Using the physical decomposition method to study the effects of Arctic factors on wintertime temperatures in the Northern Hemisphere and China

The physical decomposition method separates atmospheric variables into four parts, correlating each with solar radiation, land-sea distribution, and inter-annual and seasonal internal forcing, strengthening the anomaly signal and increasing the correlation between variables. This method was applied to the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR）, to study the effects of Arctic factors （Arctic oscillation （AO） and Arctic polar vortex） on wintertime temperatures in the Northern Hemisphere and China. It was fotmd that AO effects on zonal average temperature disturbance could persist for 1 month. In the AO negative phase in wintertime, the temperatures are lower in the mid-high latitudes than in normal years, but higher in low latitudes. When the polar vortex area is bigger, the zonal average temperature is lower at 50N. Influenced mainly by meridional circulation enhancement, cold air flows from high to low latitudes; thus, the temperatures in Continental Europe and the North American continent exhibit an antiphase seesaw relationship. When the AO is in negative phase and the Arctic polar vortex larger, the temperature is lower in Siberia, but higher in Greenland and the Bering Strait. Influenced by westerly troughs and ridges, the polar air disperses mainly along the tracks of atmospheric activity centers. The AO index can be considered a predictor of wintertime temperature in China. When the AO is in negative phase or the Asian polar vortex is intensified, temperatures in Northeast China and Inner Mongolia are lower, because under the influence of the Siberia High and northeast cold vortex, the cold air flows southwards.

Resource Allocation for Physical Layer Security in Heterogeneous Network with Hidden Eavesdropper

The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.

Application of synoptic-scale anomalous winds predicted by medium-range weather forecast models on the regional heavy rainfall in China in 2010

Atmospheric winds from observations and medium-range weather forecast model predictions can be physically decomposed as daily climate wind,planetary-scale anomalous wind,and synoptic-scale anomalous wind.The 850 hPa synoptic-scale anomalous winds were extracted from the numerical model outputs of the European Centre for Medium-Range Weather Forecasts(ECMWF) and the NCEP Global Forecast System(GFS).The results showed that most rain bands in eastern China in 2010 were located along the anomalous convergence lines.To predict the major rain bands by these convergence lines in 2010,the accuracies of the ECMWF products were 100%,85%,and 15% for leading 3,6,and 9 days,while the GFS products showed 53%,15%,and 6% accuracies,respectively.In comparison of the regional heavy rainfalls between observation and the ECMWF model prediction,the useful leading information was about 3.1 days for direct model rain prediction and 6.7 days for convergence systems predicted by ECMWF model.