiative transfer, as well as an original method accounting for atmospheric spectral transmissivity changes associated with CO2 concentration. The latter is essential when considering long-term time series of cloud properties. For the 13-year and 8-year global climatologies of cloud properties from observations of the Atmospheric IR Sounder (AIRS) and of the IR Atmospheric Interferometer (IASI), respectively, we used the latest ancillary data (atmospheric profiles, surface emissivities and atmospheric spectral transmissivities). The A-Train active instruments, lidar and radar of the CALIPSO and CloudSat missions, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height as well as to explore the vertical structure of different cloud types. CIRS cloud detection agreement with CALIPSO-CloudSat is about 84%–85% over ocean, 79%–82% over land and 70%–73% over ice / snow, depending on atmospheric ancillary data. Global cloud amount has been estimated to 67%–70%. CIRS cloud height coincides with the middle between the cloud top and the apparent cloud base (real base for optically thin clouds or height at which the cloud reaches opacity) independent of cloud emissivity, which is about 1 km below cloud top for low-level clouds and about 1.5 km to 2.5 km below cloud top for high-level clouds, slightly increasing because the apparent vertical cloud extent is slightly larger for large cloud emissivity. IR sounders are in particular advantageous for the retrieval of upper tropospheric cloud properties, with a reliable cirrus identification down to an IR optical depth of 0.1, day and night. Total cloud amount consists of about 40% high-level clouds and about 40% low-level clouds and 20% mid-level clouds, the latter two only detected when not hidden by upper clouds. Upper tropospheric clouds are most abundant in the tropics, where high opaque clouds make out 7.5%, thick cirrus 27.5% and thin cirrus about 21.5% of all clouds. The asymmetry in upper tropospheric cloud amount between Northern and Southern hemisphere with annual mean of 5% has a pronounced seasonal cycle with a maximum of 25% in boreal summer, which can be linked to the shift of the ITCZ peak latitude. Comparing tropical geographical change patterns of high opaque clouds with that of thin cirrus as a function of changing tropical mean surface temperature indicates that their response to climate change may be quite different, with potential consequences on the atmospheric circulation.

Daytime Cycle of Low-Level Clouds and the Tropical Convective Boundary Layer in Southwestern Amazonia

2005 ◽  
Vol 44 (10) ◽  
pp. 1607-1619 ◽  
Author(s):  
C. Strong ◽  
J. D. Fuentes ◽  
M. Garstang ◽  
A. K. Betts
Keyword(s):  
Boundary Layer ◽  
Mixed Layer ◽  
Solar Irradiance ◽  
Large Scale ◽  
Numerical Models ◽  
Cloud Amount ◽  
Layer Growth ◽  
Cloud Layer ◽  
Water Vapor Transport ◽  
Low Level

Abstract During the wet season in the southwestern Amazon region, daytime water transport out of the atmospheric mixed layer into the deeper atmosphere is shown to depend upon cloud amounts and types and synoptic-scale velocity fields. Interactions among clouds, convective conditions, and subcloud-layer properties were estimated for two dominant flow regimes observed during the 1999 Tropical Rainfall Measuring Mission component of the Brazilian Large-Scale Biosphere–Atmosphere (TRMM-LBA) field campaign. During daytime the cloud and subcloud layers were coupled by radiative, convective, and precipitation processes. The properties of cloud and subcloud layers varied according to the different convective influences of easterly versus westerly lower-tropospheric flows. The most pronounced flow-regime effects on composite cloud cycles occurred under persistent lower-tropospheric flows, which produced strong convective cloud growth with a near absence of low-level stratiform clouds, minimal cumulative attenuation of incoming solar irradiance (∼25%), rapid daytime mixed-layer growth (>100 m h−1), and boundary layer drying (0.22 g kg−1 h−1), high convective velocities (>1.5 m s−1), high surface buoyancy flux (>200 W m−2), and high latent heat flux (600 W m−2) into cloud layer. In contrast, persistent westerly flows were less convective, showing a strong morning presence of low-level stratiform genera (>0.9 cloud amount), greater cumulative attenuation of incoming solar irradiance (∼47%), slower mixed-layer growth (<50 m h−1) with a slight tendency for mixed-layer moistening, and a delayed peak in the low-level cumuliform cloud cycle (2000 versus 1700 UTC). The results reported in this article indicate that numerical models need to account for cloud amounts and types when estimating water vapor transport to the cloud layer.

Urban network of China from the perspective of population mobility: Three-dimensional co-occurrence of nodes and links

2021 ◽  
pp. 0308518X2199781
Author(s):  
Xinyue Luo ◽  
Mingxing Chen
Keyword(s):  
Three Dimensional ◽  
Geographical Distance ◽  
Two Dimensional ◽  
Population Mobility ◽  
Urban Network ◽  
Urban Networks ◽  
Low Level ◽  
Medium Level ◽  
High Level ◽  
External Connections

The nodes and links in urban networks are usually presented in a two-dimensional(2D) view. The co-occurrence of nodes and links can also be realized from a three-dimensional(3D) perspective to make the characteristics of urban network more intuitively revealed. Our result shows that the external connections of high-level cities are mainly affected by the level of cities(nodes) and less affected by geographical distance, while medium-level cities are affected by the interaction of the level of cities(nodes) and geographical distance. The external connections of low-level cities are greatly restricted by geographical distance.

scholarly journals Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

2020 ◽  
Vol 12 (8) ◽  
pp. 1319
Author(s):  
Xiaofan Sun ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Liangjiang Zhou ◽  
Shuai Jiang
Keyword(s):  
Standard Deviation ◽  
Vertical Structure ◽  
Incidence Angle ◽  
Three Dimensional ◽  
Solution Space ◽  
Model Complexity ◽  
Two Dimensional ◽  
Dimensional Parameter ◽  
Model Inversion ◽  
Forest Height

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability.

Morphometry of the sacrum and its implication on trans-sacral corridors using a computed tomography data-based three-dimensional statistical model

2017 ◽  
Vol 17 (8) ◽  
pp. 1141-1147 ◽  
Author(s):  
Daniel Wagner ◽  
Lukas Kamer ◽  
Takeshi Sawaguchi ◽  
Robert Geoff Richards ◽  
Hansrudi Noser ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Statistical Model ◽  
Three Dimensional ◽  
Tomography Data

scholarly journals Seasonal cycle of cloud cover analyzed using Meteosat images

1998 ◽  
Vol 16 (3) ◽  
pp. 331-341 ◽  
Author(s):  
J. Massons ◽  
D. Domingo ◽  
J. Lorente
Keyword(s):  
Spatial Resolution ◽  
Seasonal Cycle ◽  
Cloud Cover ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Total Cloud Amount ◽  
Cloud Parameters ◽  
Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

scholarly journals Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations

2012 ◽  
Vol 12 (4) ◽  
pp. 1785-1810 ◽  
Author(s):  
Y. Qian ◽  
C. N. Long ◽  
H. Wang ◽  
J. M. Comstock ◽  
S. A. McFarlane ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Probability Distribution ◽  
Vertical Structure ◽  
Cloud Amount ◽  
Cloud Fraction ◽  
Department Of Energy ◽  
The Arctic ◽  
Radiative Fluxes ◽  
The Tropics ◽  
Ipcc Ar4

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM long-term ground-based measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Comparisons are performed for three climate regimes as represented by the Department of Energy Atmospheric Radiation Measurement (ARM) sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). Our intercomparisons of three independent measurements of CF or sky-cover reveal that the relative differences are usually less than 10% (5%) for multi-year monthly (annual) mean values, while daily differences are quite significant. The total sky imager (TSI) produces smaller total cloud fraction (TCF) compared to a radar/lidar dataset for highly cloudy days (CF > 0.8), but produces a larger TCF value than the radar/lidar for less cloudy conditions (CF < 0.3). The compensating errors in lower and higher CF days result in small biases of TCF between the vertically pointing radar/lidar dataset and the hemispheric TSI measurements as multi-year data is averaged. The unique radar/lidar CF measurements enable us to evaluate seasonal variation of cloud vertical structures in the GCMs. Both inter-model deviation and model bias against observation are investigated in this study. Another unique aspect of this study is that we use simultaneous measurements of CF and surface radiative fluxes to diagnose potential discrepancies among the GCMs in representing other cloud optical properties than TCF. The results show that the model-observation and inter-model deviations have similar magnitudes for the TCF and the normalized cloud effect, and these deviations are larger than those in surface downward solar radiation and cloud transmissivity. This implies that other dimensions of cloud in addition to cloud amount, such as cloud optical thickness and/or cloud height, have a similar magnitude of disparity as TCF within the GCMs, and suggests that the better agreement among GCMs in solar radiative fluxes could be a result of compensating effects from errors in cloud vertical structure, overlap assumption, cloud optical depth and/or cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation. The GCM performance in simulating the probability distribution, transmissivity and vertical profiles of cloud are comprehensively evaluated over the three ARM sites. The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF at SGP and cloud transmissivity is less susceptible to the change of TCF than observed. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high-level CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating discrepancies in the representation of ice cloud associated with convection in the models. While the GCMs generally capture the maximum CF in the boundary layer and vertical variability, the inter-model deviation is largest near the surface over the Arctic.

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