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R. M. Welch, S. K. Sengupta, and K. S. Kuo

Abstract

Statistical measures of the spatial distributions of gray levels (cloud reflectivities) are determined for LANDSAT Multispectral Scanner digital data. Textural properties for twelve stratocumulus cloud fields, seven cumulus fields, and two cirrus fields are examined using the Spatial Gray Level Co-Occurrence Matrix method. The co-occurrence statistics are computed for pixel separations ranging from 57 m to 29 km and at angles of 0°, 45°, 90° and 135°. Nine different textual measures are used to define the cloud field spatial relationships. However, the measures of contrast and correlation appear to be most useful in distinguishing cloud structure.

Cloud field macrotexture describes general cloud field characteristics at distances greater than the size of typical cloud elements. It is determined from the spatial asymptotic values of the texture measures. The slope of the texture curves at small distances provides a measure of the microtexture of individual cloud cells. Cloud fields composed primarily of small cells have very steep slopes and reach their asymptotic values at short distances from the origin. As the cells composing the cloud field grow larger, the slope becomes more gradual and the asymptotic distance increases accordingly. Low asymptotic values of correlation show that stratocumulus cloud fields have no large scale organized structure.

Besides the ability to distinguish cloud field structure, texture appears to be a potentially valuable tool in cloud classification. Stratocumulus clouds are characterized by low values of angular second moment and large values of entropy. Cirrus clouds appear to have extremely low values of contrast, low values of entropy, and very large values of correlation.

Finally, we propose that sampled high spatial resolution satellite data be used in conjunction with coarser resolution operational satellite data to detect and identify cloud field structure and directionality and to locate regions of subresolution scale cloud contamination.

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K. S. Kuo, R. M. Welch, and S. K. Sengupta

Abstract

Twelve cirrus scenes are analyzed to determine textural and structural features using LANDSAT imagery. The main structural characteristics are: 1) cirrus cloud size distributions obey a power law, with larger cloud cells (D ⩾ 1.5 km) having smaller slopes than smaller cloud elements; 2) convective-type cirrus are fractal in nature with fractal dimensions of ≈ 1.4, while stratiform cirrus clouds show bifractal behavior, with larger clouds having smaller fractal dimensions (≈1.3); 3) stratiform cirrus cloud cells have significantly larger horizontal aspect ratio than do smaller cells; and 4) structural results are not sensitive to threshold selection.

The main textural characteristics are: 1) convective cirrus clouds have high contrast measures and a rapid decrease of correlation at short distances, while stratiform cirrus clouds have low contrast measures and more gradual slopes; 2) asymptotic values are good descriptors of general characteristics (macrotexture) of cloud fields, while the slopes of textural measure curves at small distances reveal information about cloud field microtexture; 3) contrast and correlation appear to be the best discriminators of cloud field structure, and their directional measures show preferred cloud field orientation; and 4) correlation measures are sensitive to threshold selection for cirrostratus cases.

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T. Vukicevic, M. Sengupta, A. S. Jones, and T. Vonder Haar

Abstract

This study addresses the problem of four-dimensional (4D) estimation of a cloudy atmosphere on cloud-resolving scales using satellite remote sensing measurements. The motivation is to develop a methodology for accurate estimation of cloud properties and the associated atmospheric environment on small spatial scales but over large regions to aid in better understanding of the clouds and their role in the atmospheric system. The problem is initially approached by the study of the assimilation of the Geostationary Operational Environmental Satellite (GOES) imager observations into a cloud-resolving model with explicit bulk cloud microphysical parameterization. A new 4D variational data assimilation (4DVAR) research system with the cloud-resolving capability is applied to a case of a multilayered cloud evolution without convection. In the experiments the information content of the IR window channels is addressed as well as the sensitivity of estimation to lateral boundary condition errors, model first guess, decorrelation length in the background statistical error model, and the use of a generic linear model error. The assimilation results are compared with independent observations from the Atmospheric Radiation Measurement (ARM) central facility archive.

The modeled 3D spatial distribution and short-term evolution of the ice cloud mass is significantly improved by the assimilation of IR window channels when the model already contains conditions for the ice cloud formation. The assimilated ice cloud in this case is in good agreement with the independent cloud radar measurements. The simulation of liquid clouds below thick ice clouds is not influenced by the IR window observations. The assimilation results clearly demonstrate that increasing the observational constraint from individual to combined channel measurements and from less to more frequent observation times systematically improves the assimilation results. The experiments with the model error indicate that the current specification of this error in the form of a generic linear forcing, which was adopted from other data assimilation studies, is not suitable for the cloud-resolving data assimilation. Instead, a parameter estimation approach may need to be explored in the future. The experiments with varying decorrelation lengths suggest the need to use the model horizontal grid spacing that is several times smaller than the GOES imager native resolution to achieve equivalent spatial variability in the assimilation.

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R. M. Welch, S. K. Sengupta, A. K. Goroch, P. Rabindra, N. Rangaraj, and M. S. Navar

Abstract

Six Advanced Very High-Resolution Radiometer local area coverage (AVHPR LAC) arctic scenes are classified into ten classes. These include water, solid sea ice, broken sea ice, snow-covered mountains, snow-free land, and five cloud types. Three different classifiers are examined: 1) the traditional stepwise discriminant analysis (SDA) method; 2) the feed-forward back-propagation (FFBP) neural network; and 3) the probabilistic neural network (PNN).

More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6%, 87.6%, and 87.0% for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1%. Thin cloud/fog over ice is the class with the lowest accuracy (≈75%) for all of the classifiers. The snow-covered mountains, the cirrus over ice, and the land classes are classified with the highest accuracy (⩾90%) by all of the classifiers.

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S. K. Sengupta, R. M. Welch, M. S. Navar, T. A. Berendes, and D. W. Chen

Abstract

Detailed observations of cumulus cloud scales and processes are an essential ingredient in models that deal with (i) high spatial resolution cumulus ensembles; and (ii) parameterization of cloud radiative processes. The present investigation focuses on three aspects of the morphology of cumulus clouds: 1) the inhomogeneity as represented by the size distribution of clouds and cloud “holes,” 2) the nearest-neighbor relationships regarding their sizes and mutual distances, and 3) the scales of their clustering.

Distributionwise, cloud size can best be represented by a mixture of two power laws. Clouds of diameter below 1 km have the slope parameter ranging from about 1.4 to 2.3, while larger clouds have slopes ranging from 2.1 to 4.75. Furthermore, these clouds are bifractal in nature. The break in power law and fractal dimension occurs at a size critical to the cloud-scale processes in the following sense. First, this is the cloud size that makes the largest contribution to the extent of cloud cover. Second, there are indications that this is the size at which clouds begin to modify their environment.

Cloud inhomogeneities have significant impact on radiative fluxes. The size distribution of holes in the cumulus clouds studied here have a single slope power law with estimated slopes close to 3; these holes have single fractal dimensions. Furthermore, the results suggest that as the cloud field matures, there is an increase in the number and size of the inhomogeneities along with increasing cloud size.

Nearest-neighbor relationships are studied from two different perspectives. First, the nearest-neighbor separation distance is modeled by four probability distributions: lognormal, gamma, extreme-value and Weibull. Lognormal appears to provide the best fit. Second, the nearest-neighbor pair sizes and the associated separation distance are studied using a co-occurrence frequency approach of spatial point processes using second-order statistics. The largest frequency of nearest-neighbor pairs occurs at a distance of 200–300 m, with the largest absolute differences in cloud size found at separations of about 500 m. At larger separations, there is a tendency for the larger clouds to be closer to other large clouds, apparently through the modification of the environment. Nonlinear dependence between the sizes of nearest-neighbor cloud pairs increases with increasing cloud size.

Cumulus cloud clustering scales are determined by using the classical Greig-Smith quadrat analysis technique. Clustering scales of about 15, 29, and 59 km are found for most of the ten cloud fields studied.

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R. M. Welch, K. S. Kuo, B. A. Wielicki, S. K. Sengupta, and L. Parker

Abstract

The structural characteristics of stratocumulus cloud fields off the coast of southern California are investigated using LANDSAT Multispectral Scanner (MSS) imagery. Twelve scenes in this area are examined along with three other stratocumulus scenes near San Francisco, over central Oregon, and in the Gulf of Mexico.

Results from this initial study of stratocumulus clouds indicate that 1) cloud-background threshold selection techniques based upon edge detection gradient assumptions are not appropriate for cloud segmentation and classification algorithms; 2) cloud size distributions obey a power law; 3) cell horizontal aspect ratio increases with cell diameter, 4) stratocumulus clouds are bifractal in nature with fractal dimension of about d ≈ 1.2 for cells with diameter D < 0.5 km and d ≈ 1.5 for cells with D > 0.5 km; 5) stratocumulus cloud fields appear to be homogeneous over regions of about 100 km × 100 km, a much smaller region than the 2.5° × 2.5° boxes to be used in the ISCCP regional averaging algorithms; and 6) structural properties of stratocumulus clouds observed off the coast of southern California are similar to those properties observed for stratocumulus clouds at three other locations.

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Dipanjan Chaudhuri, Debasis Sengupta, Eric D’Asaro, R. Venkatesan, and M. Ravichandran

Abstract

Cyclone Phailin, which developed over the Bay of Bengal in October 2013, was one of the strongest tropical cyclones to make landfall in India. We study the response of the salinity-stratified north Bay of Bengal to Cyclone Phailin with the help of hourly observations from three open-ocean moorings 200 km from the cyclone track, a mooring close to the cyclone track, daily sea surface salinity (SSS) from Aquarius, and a one-dimensional model. Before the arrival of Phailin, moored observations showed a shallow layer of low-salinity water lying above a deep, warm “barrier” layer. As the winds strengthened, upper-ocean mixing due to enhanced vertical shear of storm-generated currents led to a rapid increase of near-surface salinity. Sea surface temperature (SST) cooled very little, however, because the prestorm subsurface ocean was warm. Aquarius SSS increased by 1.5–3 psu over an area of nearly one million square kilometers in the north Bay of Bengal. A one-dimensional model, with initial conditions and surface forcing based on moored observations, shows that cyclone winds rapidly eroded the shallow, salinity-dominated density stratification and mixed the upper ocean to 40–50-m depth, consistent with observations. Model sensitivity experiments indicate that changes in ocean mixed layer temperature in response to Cyclone Phailin are small. A nearly isothermal, salinity-stratified barrier layer in the prestorm upper ocean has two effects. First, near-surface density stratification reduces the depth of vertical mixing. Second, mixing is confined to the nearly isothermal layer, resulting in little or no SST cooling.

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T. Koyama, T. Vukicevic, M. Sengupta, T. Vonder Haar, and A. S. Jones

Abstract

Information content analysis of the Geostationary Operational Environmental Satellite (GOES) sounder observations in the infrared was conducted for use in satellite data assimilation. Information content is defined as a first-order response of the top-of-atmosphere brightness temperature to perturbations of simulated temperature and humidity profiles, obtained from a cloud-resolving model, both in the presence and absence of clouds. Sensitivity to the perturbations was numerically evaluated using an observational operator for visible and infrared radiative transfer developed within a research satellite data assimilation system. The vertical distribution of the sensitivities was analyzed as a function of cloud optical thickness covering the range from a cloud-free scene to an optically thick cloud. The clear-sky sensitivities to temperature and humidity perturbations for each channel are representative of the corresponding channel weighting functions for a clear-sky case. For optically thin–moderate ice clouds, the vertical distributions of the sensitivities resemble clear-sky results, indicating that the use of infrared sounding observations in data assimilation can potentially improve temperature and humidity profiles below those clouds. This result is significant, as GOES infrared sounder data have until now only been used in cloud-cleared scenes. It is expected that the use of sounder data in data assimilation, even in the presence of optically thin to moderate high clouds, will help reduce errors in temperature and water vapor mixing ratio profiles below the clouds.

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R. A. Weller, J. T. Farrar, Hyodae Seo, Channing Prend, Debasis Sengupta, J. Sree Lekha, M. Ravichandran, and R. Venkatesen

Abstract

Time series of surface meteorology and air–sea fluxes from the northern Bay of Bengal are analyzed, quantifying annual and seasonal means, variability, and the potential for surface fluxes to contribute significantly to variability in surface temperature and salinity. Strong signals were associated with solar insolation and its modulation by cloud cover, and, in the 5- to 50-day range, with intraseasonal oscillations (ISOs). The northeast (NE) monsoon (DJF) was typically cloud free, with strong latent heat loss and several moderate wind events, and had the only seasonal mean ocean heat loss. The spring intermonsoon (MAM) was cloud free and had light winds and the strongest ocean heating. Strong ISOs and Tropical Cyclone Komen were seen in the southwest (SW) monsoon (JJA), when 65% of the 2.2-m total rain fell, and oceanic mean heating was small. The fall intermonsoon (SON) initially had moderate convective systems and mean ocean heating, with a transition to drier winds and mean ocean heat loss in the last month. Observed surface freshwater flux applied to a layer of the observed thickness produced drops in salinity with timing and magnitude similar to the initial drops in salinity in the summer monsoon, but did not reproduce the salinity variability of the fall intermonsoon. Observed surface heat flux has the potential to cause the temperature trends of the different seasons, but uncertainty in how shortwave radiation is absorbed in the upper ocean limits quantifying the role of surface forcing in the evolution of mixed layer temperature.

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Steven D. Miller, John M. Forsythe, Philip T. Partain, John M. Haynes, Richard L. Bankert, Manajit Sengupta, Cristian Mitrescu, Jeffrey D. Hawkins, and Thomas H. Vonder Haar

Abstract

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

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