Climate change threatens polar bear populations: a stochastic demographic analysis

The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in lambda in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log lambdas, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log lambdas approximately - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act.     

粉尘在巷道中的紊流传递分析

建立的粉尘在巷道中的传递方程是一个二维平流扩散输运方程。在巷道顶底板为吸收壁的条件下，导出了粉尘浓度分布函数。传统的工业沉降室沉降效率公式（横向混合模型）只是论文所列方程的一个特例。降低紊流强度和减小紊流扩散系数和纵向弥散系数均可提高沉降效率     

Urban meteorological modelling for nuclear emergency preparedness

The main objectives of the current EU project “Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure” (FUMAPEX) are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction (NWP) models to urban air pollution and population dose models, the building of improved urban air quality information and forecasting systems, and their application in cities in various European climates. In addition to the forecast of the worst air-pollution episodes in large cities, the potential use of improved weather forecasts for nuclear emergency management in urban areas, in case of hazardous releases from nuclear accidents or terror acts, is considered. Such use of NWP data is tested for the Copenhagen metropolitan area and the Øresund region.The Danish Meteorological Institute (DMI) is running an experimental version of the HIRLAM NWP model over Zealand including the Copenhagen metropolitan area with a horizontal resolution of 1.4 km, thus approaching the city-scale. This involves 1-km resolution physiographic data with implications for the urban surface parameters, e.g. surface fluxes, roughness length and albedo. For the city of Copenhagen, the enhanced high-resolution NWP forecasting will be provided to demonstrate the improved dispersion forecasting capabilities of the Danish nuclear emergency preparedness decision–support system, the Accident Reporting and Guidance Operational System (ARGOS), used by the Danish Emergency Management Agency (DEMA).Recently, ARGOS has been extended with a capability of real-time calculation of regional-scale atmospheric dispersion of radioactive material from accidental releases. This is effectuated through on-line interfacing with the Danish Emergency Response Model of the Atmosphere (DERMA), which is run at DMI. For local-scale modelling of atmospheric dispersion, ARGOS utilises the Local-Scale Model Chain (LSMC), which makes use of high-resolution DMI-HIRLAM NWP model data provided to DEMA by DMI four times a day under operational surveillance and covering Denmark and surroundings. The integration of DERMA in ARGOS is effectuated through automated on-line digital communication and exchange of data. The calculations are carried out in parallel for each NWP model to which DMI has access, thereby providing a mini-ensemble of dispersion forecasts for the emergency management.     

重力作用下尘粒运动的数值解法

提出了重力作用下尘粒运动的数值解法。依据尘粒的重力、浮力及所受到的阻力推出了尘粒自然沉降时的运动方程。当尘粒的运动在斯托克斯领域内时采用理论公式求解其运动轨迹,当尘粒的运动进入非斯托克斯领域内时,提出了修正斯托克斯公式的方法以达到用理论公式来近似模拟该领域内的尘粒自然沉降运动     

Using the bootstrap and fast Fourier transform to estimate confidence intervals of 2D kernel densities

Kernel density estimators are often used to estimate the utilization distributions (UDs) of animals. Kernel UD estimates have a strong theoretical basis and perform well, but are usually reported without estimates of error or uncertainty. It is intuitively and theoretically appealing to estimate the sampling error in kernel UD estimates using bootstrapping. However, standard equations for kernel density estimates are complicated and computationally expensive. Bootstrapping requires computing hundreds or thousands of probability densities and is impractical when the number of observations, or the area of interest is large. We used the fast Fourier transform (FFT) and discrete convolution theorem to create a bootstrapping algorithm fast enough to run on commonly available desktop or laptop computers. Application of the FFT method to a large (n>20,000) set of radio telemetry data would provide a 99.6% reduction in computation time (i.e., 1.6 as opposed to 444 hours) for 1000 bootstrap UD estimates. Bootstrap error contours were computed using data from a radio-collared polar bear (Ursus maritimus) in the Beaufort Sea north of Alaska.