Key Laboratory of Meteorological Disaster
Key Laboratory of Meteorological Disaster
Wang C.,Key Laboratory of Meteorological Disaster |
Wang C.,Nanjing University of Information Science and Technology |
Li H.,Key Laboratory of Meteorological Disaster |
Li H.,Nanjing University of Information Science and Technology |
And 2 more authors.
Bulletin of Environmental Contamination and Toxicology | Year: 2011
A newly proposed three-dimensional model for the effects of heavy metals on the growth of batch cultures of algae that allows the estimation of the no detected toxic effect concentration (NDEC) is presented. Two batch assays with exposure to copper were investigated in situ (ship-based enclosure). As an endpoint in these studies, the carrying capacity B f, a parameter of the logistic growth model, possesses higher sensitivity and reliability than routine ecotoxicological endpoints. Using B f as the endpoint, the NDEC from the proposed model is compared to the no observed effect concentration (NOEC), Lowest Observed Effect Concentration (LOEC), the 5% effect concentration (EC 05) and no effect concentration (NEC) also calculated from field-derived data. The results show the confidence interval for the NDEC was wholly contained within the corresponding interval between the NOEC and LOEC, as well as within the corresponding much wider confidence interval for EC 05. Though the width of the confidence interval for NEC was basically equivalent to the corresponding width for NDEC, the NEC was somewhat higher than the corresponding NDEC. The results indicate that the NDEC is a promising possible alternative parameter to the NOEC. © 2011 Springer Science+Business Media, LLC.
Gong S.,Key Laboratory of Meteorological Disaster |
Dong G.,Nanjing University of Information Science and Technology |
Sun D.,Nanjing University of Information Science and Technology |
Zhao Q.,Nanjing University of Information Science and Technology
Proceedings - 4th International Conference on Information and Computing, ICIC 2011 | Year: 2011
Atmospheric water vapor plays an important role in the high-energy thermodynamics of the atmosphere and the genesis of storm systems. Water vapor remote sensing can provide a detailed primary parameter within meteorological prediction models and climate models. The research selects four typical periods MODIS images and retrieves the contents of water vapor over Chinese continent in four seasons. Then the distributed maps of water vapor are drawn. Comparison with the measured values by radiosonde in aerological stations, the retrieval values are a bit higher. The atmospheric water vapor contents over Chinese continent in winter are the lowest, the second for spring, the third for autumn, and that in summer are the highest. The whole spatial distribution of water vapor is the lowest in southwest of China, especially in Qinghai Tibetan Plateau, lower in northeast and north of China, higher in northwest of China, the highest in east and south of China. The spatial variability of water vapor content in China depends mainly on geographical location and terrain while its seasonal variability is relative with atmospheric circulation and monsoon. © 2011 IEEE.
You Q.,Nanjing University of Information Science and Technology |
You Q.,Key Laboratory of Meteorological Disaster |
You Q.,Lappeenranta University of Technology |
Min J.,Nanjing University of Information Science and Technology |
And 6 more authors.
Journal of Geophysical Research D: Atmospheres | Year: 2015
Monthly surface relative humidity (RH) data for 71 stations in the Tibetan Plateau (TP) provided by the National Meteorological Information Center/China Meteorological Administration are compared with corresponding grid points from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR hereafter) reanalysis. Mean climatologies, interannual variabilities, and trends calculated by the Mann-Kendal method are analyzed during 1961-2013. The annual regional long-term mean surface RH is 55.3%, with a clear maximum in summer (66.4%) and minimum in winter (44.9%). Compared with observations, NCEP/NCAR overestimates RH in all seasons, especially in spring (18.2%) and winter (17.8%). Mean annual regional surface RH has decreased by -0.23% decade-1 and even more rapidly in summer (-0.60% decade-1) and autumn (-0.39% decade-1). The reduction of surface RH is also captured by the NCEP/NCAR reanalysis at the surface, 400, 500, and 600 hPa. A particularly sharp reduction of RH since the mid-1990s is evident in both data sets, in line with rapid warming over the plateau. This suggests that moisture supply to the plateau from the Arabian Sea and the Bay of Bengal is limited and that variability and trends of surface RH over the TP are not uniquely driven by the Clausius-Clapeyron relationship. Key Points The climatology, variability, and trend of RH are analyzed during 1961-2013 The overestimations from NCEP/NCAR occur in four seasons RH since the mid-1990s is evident from the observation and reanalysis ©2015. American Geophysical Union. All Rights Reserved.
Zhou J.,Key Laboratory of Meteorological Disaster |
Zhou J.,Wuhan Central Meteorological Observatory |
Wei M.,Key Laboratory of Meteorological Disaster |
Wu T.,Wuhan Central Meteorological Observatory |
Li N.,Key Laboratory of Meteorological Disaster
2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings | Year: 2011
With high temporal and spatial resolution, Doppler weather radars are important means for revealing structures and revolution of meso-micro scale weather processes. This article uses reflectivity characteristics to identify convective gale weather. 6 promising identification parameters are proposed (CR, VIL, DVIL, SWP, DCRH and SPEED), and an automated identification algorithm for convective gale is established based on fuzzy logic principles. 6 typical cases are used to obtain probability distribution characters based on the statistics of volume scan data, and then it is determined that CR, VIL, DVIL and SWP that have more concentrated probability densities are used as the input variables of the fuzzy logic technique for the identification of the convective gale. According to the statistics, these parameters can effectively identify convective gale. The algorithm identifies 150 from 174 gale wind events in 6 weather processes, with a POD probability 86.21%. © 2011 IEEE.