Detection of mesoscale thermal fronts from 4Â km data using smoothing techniques: Gradient-based fronts classification and basin scale application

This study shows that adequate noise reduction achieved by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method is easily applicable to large regions or at the global scale, with far less constraints of data manipulation and processing time using 4 km data relative to 1 km data.