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Harshvardhan and James A. Weinman

Abstract

A study has been made of infrared radiative transfer through a regular array of cuboidal clouds which considers the interaction of the sides of the clouds with each other and the ground. The theory is developed for black clouds and is extended to scattering clouds using a variable azimuth two-stream (VATS) approximation (Harshvardhan et al., 1981). It is shown that geometrical considerations often dominate over the microphysical aspects of radiative transfer through the clouds. For example, the difference in simulated 10 μm brightness temperature between black isothermal cubic clouds and cubic clouds of optical depth 10, is <2 K for zenith angles <50° for all cloud fractions when viewed parallel to the array.

The results show that serious errors are made in flux and cooling rate computations if broken clouds are modeled as planiform. Radiances computed by the usual practice of area-weighting cloudy- and clear-sky radiances are in error by 2–8 K in brightness temperature for cubic clouds over a wide range of cloud fractions and zenith angles. It is also shown that the lapse rate does not markedly affect the exiting radiances for cuboidal clouds of unit aspect ratio and optical depth 10.

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Louis Garand and James A. Weinman

Abstract

A structural-stochastic image model is developed for the analysis and synthesis of cloud images. The ability of the model to characterize the visual appearance of cloud fields observed by satellite with a limited number of parameters is demonstrated. The model merges structural and stochastic information, the stochastic model acting as a local statistical operator applied to the output of the structural model. The structural or large-scale organization of the scene is retrieved from the two-dimensional Fourier representation of the digital image. The pattern generated by the major Fourier components provides a first guess of the scene. The stochastic aspect is described by a Markov model of texture that assumes a binomial probability distribution for the local grey-level variability. This Markov model provides four parameters that represent the clustering strength in the horizontal, vertical and diagonal directions. These parameters are estimated by a standard maximum-likelihood technique. The image can be reproduced with a fair degree of verisimilitude from these parameters. The data compression factor is of the order of one hundred to several hundreds.

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Jerold T. Twitty and James A. Weinman

Abstract

The complex index of refraction for various carbonaceous materials is presented for wavelengths from 0.3μ≲λ≲20μ. An ensemble of spherical carbon dust particles represented by two size distributions typical of urban aerosols is utilized in conjunction with Mic theory to determine the extinction coefficient, the albedo for single scattering, the asymmetry factor, 〈cosθ〉, the average phase function, and the polarization for wavelengths 0.3μ≲λ≲15μ.

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Harshvardhan, James A. Weinman, and Roger Davies

Abstract

The transport of infrared radiation in a single cuboidal cloud has been modeled using a variable azimuth two-stream (VATS) approximation. Computations have been made at 10 μm for a Deirmendjian (1969) C-1 water cloud of single scattering albedo, ω = 0.638 and asymmetry parameter, g=0.865. Results indicate, that the emittance of the top face of the model cloud is always less than that for a plane parallel cloud of the same optical depth. The hemispheric flux escaping from the cloud top has a gradient from the censor to the edges which are warmer when the cloud is over warmer ground. Cooling rate calculations in the 8–13.6 μm region show that there is cooling out of the sides of the cloud at all levels even when there is heating of the core from the ground below.

The radiances exiting from model cuboidal clouds were computed by path integration over the source function obtained with the two-stream approximation. Results suggest that the brightness temperature measured from finite clouds will overestimate the cloud-top temperature.

Some key results of the model have been compared with Monte Carlo simulations. Overall errors in flux and radiance average a few degrees for most cases.

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Min-Jeong Kim, James A. Weinman, and Robert A. Houze

Abstract

This study compares the surface rainfall retrieved from the Goddard profiling (GPROF; version 5) algorithm with Kwajalein ground-based radar (KR) observations at 0.1°, 0.25°, and 1° resolutions. Comparisons of the GPROF-retrieved rainfall with KR observations for 178 overpasses show that GPROF overestimated surface rainfall with respect to the KR by 16%. Power spectral density comparisons between GPROF and KR rain maps at 0.1° resolution show that GPROF-retrieved rain maps are less spatially variable at wavelengths less than 50 km in the mean, suggesting that GPROF rainy areas are smoother and more spatially extensive than those observed by the KR. Sensitivity of rainfall retrievals to the melting layer and the impact of 85-GHz channels were tested. This study introduced Klaassen's melting-layer parameterization into the GPROF algorithm that reduced the GPROF-retrieved rainfall amount by 7.5%. Considering the poor correlation between upper-level ice amounts and surface rainfall, this study only estimated the convective area fraction from 85-GHz brightness temperature and neglected 85-GHz brightness temperature in the rainfall retrieval. This modification reduced GPROF-retrieved surface rainfall amount by 3.5% and made the GPROF-retrieved rainfall retrievals more consistent with the KR observations where the GPROF algorithm overestimated surface rainfall with respect to the KR because of strong ice scattering. For the 178 overpasses used in this study the total rainfall amount retrieved by the revised GPROF algorithm overestimated rainfall by 1% with respect to that measured by the KR.

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Roland T. Chin, Jack Y. C. Jau, and James A. Weinman

Abstract

A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.

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Louis Garand, James A. Weinman, and Christopher C. Moeller

Abstract

The usefulness of cloud classification for detecting and quantifying air temperature and humidity anomalies above the ocean surface is examined. Cloud fields are classified in 20 classes following the automated method of Garand (1988), here applied over the northwestern Atlantic during the winter season. From collocation of the classified cloud fields (scale of ≈130 km) with ship or buoy observations of air temperature and humidity, significant anomalies are found for specific cloud classes while for other classes no anomaly is found. All results are verified from independent data taken in early 1984 and 1986.

The results confirm that for the mesoscale cellular convective patterns (MCC), i.e., cloud “streets”, rolls, and open cells, the air and dew point temperatures are colder than climatology by several degrees, implying large latent and sensible heat fluxes. A latitudinal dependency of the anomaly is also observed. The removal of this bias provides estimates of surface air temperature with an accuracy of 2.8 K for these cloud types. Cirrus cloud classes and low stratus are associated with surface relative humidities above 80% while MCC patterns are associated with relatively dry surface humidity, below 70%. For those classes, the dew point depression can be inferred with an accuracy of 2 K; the corresponding relative humidity is determined with an accuracy of 10%.

The implications for numerical weather prediction are discussed by comparing the error statistics of the satellite estimates with those of the trial fields (6-h forecasts) used in the analysis cycle of the Canadian Meteorological Center. The humidity estimates are expected to have a greater influence than the temperature estimates because the temperature field is already well analyzed by conventional means whereas the humidity analyses are often deficient.

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Rongzhang Wu, James A. Weinman, and Roland T. Chin

Abstract

Radiances from clouds observed in visible and infrared images obtained from the SMS-2, GOES-2, and GOES-4 satellites have been used to estimate rainfall by means of a pattern recognition algorithm that was applied to single images. The algorithm classified rain into three classes: 0—no rain (0 ≤ R <0.5 mm h−1); 1—light rain (0.5 ≤ R <0.5 mm h−1); and 2—heavy rain (5.0 mm h−1R). The rainfall rates used in the training set and those used to test the algorithm were derived from a set of twenty-nine Plan Position Indicator (PPI) displays obtained from NOAA operational radars. Data were derived from summer storms, tropical storms and cyclones.

Rainfall from precipitating clouds was classified by a pattern recognition technique that used textural and radiance features in a hierarchic decision tree. The analysis was applied to regions 20 × 20 km in area that were measured in the visible spectral region with 1 × 1 km and 2 × 2 km resolution and in the infrared with 4 × 8 km resolution. The radiance features used in this analysis were the radiance maxima, minima, and the means. The textural features that were used included the edge strengths per unit area and the maxima and means of the mean, contrast, angular second moment, and entropy in four directions.

Of the arm sampled in this study, approximately one-third were in classes 0, one-half were in class 1 and one-sixth were in class 2. Case studies that employed data from both the visible and infrared sensors correctly identified rainfall classes 0 and (1 + 2) in about 96% of the cases and identification into classes 1 and 2 was correct in about 70% of the cases studied. The corresponding skill scores were ∼80 and 60% respectively. Data derived only from infrared images yielded correct identification of 0 and (1 + 2) classes in 85% of the cases and identification of classes 0, 1 and 2 was correct in 65% of the cases. The corresponding skill scores were ∼65% and 40% respectively.

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Craig R. Burfeind, James A. Weinman, and Bruce R. Barkstrom

Abstract

This study has applied computerized pattern analysis techniques to the location and classification of feature of several mature extratropical cyclones that were depicted in GOES satellite images. These features include the location of the center of the cyclone vortex core and the location of the associated occluded front. The cyclone type was classified in accord with the scheme of Troup and Streten. The present analysis was implemented on a personal computer, results were obtained within approximately one or two minutes without the intervention of an analyst.

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G. David Alexander, James A. Weinman, and J. L. Schols

Abstract

A technique is described in which forecasts of the locations of features associated with marine cyclones may be improved through the use of microwave integrated water vapor (IWV) imagery and image warping of forecast mesoscale model fields. Here, image warping is used to optimally match mesoscale model output to observations of IWV measured by microwave sensors. In the mesoscale model simulations presented here (one of the March 1993 “superstorm,” one of a rapidly deepening cyclone observed in the North Atlantic in February 1992, and one of the ERICA IOP 4 cyclone), the Pennsylvania State University–National Center for Atmospheric Research MM5 model is initialized from the standard National Meteorological Center (recently renamed the National Centers for Environmental Prediction) operational analysis. The simulations are then run until a time at which a Special Sensor Microwave/Imager (SSM/I) overpass occurs. For each simulation, the forecast pattern of IWV is then compared to the field shown in the SSM/I image. In all three cases, the MM5 moves the cyclones too slowly, and therefore places distinguishing features in the forecast IWV fields significantly upstream of their locations as revealed in the microwave imagery. To rectify these errors, the grid on which the source image (forecast field) is defined is then warped to match the target image (remotely observed IWV field) by choosing pairs of tie points corresponding to similar features in the two images. The values of all model moisture variables at all vertical levels are then carried to the new warped grid points and interpolated back to the original model grid. Model integration then proceeds with the new model fields. The model results at a subsequent time after the warping is applied are then compared with simultaneous model results in simulations in which no warping was applied as well as with model simulations in which a standard nudging technique is applied. Warping results in improved forecasts of cyclone minimum sea level pressure, tracks, and IWV fields over both the control simulations and the nudged simulations.

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