Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones(TCs)precipitation(DSAEF_LTP)utilises an operational numerical weather prediction(NWP)model for the forecast track,while the precipitation forecast is obtained by finding analog cyclones,and making a precipitation forecast from an ensemble of the analogs.This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments.Experiments use four model versions.The control experiment DSAEF_LTP_1 includes three factors including TC track,landfall season,and TC intensity to determine analogs.Versions DSAEF_LTP_2,DSAEF_LTP_3,and DSAEF_LTP_4 respectively integrate improved similarity region,improved ensemble method,and improvements in both parameters.Results show that the DSAEF_LTP model with new values of similarity region and ensemble method(DSAEF_LTP_4)performs best in the simulation experiment,while the DSAEF_LTP model with new values only of ensemble method(DSAEF_LTP_3)performs best in the forecast experiment.The reason for the difference between simulation(training sample)and forecast(independent sample)may be that the proportion of TC with typical tracks(southeast to northwest movement or landfall over Southeast China)has changed significantly between samples.Forecast performance is compared with that of three global dynamical models(ECMWF,GRAPES,and GFS)and a regional dynamical model(SMS-WARMS).The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm.Compared with TCs without heavy precipitation or typical tracks,TCs with these characteristics are better forecasted by the DSAEF_LTP model.

Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

A study on improving anti-interference capability of inversion method reconstructing layered sea bottom parameters

The remote measurement of the vertical distribution of acoustical parameters of the sea bottom is an inverse method in acoustics. The existing methods can be divided into two types: the wave method and the ray method. The former usually does not apply to cases with discontinuity in acoustical parameter profiles[1] , whereas the latter usually ignores the contributions made by multiple reflections, resulting in the appearance of pseudo interfaces. However, under certain practical circumstances, the ray method can produce satisfactory results[2,3]. In this paper, at first we use the conception of equitime layer thickness to obtain the pulse response iteration method. The anti-interference capability of this method is poor. In order to improve this capability, we develope the sample iteration method and the sample-averaging iteration method. The sample-averaging iteration method introduces a way of statistical averaging, which can further improve the anti-interference capability.