Comparison of the Minimum Bounding Rectangle and Minimum Circumscribed Ellipse of Rain Cells from TRMM

Based on the TRMM dataset, this paper compares the applicability of the improved MCE(minimum circumscribed ellipse), MBR(minimum bounding rectangle), and DIA(direct indexing area) methods for rain cell fitting. These three methods can reflect the geometric characteristics of clouds and apply geometric parameters to estimate the real dimensions of rain cells. The MCE method shows a major advantage in identifying the circumference of rain cells. The circumference of rain cells identified by MCE in most samples is smaller than that identified by DIA and MBR, and more similar to the observed rain cells. The area of rain cells identified by MBR is relatively robust. For rain cells composed of many pixels(N> 20), the overall performance is better than that of MCE, but the contribution of MBR to the best identification results,which have the shortest circumference and the smallest area, is less than that of MCE. The DIA method is best suited to small rain cells with a circumference of less than 100 km and an area of less than 120 km^(2), but the overall performance is mediocre. The MCE method tends to achieve the highest success at any angle, whereas there are fewer “best identification”results from DIA or MBR and more of the worst ones in the along-track direction and cross-track direction. Through this comprehensive comparison, we conclude that MCE can obtain the best fitting results with the shortest circumference and the smallest area on behalf of the high filling effect for all sizes of rain cells.

Helicopter maritime search area planning based on a minimum bounding rectangle and K-means clustering

Helicopters are widely used in maritime Search and Rescue(SAR) missions. To ensure the success of SAR missions, search areas need to be carefully planned. With the development of computer technology and weather forecast technology, the survivors’ drift trajectories can be predicted more precisely, which strongly supports the planning of search areas for the rescue helicopter. However, the methods used to determine the search area based on the predicted drift trajectories are mainly derived from the continuous expansion of the area with the highest Probability of Containment(POC), which may lead to local optimal solutions and a decrease in the Probability of Success(POS), especially when there are several subareas with a high POC. To address this problem, this paper proposes a method based on a Minimum Bounding Rectangle and Kmeans clustering(MBRK). A silhouette coefficient is adopted to analyze the distribution of the survivors’ probable locations, which are divided into multiple clusters with K-means clustering. Then,probability maps are generated based on the minimum bounding rectangle of each cluster. By adding or subtracting one row or column of cells or shifting the planned search area, 12 search methods are used to generate the optimal search area starting from the cell with the highest POC in each probability map. Taking a real case as an example, the simulation experiment results show that the POS values obtained by the MBRK method are higher than those obtained by other methods,which proves that the MBRK method can effectively support the planning of search areas and that K-means clustering improves the POS of search plans.