Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

An Abrupt Decrease in the Late-Season Typhoon Activity over the Western North Pacific

Abstract: In 1995 an abrupt shift in the late-season (October‐December) typhoon activity over the western North Pacific (WNP) is detected by a Bayesian changepoint analysis. Interestingly, a similar change also occurs in the late-season sea surface temperature series over the western Pacific, eastern North Pacific, and portions of the Indian Ocean. All of the counts, lifespans, and accumulated cyclone energy of the late-season typhoons during the 1995‐2011 epoch decreased significantly, compared with typhoons that occurred during the 1979‐94 epoch. The negative vorticity anomaly is found to be the leading contributor to the genesis potential index (GPI) decrease over the southeastern sector of the WNP during 1995‐2011. To elucidate the origin of the epochal change in the dynamic environmental conditions, a suite of sensitivity experiments is conducted based on the latest version of the Japan Meteorological Research Institute atmospheric general circulation model (MRI AGCM). The ensemble simulations suggest that the recent change to a La Ni~ state induces an unfavorable dynamic condition for typhoon genesis over the southeastern WNP. Warming in the Indian Ocean, however,contributes insignificantlytothe circulation anomalyrelated totyphoon genesis overthesoutheastern WNP. The frequency of typhoon occurrence reveals a basinwide decrease over the WNP in the recent epoch, except for a small increase near Taiwan. An empirical statistical analysis shows that the basinwide decrease in the frequency of the typhoon occurrence is primarily attributed to a decrease in typhoon genesis, while the change in track is of less importance.

Taking the pulse of COVID-19: a spatiotemporal perspective

Abstract: The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, Ind...

The contribution of China’s Grain to Green Program to carbon sequestration

Abstract: Forests play an important role in regulating atmospheric carbon dioxide concentration and mitigating the greenhouse effect. The Grain to Green Program (GGP) is one of the largest ecological programs in China, and it aims at converting croplands on steep slopes to forests. However, the magnitude and distribution of carbon sequestration induced by GGP remain unknown. In this study, we estimated the changes in carbon fluxes and stocks caused by forests converted from croplands under the GGP using a process-based ecosystem model (i.e., IBIS). Our results showed that the converted areas from croplands to forests under the GGP program could sequester 110.45 Tg C by 2020, and 524.36 Tg C by the end of this century. The sequestration capacity showed substantial spatial variations with large sequestration in southern China. The economic benefits of carbon sequestration from the GGP were also estimated according to the current carbon price. The estimated economic benefits ranged from $8.84 to $44.20 billion from 2000 through 2100, which may exceed the current total investment ($38.99 billion) on the program. As the GGP program continues and forests grow, the impact of this program will be even larger in the future, making a more considerable contribution to China’s carbon sink over the upcoming decades.

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

Abstract: This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.