Tropical cyclone (TC) size is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Satellite observation data are the primary data used to estimate TC size. Traditional methods of TC size estimation rely on a priori knowledge of the meteorological domain and emerging deep learning-based methods do not consider the considerable blurring and background noise in TC cloud systems and the application of multisource observation data. In this paper, we propose TC-Resnet, a deep learning-based model that estimates 34-kt wind radii (R34, commonly used as a measure of TC size) objectively by combining infrared and microwave satellite data. We regarded the resnet-50 model as the basic framework and embedded a convolution layer with a 5 × 5 convolution kernel on the shortcut branch in its residual block for downsampling to avoid the information loss problem of the original model. We also introduced a combined channel-spatial dual attention mechanism to suppress the background noise of TC cloud systems. In an R34 estimation experiment based on a global TC dataset containing 2003–2017 data, TC-Resnet outperformed existing methods of TC size estimation, obtaining a mean absolute error of 11.287 nmi and a Pearson correlation coefficient of 0.907.

In this paper, a high-resolution Sentinel-3 synthetic aperture radar altimeter is used to observe ISWs in the Sulu Sea. Based on the advantages of the simultaneous observation of Sentinel-3 OLCI and SRAL, the changes in ${\sigma }_0$, SWH, and SSHA caused by the ISWs are quantitatively analyzed. The results show that ${\sigma }_0$ decreases and then increases after being modulated by the ISWs in the altimeter operation direction; SWH shows a large change; and the change trend of SSHA is the same as that of ${\sigma }_0$. Because of the angle between the propagation direction of the ISWs and the SRAL trajectory, the actual position corresponding to the peak power in the waveform detects the ISWs before the nadir, at which time ${\sigma }_0$ is already modulated by ISWs, resulting in the deviation of ${\sigma }_0$. In addition, the sea surface roughness within the SRAL footprint in this case is no longer uniform, which violates the assumption of retracking and leads to the incorrect estimation of geophysical parameters such as SWH and SSHA. With a view to correcting these errors, the effect of ISWs on the retracker must therefore be considered and the model for waveform modified accordingly.

Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.

Objective: To clarify the evolutionary characteristics and transmission mode of DENV-1 genotype I dengue virus in the Asia-Pacific region. Methods: 200 DENV-1 genotype I dengue viruses in the Asia-Pacific region from 2010 to 2014 and 2015 to 2019 were used for phylogenetic analysis and bayesian phylogeographic analysis using a series of molecular biology software. Results: DENV-1 genotype I dengue virus spread frequently between coastal cities in China and Southeast Asian countries. The evolution rate of DENV-1 genotype I dengue virus during 2010-2014 was 5.73×104 (/site/year,95%CI) , and the time to the most recent common ancestor was 1970-09-14.And the evolution rate of virus during 2015-2019 was 5.25×104 (/site/year,95%CI) , and the time to the most recent common ancestor was 1978-10-30.The virus spatial diffusion pathways shows the phenomenon that the dengue virus of this genotype is widespread in the Asia-Pacific region, the spread of the dengue virus between the coastal provinces and some inland provinces of China, and Southeast Asian countries. The study results indicated that the monitoring and preventive measures should be strengthened to avoid the threat of personal safety caused by large-scale spread of DENV-1 in China.