Impact of chemical and meteorological boundary and initial conditions on air quality modeling: WRF-Chem sensitivity evaluation for a European domain

This study evaluates the impact of different chemical and meteorological boundary and initial conditions on the state-of-the-art Weather Research and Forecasting (WRF) model with its chemistry extension (WRF-Chem). The evaluation is done for July 2005 with 50 km horizontal resolution. The effect of monthly mean chemical boundary conditions derived from the chemical transport model LMDZ-INCA on WRF-Chem is evaluated against the effect of the preset idealized profiles. Likewise, the impact of different meteorological initial and boundary conditions (GFS and Reanalysis II) on the model is evaluated. Pearson correlation coefficient between these different runs range from 0.96 to 1.00. Exceptions exists for chemical boundary conditions on ozone and for meteorological boundary conditions on PM₁₀, where coefficients of 0.90 were obtained. Best results were achieved with boundary and initial conditions from LMDZ-INCA and GFS. Overall, the European simulations show encouraging results for observed air pollutant, with ozone being the most and PM₁₀ being the least satisfying.     

Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar,India

Ambient air quality in respect of SO₂, NO₂ and total suspended particulate matter (TSPM) was monitored at Pantnagar, India from May, 2008 to April, 2009 and statistically analyzed with meteorological variables such as relative humidity (RH), wind speed (WS), precipitation (P) and mean air temperature (T). TSPM was found to be the major air pollutant causing significant deterioration of air quality with annual mean concentrations of 280 μg/m³. Further, weekly mean air pollutant concentrations were statistically analyzed through stepwise multiple linear regression analysis in respect of independent meteorological variables to develop suitable statistical models. Both NO₂ and TSPM concentrations were found to have been influenced by meteorological variables with coefficient of determination (R²) of 82.21 and 92.84%, respectively. However, atmospheric SO₂ revealed only 22.87% of dependencies on meteorological variables. Partial correlation coefficients revealed that wind speed has the maximum influence (77.80 and 31.50%) on proposed equations for NO₂ and SO₂, closely followed by weekly mean temperature (73.60 and 24.30%). However, in case of TSPM, individual contribution of ambient temperature (94.40%) was found maximum, followed by relative humidity (86.50%). Model performances were evaluated through both quantitative data analysis techniques and statistical methods. Nearly 98 and 95% of potential error has been explained by the model developed for TSPM and NO₂, while in case of SO₂, it is found as only 61%. Therefore, performances of models (for TSPM and NO₂) to predict ambient weekly mean concentrations based on forecasted weather parameters were found to be excellent, however, performance of model developed for SO₂ was found only satisfactory.     

Predictability of the Meteorological Conditions Favourable to Radiative Fog Formation During the 2011 ParisFog Campaign

Radiative fog formation is a complex phenomenon involving local physical and microphysical processes that take place when particular meteorological conditions occur. This study aims at quantifying the ability of a regional numerical weather model to analyze and forecast the conditions favourable to radiative fog formation at an instrumental site in the Paris area. Data from the ParisFog campaign have been used in order to quantify the meteorological conditions favorable to radiative fog formation (pre-fog conditions) by setting threshold values on the key meteorological variables driving this process: 2-m temperature tendency, 10-m wind speed, 2-m relative humidity and net infrared flux. Data from the ParisFog observation periods of November 2011 indicate that use of these thresholds leads to the detection of 87 % of cases in which radiative fog formation was observed. In order to evaluate the ability of a regional weather model to reproduce adequately these conditions, the same thresholds are applied to meteorological model fields in both analysis and forecast mode. It is shown that, with this simple methodology, the model detects 74 % of the meteorological conditions finally leading to observed radiative fog, and 48 % 2 days in advance. Finally, sensitivity tests are conducted in order to evaluate the impact of using larger time or space windows on the forecasting skills.     

Denitrification through PSC formation: A 1-D model incorporating temperature oscillations

A one-dimensional model of polar stratospheric cloud (PSC) formation and evolution during the polar winter, incorporating both HNO₃ and H₂O condensation, has been developed to investigate the interactions between Type I and Type II PSCs and the effects of these clouds on the stratospheric composition. Model simulations for various meteorological conditions and the results of extensive sensitivity tests are presented. Temperature oscillations, which have been included in the model, are shown to have an important influence on the characteristics and effects of the PSCs. The predicted proportions of the PSCs are consistent with observations of number, size, and optical effects, such as depolarization. Denitrification of stratospheric air by 35–88% is shown to occur in the presence of both Type I and Type II PSCs, with comparable nitrate removal in both types of clouds. Dehydration by Type II clouds simultaneously removes similar percentages of water vapour, up to 79% at lower altitudes. Altough dehydration is insensitive to most of the parameter variations except the minimum temperature, the process of denitrification, especially the proportion removed by Type I PSCs, is highly variable.     

Stabilizing CO2 concentrations with incomplete international cooperation

Many stabilization scenarios have examined the implications of stabilization on the assumption that all regions and all sectors of all of the world's economies undertake emissions mitigations wherever and whenever it is cheapest to do so. This idealized assumption is just one of many ways in which emissions mitigation actions could play out globally, but not necessarily the most likely. This paper explores the implications of generic policy regimes that lead to stabilization of CO₂ concentrations under conditions in which non-Annex I regions delay emissions reductions and in which carbon prices vary across participating regions. The resulting stabilization scenarios are contrasted with the idealized results. Delays in the date by which non-Annex I regions begin to reduce emissions raise the price of carbon in Annex I regions relative to the price of carbon in Annex I in an idealized regime for any given CO₂ concentration limit. This effect increases the longer the delay in non-Annex I accession, the lower the non-Annex I carbon prices relative to the Annex I prices, and the more stringent the stabilization level. The effect of delay is very pronounced when CO₂ concentrations are stabilized at 450 ppmv, however the effect is much less pronounced at 550 ppmv and above. For long delays in non-Annex I accession, 450 ppmv stabilization levels become infeasible.     

南京大气能见度变化规律及影响因子分析

利用累积百分率法、Ridit中值分析法、"非常好"能见度出现频率法以及平均能见度年际和季节变化法,对1980—2005年南京大气能见度年际变化趋势进行分析,发现1980—1984年能见度呈上升趋势,1985年以后则在波动中呈明显下降趋势。26 a中,日均大气能见度最小值为0.55 km,最大值为29.25 km,平均值为8.59 km。大气能见度具有明显的日变化和季节变化特征,一日之中,14时最好,08时最差;一年之中,冬季能见度最低,夏季最高。能见度与相对湿度呈负相关,与风速呈正相关,与温度和气压的相关性相对较小。PM10是影响南京地区大气能见度的首要污染物,通过对能见度与PM10平均质量浓度进行曲线拟合发现,二者呈负相关,复相关系数在秋季最高,夏季最低。由统计预报方程可知,空气污染和气象条件协同作用对能见度的影响在春季、秋季、冬季较为明显,夏季则相对较差。     

Atmospheric profiling with the UAS SUMO: a new perspective for the evaluation of fine-scale atmospheric models

For the first time, unmanned aerial system measurements collected by the small unmanned meteorological observer (SUMO) are used to evaluate atmospheric boundary layer (ABL) parameterization schemes embedded in the Advanced Weather Research and Forecasting model (AR-WRF). Observation sites were located in the vicinity of the almost idealized shaped mountain Hofsj?kull, Central Iceland. SUMO profiles provided temperature, relative humidity and wind data to maximum heights of 3?km above ground. Two cases are investigated, one with calm wind conditions and development of a convective ABL and one with moderate winds and gravity waves over Hofsj?kull. For the high-resolution simulation with AR-WRF, three two-way nested domains are chosen with a grid size of 9, 3 and 1?km. During its first meteorological test, SUMO has proved its great value for the investigation of the diurnal evolution of the ABL and the identification of mesoscale features residing above the ABL, such as subsidence.     

Modeling the concentration of pollutants using the WRF-ARW atmospheric model and CHIMERE chemistry transport model

Described is a system for analyzing and forecasting the air quality in the central regions of Russia, During the operation of the system, the detailed meteorological information provided by the WRF-ARW model is used by the CHIMERE chemistry transport model for simulating the processes of transport, chemical transformation, and deposition of atmospheric minor constituents. Considered is the quality of retrieved and forecasted (with the lead time up to three days) concentrations of O₃, NO₂, NO, CO, and PM₁₀. The presented verification scores of pollutant concentrations demonstrate a relative success of the system. Demonstrated is a need in improving the data on the emissions of the air pollutants used for simulations. A procedure for the statistical correction of computed concentrations is described and verification scores of its results are given.     

前向散射能见度仪测试模拟环境与试验

能见度是气象、环境和交通观测重要要素,目前不同原理和型号的前向散射能见度仪没有统一的测试环境和定标标准,导致测量结果差别大。为实现前向散射能见度仪统一测试环境,研制了雾环境模拟舱,采用造雾器和净化器组合实现能见度10m到50km模拟。寻找模拟舱内空气均匀度监控方法,消除由空气不均匀带来的误差。测试不同型号的前向散射能见度仪和相同型号能见度仪,结果显示不同型号能见度仪测量值差别大于一倍,同一型号的能见度仪测量值相对误差在±5%内。尝试不同型号能见度仪修正方法,并且验证修正方法可行,修正后的不同型号仪器相对误差在±10%内。     

Quality analysis applied on eddy covariance measurements at complex forest sites using footprint modelling

Summary Measuring turbulent fluxes with the eddy covariance method has become a widely accepted and powerful tool for the determination of long term data sets for the exchange of momentum, sensible and latent heat, and trace gases such as CO₂ between the atmosphere and the underlying surface. Several flux networks developed continuous measurements above complex terrain, e.g. AmeriFlux and EUROFLUX, with a strong focus on the net exchange of CO₂ between the atmosphere and the underlying surface. Under many conditions basic assumptions for the eddy covariance method in its simplified form, such as stationarity of the flow, homogeneity of the surface and fully developed turbulence of the flow field, are not fulfilled. To deal with non-ideal conditions which are common at many FLUXNET sites, quality tests have been developed to check if these basic theoretical assumptions are valid.In the framework of the CARBOEUROFLUX project, we combined quality tests described by Foken and Wichura (1996) with the analytical footprint model of Schmid (1997). The aim was to identify suitable wind sectors and meteorological conditions for flux measurements. These tools were used on data of 18 participating sites. Quality tests were applied on the fluxes of momentum, sensible and latent heat, and on the CO₂-flux, respectively. The influence of the topography on the vertical wind component was also checked. At many sites the land use around the flux towers is not homogeneous or the fetch may not be large enough. So the relative contribution of the land use type intended to be measured was also investigated. Thus the developed tool allows comparative investigations of the measured turbulent fluxes at different sites if using the same technique and algorithms for the determination of the fluxes as well as analyses of potential problems caused by influences of the surrounding land use patterns.     

Increased hurricane intensities with CO2-induced warming as simulated using the GFDL hurricane prediction system

 The impact of CO₂-induced global warming on the intensities of strong hurricanes is investigated using the GFDL regional high-resolution hurricane prediction system. The large-scale initial conditions and boundary conditions for the regional model experiments, including SSTs, are derived from control and transient CO₂ increase experiments with the GFDL R30-resolution global coupled climate model. In a case study approach, 51 northwest Pacific storm cases derived from the global model under present-day climate conditions are simulated with the regional model, along with 51 storm cases for high CO₂ conditions. For each case, the regional model is integrated forward for five days without ocean coupling. The high CO₂ storms, with SSTs warmer by about 2.2 °C on average and higher environmental convective available potential energy (CAPE), are more intense than the control storms by about 3–7 m/s (5%–11%) for surface wind speed and 7 to 24 hPa for central surface pressure. The simulated intensity increases are statistically significant according to most of the statistical tests conducted and are robust to changes in storm initialization methods. Near-storm precipitation is 28% greater in the high CO₂ sample. In terms of storm tracks, the high CO₂ sample is quite similar to the control. The mean radius of hurricane force winds is 2 to 3% greater for the composite high CO₂ storm than for the control, and the high CO₂ storms penetrate slightly higher into the upper troposphere. More idealized experiments were also performed in which an initial storm disturbance was embedded in highly simplified flow fields using time mean temperature and moisture conditions from the global climate model. These idealized experiments support the case study results and suggest that, in terms of thermodynamic influences, the results for the NW Pacific basin are qualitatively applicable to other tropical storm basins. Received: 20 July 1998/Accepted: 24 December 1998     

大气物理过程方案对PM2.5预报的敏感性试验

利用WRF-Chem大气化学模式,选择2015年12月中旬发生在我国的大范围空气污染过程,在采用同样化学方案条件下,针对模式中不同物理过程及其参数化方案开展了地面PM_(2.5)预报的敏感性试验。结果表明:该模式能较好展示此次PM_(2.5)污染的演变过程,与实况也较接近,但对青海经宁夏至内蒙的PM_(2.5)高值区出现了漏报现象,这可能是模式外边界未对污染物做更新所致。对地面PM_(2.5)的预报,各物理过程的敏感度不同,边界层(含近地面层)过程的影响要明显大于积云对流及微物理过程的影响,不同的参数化方案会造成不同的预报误差。边界层过程QNSE和与其配套的近地面层方案的组合是预报的较佳组合;而TEMF和与其配套的组合以及ACM2和Pleim-X的组合则不佳。合理的物理过程参数化方案有助于提高PM_(2.5)预报质量。模式预报对排放源也有适应过程,其Spin-up时间较气象要素长。     

Simultaneous measurements of ozone and its precursors on a diurnal scale at a semi urban site in India

Simultaneous observations of surface ozone (O₃) with its precursors namely, carbon monoxide (CO) and oxides of nitrogen (NO_x) have been taken on diurnal scale from a tropical semi-urban site, Pune (18.54°N, 73.81°E) in India. We present the data for one year (2003–2004) period to study the salient features of these trace gases. The peak in amplitude of ozone is found during the noontime whereas in CO and NO_X it is observed in the morning hours between 0800 and 0900 H. The concentration of these pollutants drop down considerably during southwest monsoon months and the diurnal pattern also become very weak. The diurnal trends of these gases are found to be different for different seasons, which are specific to the receptor site. Model simulations using 3-D chemical-transport model with regional emission inventories and observed winds have also been carried out. The comparison of model results with observations, on seasonal basis yielded a reasonable qualitative agreement. The relative role of local emissions and long range transport in the diurnal pattern for different seasons has been outlined, which reveals that the ozone is highly influenced by regional/long range transport in this region. The effect of precursor amounts in the morning on afternoon ozone peak levels has been investigated using the lag correlation study, which reveals that a time lag of 5–7 h is required for most of these precursor gases to photo-chemically produce ozone to its maximum potential. Results are discussed in the light of available topographic and meteorological conditions.     

Effects of Wall Heating on Flow Characteristics in a Street Canyon

We develop a large-eddy simulation (LES) model based on a meteorological numerical model for a real scale street-canyon flow with rough building facets heated by a given temperature. The model is applied to a canyon with the aspect ratio of unity for two idealized heating scenarios: (1) the roof and the entire upstream wall are heated, named as ‘assisting cases’, and (2) the roof and the entire downstream wall are heated, named as ‘opposing cases’. These facets were heated up to 15 K above the air temperature. A wall function for temperature is proposed for a rough facet with an assumption that the thermal roughness length, z _0T, is much smaller than the aerodynamic roughness length, z ₀. It is demonstrated that the sensible heat flux and canyon-air temperature are significantly influenced by the near-facet process that is parametrized by z _0T as the primary factor; other processes such as in-canyon mixing and roof-level exchange are secondary. This new finding strongly suggests that it is vital to choose an appropriate value of z _0T in a numerical simulation of street-canyon flows with the facet-air exchange processes of heat or any scalar. The finding also raises an awareness of the demand for carefully designed laboratory or field experiments of quantifying z _0T values for various urban surfaces. For the opposing cases, an unsteady penetrating narrow updraft zone appears occasionally along the heated wall and this feature is consistent field observations. The unique result indicates the superior capability of LES. The results of this study can be used to guide the parametrization of turbulent processes inside the urban canopy layer.     

Influences of meteorological conditions on interannual variations of particulate matter pollution during winter in the Beijing–Tianjin–Hebei area

To investigate the interannual variations of particulate matter (PM) pollution in winter, this paper examines the pollution characteristics of PM with aerodynamic diameters of less than 2.5 and 10 μm (i.e., PM_2.5 and PM₁₀), and their relationship to meteorological conditions over the Beijing municipality, Tianjin municipality, and Hebei Province—an area called Jing–Jin–Ji (JJJ, hereinafter)—in December 2013–16. The meteorological conditions during this period are also analyzed. The regional average concentrations of PM_2.5 (PM₁₀) over the JJJ area during this period were 148.6 (236.4), 100.1 (166.4), 140.5 (204.5), and 141.7 (203.1) μg m^–3, respectively. The high occurrence frequencies of cold air outbreaks, a strong Siberian high, high wind speeds and boundary layer height, and low temperature and relative humidity, were direct meteorological causes of the low PM concentration in December 2014. A combined analysis of PM pollution and meteorological conditions implied that control measures have resulted in an effective improvement in air quality. Using the same emissions inventory in December 2013–16, a modeling analysis showed emissions of PM_2.5 to decrease by 12.7%, 8.6%, and 8.3% in December 2014, 2015, and 2016, respectively, each compared with the previous year, over the JJJ area.     

Relative importance of parameters affecting wind speed prediction using artificial neural networks

In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz.     

The Influence of Selected Meteorological Parameters on the Concentration of Surface Ozone in the Central Region of Poland

Abstract?This paper presents the results of measurements of the concentration of surface ozone and concurrent standard meteorological parameters: total solar radiation, temperature, relative humidity, pressure, wind speed, and vertical and horizontal components of the wind. The data were collected from 2005 to 2010 at stations located in central Poland (Mazowieckie voivodeship): Warszawa (urban), Legionowo (suburban), Granica and Belsk (rural). Furthermore, Granica is situated in the forested area of Kampinoski National Park. Continuously measured surface ozone concentrations demonstrated the well-known diurnal cycle of surface ozone concentration with a maximum in the afternoon and a minimum in the early morning hours. The averaged diurnal variations over six years reveal that the highest concentrations appear at rural stations (Belsk: 55?µg?m^?3 and Granica: 50?µg?m^?3) and the lowest at the urban station (Warszawa: 41?µg?m^?3). The threshold for high levels of surface ozone (120?µg?m^?3 per 8?h) was exceeded most often at Granica and Belsk. The occurrence of the ozone “weekend effect,” especially at urban stations, has been identified. The difference between weekend and weekday surface ozone concentrations at urban and rural stations was as high as 6.5?µg?m^?3 and approximately 2?µg?m^?3, respectively. Using appropriate statistical tools, it has been shown that meteorological conditions have a significant influence on ozone concentration. High correlation coefficients were found between ozone concentration and solar radiation, temperature, relative humidity, and wind speed. The forward stepwise regression model explains up to 75% of the variations in daily surface ozone concentration in terms of meteorological variability in summer and up to 70% in winter. At the same time, a multilayer perceptron neural network model was used to reconstruct the concentration of surface ozone. High correlation coefficients (up to 0.89) indicate that, on the basis of standard meteorological parameters and NO₂ concentration, we can determine ozone concentration with high accuracy.     

Surface ozone concentrations in Agra: links with the prevailing meteorological parameters

Measurements of surface ozone (O₃), nitric oxide (NO), nitrogen dioxide (NO₂), oxides of nitrogen (NO_x=NO+NO₂) and meteorological parameters have been made at Agra (North Central India, 27°10??N, 78°05??E) in post monsoon and winter season. The diurnal variation in O₃ concentration shows daytime in situ photochemical production with diurnal maximum in noon hours ranging from 51 to 54 ppb in post monsoon and from 76 to 82 ppb in winter, while minimum (16?C24 ppb) during nighttime and early morning hours. Average 8-h O₃ concentration varied from 12.4 to 83.9 ppb. The relationship between meteorological parameters (solar radiation intensity, temperature, relative humidity, wind speed and wind direction) and surface O₃ variability was studied using principal component analysis (PCA), multiple linear regression (MLR) and correlation analysis (CA). PCA and MLR of daily mean O₃ concentrations on meteorological parameters explain up to 80 % of day to day ozone variability. Correlation with meteorology is strongly emphasized on days having strong solar radiation intensity and longer sunshine time.     

Association among geomagnetic activity,atmospheric electric field and selected meteorological parameters

The association among the geomagnetic activity (Ap index) and atmospheric electric field, meteorological parameters was investigated using a long series of continuous data set available for Colaba (18o53’N, 72o48’E, 11m ASL) for the period 1936-1966. The meteorological parameters used for the investigation are the surface pressure, temperature, wind velocity and relative humidity. The results of the above study indicate that the atmospheric electric field and the meteorological parameters are associated with the geomagnetic storms with Ap > 100. The atmospheric electric field shows an increasing trend after the geomagnetic storm. The surface pressure dips and surface tempera-tures increase after a geomagnetic storm. The wind velocity shows a decreasing trend and the relative humidity shows an increasing trend after the geomagnetic storm.     

数值天气预报多要素深度学习融合订正方法

数值天气预报作为现代天气预报的主流技术方法,近年来不断朝着精细化方向发展,但预报误差至今仍无法避免.文中在CU-Net模型中引入稠密卷积模块形成数值预报要素偏差订正模型Dense-CUnet,在此基础上进一步融合多种气象要素和地形特征构建了Fuse-CUnet模型,开展不同模型的偏差订正试验和对比分析.以均方根误差(R...