Potential Intensification Rate of Tropical Cyclones in a Simplified Energetically Based Dynamical System Model: An Observational Analysis

In a recent study by Wang et al. (2021a) that introduced a dynamical efficiency to the intensification potential of a tropical cyclone (TC) system, a simplified energetically based dynamical system (EBDS) model was shown to be able to capture the intensity-dependence of TC potential intensification rate (PIR) in both idealized numerical simulations and observations. Although the EBDS model can capture the intensity-dependence of TC intensification as in observations, a detailed evaluation has not yet been done. This study provides an evaluation of the EBDS model in reproducing the intensity-dependent feature of the observed TC PIR based on the best-track data for TCs over the North Atlantic, central, eastern and western North Pacific during 1982–2019. Results show that the theoretical PIR estimated by the EBDS model can capture basic features of the observed PIR reasonably well. The TC PIR in the best-track data increases with increasing relative TC intensity (intensity normalized by its corresponding maximum potential intensity–MPI) and reaches a maximum at an intermediate relative intensity around 0.6, and then decreases with increasing relative intensity to zero as the TC approaches its MPI, as in idealized numerical simulations. Results also show that the PIR for a given relative intensity increases with the increasing MPI and thus increasing sea surface temperature, which is also consistent with the theoretical PIR implied by the EBDS model. In addition, future directions to include environmental effects and make the EBDS model applicable to predict intensity change of real TCs are also discussed.

Comparison of mutual information and its point similarity implementation for image registration

Mutual information (MI) is one of the most popular and widely used similarity measures in image registration. In traditional registration processes, MI is computed in each optimization step to measure the similarity between the reference image and the moving image. The presumption is that whenever MI reaches its highest value, this corresponds to the best match. This paper shows that this presumption is not always valid and this leads to registration error. To overcome this problem, we propose to use point similarity measures (PSM) which in contrast to MI allows constant intensity dependence estimates called point similarity functions (PSF). We compare MI and PSM similarity measures in terms of registration misalignment errors. The result of the comparison confirms that the best alignment is not at the highest value of MI but near to it and it shows that PSM performs better than MI if PSF matches the correct intensity dependence between images. This opens a new direction of research towards the improvement of image registration.

Multi-Modality Non-rigid Image Registration Using Local Similarity Estimations

In this study, we focus on improving the efficiency and accuracy of nonrigid multi-modality registration of medical images. In this regard, we analyze the potentials of using the point similarity measurement approach as an alternative to global computation of mutual information (MI), which is still the most renown multi-modality similarity measure. The improvement capabilities are illustrated using the popular B-spline transformation model. The proposed solution is a combination of three related improvements of the most straightforward implementation, i.e., efficient computation of the voxel displacement field, local estimation of similarity and usage of a static image intensity dependence estimate. Five image registration prototypes were implemented to show contribution and dependence of the proposed improvements. When all the proposed improvements are applied, a significant reduction of computational cost and increased accuracy are obtained. The concept offers additional improvement opportunities by incorporating prior knowledge and machine learning techniques into the static intensity dependence estimation.