Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.     

Improvement of a Snow Albedo Parameterization in the Snow–Atmosphere–Soil Transfer Model:Evaluation of Impacts of Aerosol on Seasonal Snow Cover

The presence of light-absorbing aerosols(LAA) in snow profoundly influence the surface energy balance and water budget.However,most snow-process schemes in land-surface and climate models currently do not take this into consideration.To better represent the snow process and to evaluate the impacts of LAA on snow,this study presents an improved snow albedo parameterization in the Snow–Atmosphere–Soil Transfer(SAST) model,which includes the impacts of LAA on snow.Specifically,the Snow,Ice and Aerosol Radiation(SNICAR) model is incorporated into the SAST model with an LAA mass stratigraphy scheme.The new coupled model is validated against in-situ measurements at the Swamp Angel Study Plot(SASP),Colorado,USA.Results show that the snow albedo and snow depth are better reproduced than those in the original SAST,particularly during the period of snow ablation.Furthermore,the impacts of LAA on snow are estimated in the coupled model through case comparisons of the snowpack,with or without LAA.The LAA particles directly absorb extra solar radiation,which accelerates the growth rate of the snow grain size.Meanwhile,these larger snow particles favor more radiative absorption.The average total radiative forcing of the LAA at the SASP is 47.5Wm~(-2).This extra radiative absorption enhances the snowmelt rate.As a result,the peak runoff time and "snow all gone" day have shifted 18 and 19.5 days earlier,respectively,which could further impose substantial impacts on the hydrologic cycle and atmospheric processes.     

Observed particle sizes and fluxes of Aeolian sediment in the near surface layer during sand-dust storms in the Taklamakan Desert

Monitoring, modeling and predicting the formation and movement of dust storms across the global deserts has drawn great attention in recent decades. Nevertheless, the scarcity of real-time observations of the wind-driven emission, transport and deposition of dusts has severely impeded progress in this area. In this study, we report an observational analysis of sand-dust storm samples collected at seven vertical levels from an 80-m-high flux tower located in the hinterland of the great Taklamakan Desert for ten sand-dust storm events that occurred during 2008–2010. We analyzed the vertical distribution of sandstorm particle grain sizes and horizontal sand-dust sediment fluxes from the near surface up to 80 m high in this extremely harsh but highly representative environment. The results showed that the average sandstorm grain size was in the range of 70 to 85 μm. With the natural presence of sand dunes and valleys, the horizontal dust flux appeared to increase with height within the lower surface layer, but was almost invariant above 32 m. The average flux values varied within the range of 8 to 14 kg m^?2 and the vertical distribution was dominated by the wind speed in the boundary layer. The dominant dust particle size was PM₁₀₀ and below, which on average accounted for 60–80 % of the samples collected, with 0.9–2.5 % for PM_0–2.5, 3.5–7.0 % for PM_0–10, 5.0–14.0 % for PM_0–20 and 20.0–40.0 % for PM_0–50. The observations suggested that on average the sand-dust vertical flux potential is about 0.29 kg m^?2 from the top of the 80 m tower to the upper planetary boundary layer and free atmosphere through the transport of particles smaller than PM₂₀. Some of our results differed from previous measurements from other desert surfaces and laboratory wind-dust experiments, and therefore provide valuable observations to support further improvement of modeling of sandstorms across different natural environmental conditions.     

Sensitivity of Potential Evapotranspiration Estimation to the Thornthwaite and Penman–Monteith Methods in the Study of Global Drylands

Drylands are among those regions most sensitive to climate and environmental changes and human-induced perturbations.The most widely accepted definition of the term dryland is a ratio,called the Surface Wetness Index(SWI),of annual precipitation to potential evapotranspiration(PET)being below 0.65.PET is commonly estimated using the Thornthwaite(PET Th)and Penman–Monteith equations(PET PM).The present study compared spatiotemporal characteristics of global drylands based on the SWI with PET Th and PET PM.Results showed vast differences between PET Th and PET PM;however,the SWI derived from the two kinds of PET showed broadly similar characteristics in the interdecadal variability of global and continental drylands,except in North America,with high correlation coefficients ranging from 0.58 to 0.89.It was found that,during 1901–2014,global hyper-arid and semi-arid regions expanded,arid and dry sub-humid regions contracted,and drylands underwent interdecadal fluctuation.This was because precipitation variations made major contributions,whereas PET changes contributed to a much lesser degree.However,distinct differences in the interdecadal variability of semi-arid and dry sub-humid regions were found.This indicated that the influence of PET changes was comparable to that of precipitation variations in the global dry–wet transition zone.Additionally,the contribution of PET changes to the variations in global and continental drylands gradually enhanced with global warming,and the Thornthwaite method was found to be increasingly less applicable under climate change.     

新疆库尔勒市空气质量等级及首要污染物时空变化研究

城市空气质量与经济建设及人民生活息息相关,是政府和人民关注的重大社会问题。利用库尔勒市2014年9月1日-2015年8月31日州政府、棉纺厂、开发区三个环境监测站逐时观测资料,分析了库尔勒市空气质量等级和首要污染物的时空分布,并分析了污染天气发生的日变化特征。结果表明：（1）三站污染天气出现频率分别为49.4%、40.9%和34.7%,符合州政府＞棉纺厂＞开发区的分布规律。三站污染天气均是春季出现频率最高,分别占到44.7%,39.4%和48.5%,其次为秋季、冬季和夏季;（2）库尔勒市首要污染物中, PM10的频率最高,其次为PM2.5,CO、NO2、O3及SO2出现频率均低于10.0%。PM10作为首要污染物在春季出现频率最高,三站出现频率为33.9%-36.3%,PM2.5则是冬季出现频率最高,三站出现频率为59.1%-86.4%;（3）库尔勒市污染天气和重污染天气发生频率符合傍晚与夜间高,白天低的变化规律。污染天气发生频率最高值出现在0:00时,最低值出现在6:00-7:00时,重污染天时延迟到9:00时。上述结果表明库尔勒市春季周边自然污染源的排放与输送对该市的空气质量影响巨大。     

策勒沙漠-绿洲输沙及动力环境特征

利用2012年4月9日—5月9日策勒沙漠和绿洲内部测点的输沙及气象资料,分析了沙漠与绿洲内部沙尘传输的差异性,并从风动力环境上揭示了成因。结果表明:(1)观测期间,通过沙漠测点100 cm(宽)×200 cm(高)断面的沙尘总量为117.5 kg,通过绿洲内部棉田相同断面的沙尘总量为15.1 kg,比沙漠测点减少了87.1%;(2)过渡带和防护林带对风速的消减作用明显,观测期间,沙漠测点起沙风的持续时间为97.4 h,棉田测点仅为18.9 h;(3)观测期间,沙漠测点的总输沙势为114.2 VU,合成输沙势为72.8 VU,合成输沙方向为65.5°;棉田测点的总输沙势为16.1 VU,合成输沙势为15.1 VU,合成输沙方向为104.5°。荒漠过渡带和绿洲防护林通过降低绿洲内部的风速,改变动力环境,削弱沙尘在绿洲区的传输。     

基于改进的累积气旋能量指数评估西北太平洋TC活动与ENSO的关系

采用1977—2008年日本气象厅(JMA)TC最佳路径集资料和美国气候预测中心(CPC)的ENSO资料,设计了一个考虑热带气旋(TC)尺度信息的累积气旋能量指数(SACE),据此分析了西北太平洋TC活动与ENSO之间的关系。结果表明:相比于不考虑尺度效应的ACE,引入TC尺度信息的SACE增加了较大强度、较大尺度TC活动水平的权重,能够更加准确地刻画TC强度及其影响,其与Ni1o指数的相关性进一步增强。SACE能够更好地表征西北太平洋TC活动与ENSO年际变化的关系,其对ENSO位相的依赖性相对于ACE更为敏感,是一个能更好预测ENSO状态的因子。TC强度、生命史、频数以及尺度均对SACE有一定的贡献,但生命史的贡献最大,这一方面说明ENSO主要通过调制TC生命史来影响SACE,另一方面也说明相对于强度、频数和尺度而言,TC生命史更能影响SACE中的ENSO信号。     

乌鲁木齐市边界层日变化特征

利用乌鲁木齐市晴天CFL-03型风廓线雷达观测资料,分析了边界层日变化特征。得出结论如下:边界层结构季节变化明显。冬、春季300~600m以下风速较小,小于3m/s,且愈近地面风速愈小;以上风速大、风向恒定,基本为东南大风。夏季和秋季风速比冬季和春季小,流场特征较复杂,水平风速和风向变化较活跃,存在明显的风切变。折射率结构常数春、秋和冬季比夏季分别小1个、3个和1~3个量级;夏季最大,集中在10~(-16)~10~(-13) m~(-2/3)之间。春、夏和秋季晴天湍流动能耗散率量级分别在10~(-6)~10~(-2) m~2·s~(-3)、10~(-4)~10~(-3) m~2·s~(-3)、10~(-6)~10~(-3) m~2·s~(-3)之间;白天比夜间约大1个量级。晴天折射率结构常数和湍流动能耗散率日变化特征与风场日变化特征有较好地对应关系,即湍流发展旺盛的区域与风速较大的区域相一致。风廓线雷达资料反演的湍流动能耗散率对春季和夏季边界层结构日变化演变特征的监测较好。夏季夜间稳定边界层约400~500m,残余层可达到约1800m,对流边界层可发展到约2500m,混合层约2200m,夹卷层约300~400m。     

北半球中高纬度低频振荡对2012/2013年冬季中国东北极端低温事件的影响

利用NCEP/DOE的逐日再分析资料和国家气象信息中心的常规观测站温度资料,首先分析了2012/2013年冬季中国东北区域极端低温事件过程中区域平均温度的低频振荡变化特征,然后分析了北半球中高纬度对流低层和中层低频环流系统配置的变化及其与东北地区强冷空气活动过程的联系,最后进一步研究了中高纬度低频环流系统的传播特征及其对温度变化的影响。主要结果有:（1）2012/2013年冬季东北区域平均温度存在很强的30～60 d的周期振荡特征,同时伴随较强的10～30 d低频振荡,后者与实际降温过程对应关系更好;（2）对10～30 d的低频振荡而言,在东北地区低频温度变化降低最大的位相7（位相3升高最大）,500 hPa上,我国东部地区正好处于贝加尔湖地区的低频高压（低压）环流和日本海的低频低压（高压）环流型之间的低频偏北（偏南）的较强引导气流中;同时在850 hPa上,我国东部从东北到南海都是较强的偏北（偏南）低频风控制,这使得东亚冬季风环流系统加强（被抑制）,东北区域则经历一次大幅度的低频温度降低（升高）过程,这些高低空低频环流系统的配置和演变导致了2012/2013年冬季一次次强（或较强）的冷空气沿偏东偏北的路径影响我国东北地区,并导致极端低温事件的出现;（3）沿着乌拉尔山-贝加尔湖-我国东北地区-西北太平洋传播的500 hPa低频波列,是驱动2012/2013年冬季东亚冬季风低频振荡和我国东北地区极端低温事件的环流系统。