回归诊断在城市空气质量预报中的应用研究

城市空气质量回归预报模型的残差分布存在着不对称现象，它是由高杠杆点引起。这些高杠杆试验点的残差存在着统计天气预报意义上的不合理性，导致回归系数L5估计的误差，从而引起预报的误差。针对这些问题提出了城市空气质量的回归诊断预报模型。实例计算说明，回归诊断预报模型要优于常规回归预报模型。进一步分析指出，城市空气质量回归预报模型的不合理性并非个别例子的特殊性所造成，而是由模型的数学特点所决定，因此城市空气质量的回归诊断预报模型具有普遍     

珠三角城市环境对对流降水影响的模拟研究

采用具有2 km分辨率的中尺度气象模式WRF及其耦合的单层城市冠层模式,以及Thompson云微物理方案,针对广州市附近发生的一次对流风暴过程,模拟研究了城市环境包括城市地表性质变化、城市空气污染可能引起的云粒子浓度增大现象对对流降水发展的影响问题.结果表明城市地表引起的热岛和干岛效应,可造成城市边界层高度升高,有利于城区附近辐合流场形成和不稳定能量增大.模拟结果显示,城市地表作用可在广州市南北各形成一个对流有效位能CAPE增大的辐合区,模拟对流降水回波起始发展于这些具有高不稳定能量的辐合区,并与观测雷达回波特征相一致,反映出城市地表对对流的起始发展及其发生位置有更直接作用.对流发展起来后,敏感试验反映出高云粒子浓度 (污染) 情形中有更多降水形成,降水增多可达20%以上.诊断分析发现降水增多与对流云中有更多雨水及过冷却云水形成有关系.增多的云水雨水通过相应的由于潜热释放增加引起的强上升运动被传送到较高层次,引起云中冻结过程及液态水和冰相物质之间的相互作用增强,从而导致更多冰相物质形成、降落地面降水增多.     

郑州城市降水与湿度特征

观测资料分析结果表明：郑州城市降水量比郊区略多；城市湿度比郊区小，存在着城市“干岛现象”。     

减缓城市"热岛效应" 调节城市生态系统

对城市"热岛效应"现象的形成及其对城市生态系统的影响进行初步探讨,得出结论(1)城市的发展加强了"热岛效应".(2)城市"热岛效应"对城市生态系统产生不良影响,使系统自我调节功能下降.(3)合理规划城市布局,发展城市绿化,可有效减缓"热岛效应",改良城市生态系统.     

对新疆气候与生态变化的三点认识

1新疆气候变暖这些年一些气象学者认为气候在变暖。新疆的一些研究也发现这里的温度在上升。然而在小气候研究中人们普遍承认了城市扩大所导致的城市热岛效应。于是我就提出一个问题:在所谓的气候变暖的数据中,有多少是由于城市热岛效应引起的?城市的热岛效应在什么时候比较明显呢?它应当在空气比较稳定、风速比较小的时候较为明显。对于新疆,在早晨和冬季经常出现逆温层。逆温层中风速小,空气稳定,所以城市的热岛效应应当在早晨和冬季表现明显一些。或者说随着城市的扩大,城市地区的气象站的百叶箱温度应当在早晨和冬季有升高的现象。1980…     

城市内涝的原因与预防

对产生城市内涝的原因进行了分析,并提出了几点预防措施或建议,以减少或避免城市内涝现象发生,减轻或避免内涝给人们的出行甚至生命财产带来影响和危害.     

数字赋能与城市碳排放——基于下一代互联网示范城市的准自然试验

研究基于2009—2017年中国268个地级市面板数据,将下一代互联网示范城市政策视为准自然实验,利用双重差分模型（DID）实证检验了下一代互联网示范城市政策对城市碳排放的影响。结果表明：下一代互联网示范城市政策对城市碳排放具有缓解作用,可使城市碳排放减少1.41%,该结论在经过稳健性检验后依然成立;在机制分析中得出,下一代互联网示范城市政策主要通过产业结构优化和绿色技术创新的方式缓解城市碳排放;进一步异质性检验表明,下一代互联网示范城市的建设对大型城市和高碳排放量城市的减排效果更加显著,对中小型城市则表现出加剧碳排放的现象。     

城市的大气构造、环境污染和人类健康

利用城市区域大气空间分布和大气污染以及健康状况资料,分析了城市区域风的状况和大气污染的时间变化、地域变化及其对城市人们健康的影响,得出目前城市区域不断扩大,所引起的大气空间结构改变,导致了大气环境的不断恶化已对生活在其中的人们产生了不可忽视的影响。结果可为城市区域的医疗保健、污染防治提供参考。       

减缓城市“热岛效应” 调节城市生态系统

对城市“热岛效应”现象的形成及其对城市生态系统的影响进行初步探讨，得出结论：(1)城市的发展加强了“热岛效应”。(2)城市“热岛效应”对城市生态系统产生不良影响，使系统自我词节功能下降。(3)合理规划城市布局，发展城市绿化，可有效减缓“热岛效应”，改良城市生态系统。     

城市雨水利用在区域规划环评中的可行性分析

我国目前正处于城市化快速发展阶段,随之而来的城市雨水资源的大量流失和污染严重的雨水径流及由此引起的城市洪灾和生态环境破坏等问题引起了人们的高度关注。对开发区雨水利用可行性分析结果表明:开发区雨水收集量可占总用水量的2.5%—8.0%,可节约用水费用12%—20%;与污水回用相比,在污水量小于5 000 m3/d时,采用雨水收集利用更加经济可行。因此,提出在区域规划层次上,应提高对城市雨水利用对策的重视,充分发挥区域规划环境影响评价宏观控制、协调的作用。     

雷暴云起电G模型及其应用

1引言目前,对云内粒子荷电的成因机制和分布还没有公认的理论。对雷电的成因有以下几种假说[1]。感应起电是最早提出的雷暴起电理论之一。该理论认为,在电场强度方向朝下的晴天电场中,云质粒和降水质粒(固态、液态)都会被极化,使粒子上半部带负电,下半部带正电。这一效应可引起不同的起电过程。     

浅谈人工影响天气作业安全文化

1引言人工影响天气(以下简称人影,但专有名称除外)是指为避免或者减轻气象灾害,合理利用气候资源,在适当条件下通过科技手段对局部大气的物理过程进行人为影响,实现增雨(雪)、防雹、防霜、消雨、消雾等目的的活动[1]。人影工作是气象防灾减灾、服务地方经济建设和社会发展的重要科技手段之一。人影地面作业使用高炮、火箭,从地基对空中云体发射炮弹、火箭弹实施催化影响,如因作业装备质量瑕疵或操作不慎、储运不当等,出现作业安全事故就会危及空中飞行器、地面人员和财产的安全;使用飞机在空中对云体实施催化影响,如稍有疏忽就会发生安全事故。     

Atmospheric boundary layer research at Cabauw

At Cabauw, The Netherlands, a 213 m high mast specifically built for meteorological research has been operational since 1973. Its site, construction, instrumentation and observation programs are reviewed. Regarding analysis of the boundary layer at Cabauw, the following subjects are discussed:

- terrain roughness;

- Monin-Obukhov theory in practice;

- the structure of stable boundary layers;

- observed evolution of fog layers;

- inversion rise and early morning entrainment;

- use of the geostrophic wind as a predictor for wind profiles;

- height variation of wind climate statistics;

- air pollution applications: long range transport and short range dispersion;

- dependence of sound wave propagation on boundary-layer structure;

- testing of weather and climate models.

     

大气气溶胶吸收系数的测量

本文讨论了大气气溶胶吸收系数的测量,并介绍了我系根据毛玻璃屏积分法设计的测量系统。根据在北京中关村地区取样观测的结果,在采暖期,气溶胶吸收系数变化于10~(-3)—10~(-4)m~(-1)之间,而在非采暖期,其值约为10~(-4)m~(-1)量级。若利用当量碳的概念,则在采暖期当量碳浓度占气溶胶总浓度的50—60％,而在非采暖期,其比例为30—37％。     

Modelling evaporation from wetland tundra

Evapotranspiration is a major component of both the energy and water balances of wetland tundra environments during the thaw season. Reliable estimates of evapotranspiration are required in the analysis of climatological and hydrological processes occurring within a wetland and in interfacing the surface climate with atmospheric processes. Where direct measurements are unavailable, models designed to accurately predict evapotranspiration for a particular wetland are used.This paper evaluates the performance, sensitivity and limitations of three physically-based, one-dimensional models in the simulation of evaporation from a wetland sedge tundra in the Hudson Bay Lowland near Churchill, Manitoba. The surface of the study site consists of near-saturated peat soil with a sparse sedge canopy and a constantly varying coverage of standing water. Measured evaporation used the Bowen ratio energy balance approach, to which the model results were compared. The comparisons were conducted with hourly and daily simulations.The three models are the Penman-Monteith model, the Shuttleworth-Wallace sparse canopy model and a modified Penman-Monteith model which is weighted for surface area of the evaporation sources.Results from the study suggest that the weighted Penman-Monteith model has the highest potential for use as a predictive tool. In all three cases, the importance of accurately measuring the surface area of each evaporation source is recognized. The difficulty in determining a representative surface resistance for each source and the associated problems in modelling without it are discussed.

List of Symbols

Models BREB Bowen ratio energy balance - P-M Penman-Monteith combination - S-W Shuttleworth-Wallace combination - W-P-M Weighted Penman-Monteith combination Other AE Available energy-all surfaces - AE _c Available energy-canopy (S-W, W-P-M) - AE _s Available energy-bare soil (S-W, W-P-M) - AE _w Available energy-open water (W-P-M) - C _p Specific heat of air - D Vapor pressure deficit - DAI Dead area index - FAI Foliage area index - LAI Leaf area index - Q ^* Net radiation - Q _e Latent heat flux-total - Q _ec Latent heat flux-canopy (S-W, W-P-M) - Q _es Latent heat flux-bare soil (S-W, W-P-M) - Q _ew Latent heat flux-open water (W-P-M) - Q _g ground heat flux - Q _h Sensible heat flux - S Proportion of area in bare soil - W Proportion of surface in open water - r _a Aerodynamic resistance (P-M, W-P-M) - r _c Canopy resistance - r _s Generalized optimized surface resistance - r _st Stomatal resistance - r _c ^a Bulk boundary layer resistance (S-W) - r _s ^a Aerodynamic resistance below mean canopy level (S-W) - r _s ^s Soil surface resistance (S-W, W-P-M) Greek Bowen ratio - Psychrometer constant - Air density - Slope of saturation vapour pressure vs temperature curve     

A global model of changing N2O emissions from natural and perturbed soils

A high resolution global model of the terrestrial biosphere is developed to estimate changes in nitrous oxide (N₂O) emissions from 1860–1990. The model is driven by four anthropogenic perturbations, including land use change and nitrogen inputs from fertilizer, livestock manure, and atmospheric deposition of fossil fuel NO _x . Global soil nitrogen mineralization, volatilization, and leaching fluxes are estimated by the model and converted to N₂O emissions based on broad assumptions about their associated N₂O yields. From 1860–1990, global N₂O emissions associated with soil nitrogen mineralization are estimated to have decreased slightly from 5.9 to 5.7 Tg N/yr, due mainly to land clearing, while N₂O emissions associated with volatilization and leaching of excess mineral nitrogen are estimated to have increased sharply from 0.45 to 3.3 Tg N/yr, due to all four anthropogenic perturbations. Taking into account the impact of each perturbation on soil nitrogen mineralization and on volatilization and leaching of excess mineral nitrogen, global 1990 N₂O emissions of 1.4, 0.7, 0.4 and 0.08 Tg N/yr are attributed to fertilizer, livestock manure, land clearing and atmospheric deposition of fossil fuel NO _x , respectively. Consideration of both the short and long-term fates of fertilizer nitrogen indicates that the N₂O/fertilizer-N yield may be 2% or more.C. NBM Definitions AET _mon (cm H₂O) = monthly actual evapotranspiration - AET _ann (cm H₂O) = annual actual evapotranspiration - age _h (years) = stand age of herbaceous biomass - age _w (years) = stand age of woody biomass - atmblc (gC/m²/month) = net flux of CO₂ from grid - biotoc (gC/g biomass) = 0.50 = convert g biomass to g C - beff _h = 0.8 = fraction of cleared herbaceous litter that is burned - beff _w = 0.4 = fraction of cleared woody litter that is burned - bfmin = 0.5 = fraction of burned N litter that is mineralized or converted to reactive gases which rapidly redeposit. Remainder assumed pyrodenitrified to N₂. + N₂O - bprob = probability that burned litter will be burned - burn _h (gC/m²/month) = herbaceous litter burned after land clearing - burn _w (gC/m²/month) = woody litter burned after land clearing - cbiomsh (gC/m²) = C herbaceous biomass pool - cbiomsw (gC/m²) = C woody biomass pool - clear (gC/m²/month) = woody litter C removed by land clearing - clearn (gN/m²/month) = woody litter N removed by land clearing - cldh (month^–1) = herbaceous litter decomposition coefficient - cldw (month^–1) = woody litter decomposition coefficient - clittrh (gC/m²) = C herbaceous litter pool - clittrw (gC/m²) = C woody litter pool - clph (month^–1) = herbaceous litter production coefficient - clpw (month^–1) = woody litter production coefficient - cnrath (gC/gN) = C/N ratio in herbaceous phytomass - cnrats (gC/gN) = C/N ratio in soil organic matter - cnratt (gC/gN) = average C/N ratio in total phytomass - cnratw (gC/gN) = C/N ratio in woody phytomass - crod (month^–1) = forest clearing coefficient - csocd (month^–1) = actual soil organic matter decompostion coefficient - decmult decomposition coefficient multiplier; natural =1.0; agricultural =1.0 (1.2 in sensitivity test) - fertmin (gN/m²/month) = inorganic fertilizer input - fleach fraction of excess inorganic N that is leached - fligh (g Lignin/ g C) = lignin fraction of herbaceous litter C - fligw (g Lignin/ g C) = 0.3 = lignin fraction of woody litter C - fln2o = .01–.02 = fraction of leached N emitted as N₂O - fnav = 0.95 = fraction of mineral N available to plants - fosdep (gN/m²/month) = wet and dry atmospheric deposition of fossil fuel NO _x - fresph = 0.5 = fraction of herbaceous litter decomposition that goes to CO₂ respiration - fresps = 0.51 + .068 ^* sand = fraction of soil organic matter decomposition that goes to CO₂ respiration - frespw = 0.3 ^* (^* see comments in Section 2.3 under decomposition) = fraction of woody litter decomposition that goes to CO₂ respiration - fsoil = ratio of NPP measured on given FAO soil type to NPF_miami - fstruct = 0.15 + 0.018 ^* ligton = fraction of herbaceous litter going to structural/woody pool - fvn2o = .05–.10 = fraction of excess volatilized mineral N emitted as N₂O - fvol = .02 = fraction of gross mineralization flux and excess mineral N volatilized - fyield ratio of total agricultural NPP in a given country in 1980 to total NPP_miami of all displaced natural grids in that country - gimmob _h (gN/m²/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of herbaceous litter - gimmob _s (gN/m²/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of soil organic matter - gimmob _w (gN/m²/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of woody litter - graze (gC/m²/month) = C herbaceous biomass grazed by livestock - grazen (gN/m²/month) = N herbaceous biomass grazed by livestock - growth _h (gC/m²/month) = herbaceous litter incorporated into microbial biomass - growth _w (gC/m²/month) = woody litter incorporated into microbial biomass - gromin _h (gN/m²/month) = gross N mineralization due to decomposition and burning of herbaceous litter - gromin _s (gN/m²/month) = gross N mineralization due to decomposition of soil organic matter - gromin _w (gN/m²/month) = gross N mineralization due to decomposition and burning of woody litter - herb herbaceous fraction by weight of total biomass - leach (gN/m²/month) = leaching (& volatilization) losses of excess inorganic N - ligton (g lignin-C/gN) = lignin/N ratio in fresh herbaceous litter - LP _h (gC/m²/month)= C herbaceous litter production - LP (gC/m²/month) = C woody litter production - LPN _h (gN/m²/month) = N herbaceous litter production - LPN _W (gN/m²/month) = N woody litter production - manco2 (gC/m²/month) = grazed C respired by livestock - manlit (gC/m²/month) = C manure input (feces + urine) - n2oint (gN/m²/month) = intercept of N₂O flux vs gromin regression - n2oleach (gN/m²/month) = N₂O flux associated with leaching and volatilization of excess inorganic N - n2onat (gN/m²/month) = natural N₂O flux from soils - n2oslope slope of N₂O flux vs gromin regression - nbiomsh (gN/m²) = N herbaceous biomass pool - nbiomsw (gN/m²) = N woody biomass pool - nfix (gN/m²/month) = N₂ fixation + natural atmospheric deposition - nlittrh (gN/m²) = N herbaceous litter pool - nlittrw (gN/m²) = N woody litter pool - nmanlit (gN/m²/month) = organic N manure input (feces) - nmanmin (gN/m²/month) = inorganic N manure input (urine) - nmin (gN/m²) = inorganic N pool - NPP _acth (gC/m²/month)= actual herbaceous net primary productivity - NPP _actw (gC/m²/month) = actual woody net primary productivity - nvol (gN/m²/month) = volatilization losses from inorganic N pool - plntnav (gN/m²/month)= mineral N available to plants - plntup _h (gN/m²/month) = inorganic N incorporated into herbaceous biomass - plntup _w (gN/m²/month) = inorganic N incorporated into woody biomass - precip _ann (mm) = mean annual precipitation - precip _mon (mm) = mean monthly precipitation - pyroden _h (gN/m²/month) = burned herbaceous litter N that is pyrodenitrified to N₂ - pyroden _w (gN/m²/month) = burned woody litter N that is pyrodenitrified to N₂ - recyc fraction of N that is retranslocated before senescence - resp _h (gC/m²/month) = herbaceous litter CO₂ respiration - resp _s (gC/m²/month) = soil organic carbon CO₂ respiration - resp _w (gC/m²/month) = woody litter CO₂ respiration - sand sand fraction of soil - satrat ratio of maximum NPP to N-limited NPP - soiloc (gC/m²) = soil organic C pool - soilon (gN/m²) = soil organic N pool - temp _ann (°C) = mean annual temperature - temp _mon (°C) = mean monthly temperature Now at the NOAA Aeronomy Laboratory, Boulder, Colorado.     

Numerical studies of heat island circulations

A two dimensional model has been set up to investigate the circulation induced by an urban heat island in the absence of synoptic winds. The boundary conditions need to be formulated carefully and due to difficulties arising here, we restrict our attention to cases of initially stable thermal stratification. Heat island circulations are allowed to develop from rest and prior to the appearance of the final symmetric double cell pattern, a transitional multi-cell pattern is observed in some cases. The influence on the steady state circulation of various parameters is studied, among which are eddy transfer coefficients, the heat island intensity, the initial temperature stratification and the heat island size. Some results are presented for a case in which differential surface cooling beneath an initially stable atmosphere produces a circulation and an unstable layer capped by an elevated inversion over the city. It is hoped that this case is vaguely representative of the night-time heat island with no geostrophic wind.Notation c_p Specific heat at constant pressure - g Acceleration due to gravity - H Top of integration region - K_z Vertical eddy transfer coefficient - K_x, K_xH, K_xm Horizontal eddy transfer coefficients for heat and momentum - l ixing length - p Pressure - p₀ Reference surface pressure (1000 mb) - P_H (x, t) Pressure at z = H - R Specific gas constant for dry air - t Time - u, w Horizontal and vertical velocities - x, z Horizontal and vertical coordinates - x₁, x₂ Positions of discontinuities in surface temperature field (see Figure 2) - x_a Heat island half-width - x_b Boundary of integration region - Parameter in formula for eddy coefficients (variable-K case) = 18.0 - _s Intensity of heat island - Potential temperature field - Reference absolute temperature (variable-K case) - _r Reference temperature (° C) - _s Surface temperature - Q Air density     

Sulfur dioxide plume dispersion and subsequent absorption by coniferous forests: Field measurements and model calculations

A Forest SO₂ Absorption Model (ForSAM) was developed to simulate (1) SO₂ plume dispersion from an emission source, (2) subsequent SO₂ absorption by coniferous forests growing downwind from the source. There are three modules: (1) a buoyancy module, (2) a dispersion module, and (3) a foliar absorption module. These modules were used to calculate hourly abovecanopy SO₂ concentrations and in-canopy deposition velocities, as well as daily amounts of SO₂ absorbed by the forest canopy for downwind distances to 42 km. Model performance testing was done with meteorological data (including ambient SO₂ concentrations) collected at various locations downwind from a coal-burning power generator at Grand Lake in central New Brunswick, Canada. Annual SO₂ emissions from this facility amounted to about 30,000 tonnes. Calculated SO₂ concentrations were similar to those obtained in the field. Calculated SO₂ deposition velocities generally agreed with published values.Notation c air parcel cooling parameter (non-dimensional) - E foliar absorption quotient (non-dimensional) - f areal fraction of foliage free from water (non-dimensional) - f _w SO₂ content of air parcel - h height of the surface layer (m) - H height of the convective mixing layer (m) - H _stack stack height (m) - k time level - k _drag coefficient of drag on the air parcel (non-dimensional) - K _z eddy viscosity coefficient for SO₂ (m²·s^–1) - L Monin-Obukhov length scale (m) - L _A single-sided leaf area index (LAI) - n degree-of-sky cloudiness (non-dimensional) - N number of parcels released with every puff (non-dimensional) - PAR photosynthetically active radiation (W m^–2) - Q emission rate (kg s^–2) - r _b diffusive boundary-layer resistance (s m^–1) - r _c canopy resistance (s m^–1) - r _cuticle cuticular resistance (s m^–1) - r _m mesophyllic resistance (s m^–1) - r _s stomatal resistance (s m^–1) - r _exit smokestack exit radius (m) - R normally distributed random variable with mean of zero and variance of t (s) - u _* frictional velocity scale, (m s^–1) - v lateral wind vector (m s^–1) - v _d SO₂ dry deposition velocity (m s^–1) - VCD water vapour deficit (mb) - z _can mean tree height (m) - Z zenith position of the sun (deg) - environmental lapse rate (°C m^–1) - dry adiabatic lapse rate (0.00986°C m^–1) - von Kármán's constant (0.04) - _B vertical velocities initiated by buoyancy (m s^–1) - canopy extinction coefficient (non-dimensional) - ()a denotes ambient conditions - ()can denotes conditions at the top of the forest canopy - ()h denotes conditions at the top of the surface layer - ()H denotes conditions at the top of the mixed layer - ()s denotes conditions at the canopy surface - ()p denotes conditions of the air parcels     

The impact of geophysical parameters on longwave radiation budget at the top and base of the atmosphere

Summary The effect of clouds on longwave radiation budget at the top and base of the atmosphere is studied by using the HIRS2/MSU-retrieved temperature and humidity fields, and cloud fields and the International Satellite Cloud Climatology Project-produced fields. Detailed studies are carried out at four selected sites: one at Equatorial Eastern Pacific (ITCZ) area, one at Libyan Desert (Libya), one at Ottawa, Montreal (Ottawa), and one at central Europe (Europe). The monthly mean differences in outgoing longwave radiation (OLR) (the ISCCP-based OLR minus the HIRS2-based OLR), ranging from –2.8 Wm^–2 at ITCZ to –15.4 Wm^–2 at Ottawa, are less than the monthly mean differences in surface downward flux, ranging from –2.7 Wm^–2 at Libya to 40.6 Wm^–2 at the ITCZ. The large differences in surface downward flux are mainly due to large differences in cloud amount and moisture in the low levels of the atmosphere.Monthly mean OLR and surface downward flux can be derived either (1) from instantaneous temperature, humidity, and cloud fields over a month period or (2) from monthly mean temperature, humidity, and cloud fields. The monthly mean OLR and surface downward flux derived from the first approach is compared with the second. The differences in OLR are small, ranging from –0.05 Wm^–2 to 6.2 Wm^–2, and the differences in surface downward flux is also small, ranging from 0.4 Wm^–2 to 6.4 Wm^–2.List of Acronyms AVHRR Advanced Very High Resolution radiometer - ERB Earth Radiation Budget - ERBE Earth Radiation Budget Experiment - FGGE First Global GARP Experiment - GARP Global Atmospheric Research Program - GCM General Circulation Model - GISS Goddard Institute for Space Studies - GLA Goddard Laboratory for Atmospheres - GMS Geostationary Meteorological Satellite - GOES Geostationary Operational Environmental Satellite - HIRS2 High Resolution Infrared Radiation Sounder/2 - ISCCP International Satellite Cloud Climatology Project - IR Infrared - MSU Microwave Sounding Unit - NFOV Narrow Field of View - NOAA National Oceanic and Atmospheric Administration - NESDIS National Environmental Satellite Data Information Service - TOVS TIROS Operational Vertical Sounder With 4 Figures     

MEASUREMENTS OF THE ABSORPTION COEFFICIENT OF ATMOSPHERIC AEROSOLS

This paper discusses the measurement of the absorption coefficient of atmospheric aerosols and its measuring system based on the principle of integrating plate. Measurements in Beijing show that the absorption coefficient of atmospheric aerosols in the heating period varies in a range of 10^-3 to 10^-4 m^-1 and in the non-heating period, its values are near 10^-4m^-1.