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Robert W. Fett and Paul M. Tag

Abstract

Meteorological satellite scanning radiometer data from a visual sensor during daylight hours are characteristically influenced by sunglint from the ocean surface as the sensor scans in the direction toward the sun (between the satellite subpoint and solar subpoint). When seas are calm in the region near the primary specular point (PSP), the sun's rays are either reflected directly into the spacecraft sensor yielding a high energy (bright) response, or away from the sensor yielding a low energy (dark) response. The particular effect depends on the proximity of the calm area to the PSP. This paper shows examples of bright and dark linear patterns adjacent to and tending to parallel coastlines. The patterns are interpreted to be sea-breeze-induced calm zones originating during periods of offshore flow when the pressure gradient causing the sea breeze is exactly counterbalanced by the larger-wale synoptic gradient. A two-dimensional planetary boundary layer (PBL) numerical model successfully simulates this condition and additionally shows that the calm region first appears near the coastline as daytime heating commence and then moves seaward with time as afternoon heating over land is maximized. We show that, at least initially, the rapidity of movement and the distance covered in this movement are directly related to the land-sea temperature contrast and indirectly related to the speed of the offshore flow, and are highly sensitive to small changes in these parameters.

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James E. Peak and Paul M. Tag

Abstract

An Expert system for Shipboard Obscuration Prediction (AESOP), an artificial intelligence approach to forecasting maritime visibility obscurations, has been designed, developed, and tested. The problem-solving model for AESOP, running within an IBM-PC environment, is rule-based, uses backward chaining, and has meta-rules; a user, in a consultation session, answers questions about certain atmospheric parameters. The current version, AESOP 2.0, has 232 rules and has been designed in terms of nowcasts (0–1 h) and forecasts (1–6 h). An extensive explanation feature allows the user to understand the reasoning process behind a particular forecast. AESOP has been evaluated against 83 test cases, in which clear, hazy, or foggy conditions are predicted. The overall performance of AESOP is 75% correct. This value indicates considerable forecast skill when compared to 47% for persistence and 41% for random chance. When the distinction between clear and haze is ignored, the expert system correctly forecasts 84% of the “Fog/No fog” situations.

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