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This report describes the development and testing of an objective technique to forecast cloudiness and precipitation through extrapolation of satellite imagery. By utilizing on objectively determined cloud-motion vector, the technique makes local forecasts of satellite parameters (brightness and IR temperature), with high temporal resolution, using simple linear extrapolation. Algorithms are then used to convert the satellite parameters to total cloud cover, probability of 1-hour precipitation, and presence of low, middle, and high clouds. The test program computed motion vectors and made forecasts out to 7 hours, in half-hour steps, at 30 locations. The program was tested on 12 spring and fall cases, using half-hourly GOES imagery. For periods beyond 2 hours, forecasts of cloud cover and precipitation were markedly better than persistence, which deficiencies in specification hindered short-period performance. Forecasts of cloud layers were worse than persistence due to inadequate specification algorithms. The net results were quite encouraging, and further refinements and developments are planned.
An effort is underway at Air Force Geophysics Laboratory to develop automated procedures to make short-range (0-6 hr) terminal weather forecasts using GOES imagery data. A simple approach is to extrapolate the cloud patterns using motion vectors derived from a comparison of successive images. This report describes a test of candidate motion vector techniques using twelve cases of six successive images in a variety of weather conditions. Included in the techniques were two that track brightness centers, three that use cross-covariance, and two using winds aloft. All were compared against persistence (no motion, no change). For all time periods and all thresholds, a binary covariance technique had the highest scores but the techniques using winds aloft were very close. Also, no technique was much better than persistence. There is evidence that most (perhaps 75 percent) of the total changes occurring are not due to simple motion of the cloud patterns but due to more complex processes.
This report summarizes the work accomplished during the first phase of an investigation concerning methods of introducing digitized satellite imagery into short-range, objective forecasting operations. The data archive being assembled for this study is described, with particular attention given to the steps taken to maximize the accuracy of the satellite imagery. These steps included 'fine tuning' the navigation and selecting procedures for 'normalizing' the data by correcting for the effects of Lambertian and anisotropic scattering. Consistency of the data, spatial and temporal, was tested by analysis of ground reflectance during cloudless days, and a pilot test of the specification of single layers of clouds was conducted. Both of these tests gave encouraging results. An investigation of specifying precipitation rate, using just the visible reflectance and infrared temperature of the cloud top, also produced good results. Nomograms for the average rate during the hour following the satellite observation, as well as for the probability of observing more than 0.01 in. and 0.10 in. of precipitation, are illustrated. Two appendices present the geometrical and optical equations relevant to the investigation. (Author).
Skill-scores, relative to climatology, for some parameters such as ceiling/visibility and precipitation are much lower than others, such as minimum temperature and pressure gradients. Also, the skill-scores have been improving appreciably faster for forecasts of 36 h (and more) than for forecasts of 24 h (and less). At the shortest ranges, less than 12 h, skill-scores relative to persistence are rather low, with values of 0.0 to 0.5 as typical. Power spectra for wind, temperature, dew point, rainfall rate, cloud reflectivity, and extinction coefficient (inversely related to visibility) were computed for periods of 10 min to 20 days, using fall season data from northeast United States. Analyses of these spectra indicate some of the problems in forecasting. Wind, temperature, and dew point spectra all had considerably more power at periods longer than 24 h than did rainfall rate, cloud reflectivity, and extinction coefficient, which relates to differences in forecast skill-scores. The greatest contribution to change for 2- to 8-h forecasts comes from disturbances with periods of about 8 to 32 h. Disturbances with periods shorter than about 24 h are purposedly filtered from current operational numerical models, in order to improve performance over longer ranges. The disturbances filtered out may be relatively unimportant to wind and temperature forecasts but quiet important for cloud and precipitation forecasts. Disturbances with periods less than about 2 h cannot be adequately resolved temporally or spatially using current weather data, yet these disturbances have sufficient amplitude to contribute noise in the analyses of longer period disturbances.