The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Fog is challenging to predict, and the accuracy of fog prediction may depend on location and time of day. Furthermore, accurate detection of fog is difficult, since, historically, it is often carried out based on visual observations which can be biased and are often not very frequent. Furthermore, visual observations are more challenging to make during the night. To overcome these limitations, we detected fog using FM-120 instruments, which continuously measured liquid water content in the air in the Monterey, California (USA), area. We used and compared the prediction performance of logistic regression (LR) and random forest (RF) models each evening between 5 pm and 9 pm, which is often the time when advection fog is generated in this coastal region. The relative performances of the models depended on the hours between 5 pm and 9 pm, and the two models often generated different predictions. In such cases, a consensus approach was considered by revisiting the past performance of each model and weighting more heavily the more trustworthy model for a given hour. The LR resulted in a higher sensitivity (hit rate) than the RF model early in the evening, but the overall performance of the RF was usually better than that of the LR. The consensus approach provided more robust prediction performance (closer to a better accuracy level between the two methods). It was difficult to conclude which of the LR and RF models was superior consistently, and the consensus approach provided robustness in 3 and 2 h forecasts.

Les systèmes CODAR Seasonde et MCR (Multi-Frequency Coastal Radar) sont conçus pour mesurer les caractéristiques environnementales océaniques, en particulier les courants océaniques, les vagues et les vents. En plus de permettre ces mesures, ces systèmes ont aussi la capacité de détecter des cibles discrètes comme les navires. Dans cet article, on discute des mesures réalisées à l'aide de ces deux systèmes dans le contexte de la détection des navires sur la côte est et ouest des Etats-Unis. Des données radar acquises au-dessus du Lac Michigan démontrent aussi, pour la première fois, le potentiel du radar HF pour la détection des navires au-dessus de lacs d'eau douce. Les données de rétrodiffusion acquises à partir de navires de convenance sont consistantes avec les considérations théoriques sur les portées maximales atteignables pour la détection des navires dans le cas des systèmes CODAR SeaSonde et MCR. Des estimations des surfaces équivalentes des navires sont aussi consistantes avec les valeurs expérimentales existantes. Les méthodes comme la stationnanté du signal, la diversité de fréquences et le suivi des pics permettent de distinguer les navires des autres cibles et sources de bruit à l'intérieur des échos reçus de ces deux systèmes radar.

Simulation techniques are applied to evaluate the performance of HF radar surface current algorithms. The simulation assumes that the ocean echo is due to first order Bragg scatter from a large number of differential area elements in an annular ring of the ocean surface, and generates, to first order, the voltage time series data which would be sampled from an eight element phased array, at four frequencies from 4.8 to 21.8 MHz. The geometry and frequencies match those of a pair of existing pulsed radar systems currently deployed at Monterey Bay. The simulation addresses the problem of estimation of accuracy and error bounds for the ocean radar system. The authors examine physically plausible surface current profiles with random fluctuations in the amplitude of the scattered signal as a function of position on the ocean surface. Deviations between simulation input and algorithm output as a function of the parameters input to the simulation are examined for both direction finding and beam forming algorithms.