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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 ...

Two methods of deriving probability of precipitation fields (PP) over oceanic areas are presented and compared. The cloud fields are analyzed at the scale of ∼130–150 km from satellite visible and infrared imagery and collocated with ship observations of present weather. Method 1 is based on a detailed cloud classification scheme in 20 classes: a mean PP is determined for each cloud class. Method 2 assigns a PP based on cloud top temperature and mean cloud albedo only. For both methods, a normalization with respect to cloud fraction is applied. Method 1 involves more cloud field descriptors than Method 2, but the latter is simpler to implement and much faster. The PPs assigned to individual cloud fields vary between 0% and 65%. The importance of the visible sensor is clearly demonstrated, i.e., infrared-only techniques will be much less accurate. For real-time applications, the two methods provide similar results except for some specific cloud classes where maximum differences reach 13%, due to t...

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.

Oceanic cloud patterns are classified in twenty classes from visible and infrared imagery available from a geostationary satellite. A vector of features representing height, albedo, shape and multilayering characteristics of the cloud fields permits an objective classification. An original aspect of the scheme is its capability to recognize direc-tional patterns such as cloud 'rolls' or 'streets', and doughnut-shaped open cells as well, from features derived from the power spectrum of the visible image. The classifier was trained using 2000 samples of size 128 x 128km extracted from February 1984 images over the Northwestern Atlantic. Expert nephanalysts suggest strict accuracy in 79% of the cases while the machine gives at least the second best choice among twenty classes 89% of the time. The McIDAS system is used to process the imagery. The grid of analysis is super-imposed on the satellite image and as the program runs, the class number appears in the middle of each box at the rate of one every 2.5 seconds while all the information retrieved is stored in a file. Applications of the scheme are suggested for meteorological para-meters such as the probability of precipitation and the surface air and dew point temperature.

This study proposes a simple parameterization to derive surface broadband albedo from satellite observations, using the results of detailed radiative transfer computations for a variety of atmospheric and surface conditions. The end result is a single equation that directly yields surface albedo from observed albedo at the top of the atmosphere, solar zenith angle, and total precipitable water. It was demonstrated that the parameterization is valid for retrieval of both instantaneous and daily mean surface albedo. Sensitivity tests were conducted for precipitable water, aerosol, CO2, O3, and temperature profile. Preliminary validation using collocated satellite and tower measurements indicates that the absolute accuracy requirement of 5% for climate studies is well satisfied. A global monthly climatology (excluding polar areas) of surface albedo is then developed from 5 years of Earth Radiation Budget Experiment clear-sky satellite data and European Centre for Medium-Range Weather Forecasts humidity analysis data. Examination of month-to-month differences for specific 2.5°×2.5° areas indicate that the absolute random error on monthly estimates is less than 1%. Seasonal variation of surface albedo exceeding 1% can thus be detected. Comparisons with other satellite estimates show much closer agreement than with the values used in some general circulation models and numerical weather prediction models especially over snow/ice and deserts.