香河站太阳辐射识别晴空方法的评估

根据位于华北平原的重污染站点——香河5年的分钟级别地表太阳辐射和人工订正的全天空成像仪数据,对21种晴空识别(CSD)方法进行了评估:晴空识别准确率较高的方法云天识别准确率较低,反之亦然;由于CSD方法采用的参数阈值不适用于污染情况,当气溶胶含量增加时,识别准确率呈下降趋势.研究结果显示,利用太阳辐射数据识别晴空的方法在高污染地区使用时需进行改进.

Preface

Laboratory of Middle Atmosphere and Global Environment Observation(LAGEO)was established in 1995and became one of the Key Laboratory of Chinese Academy of Sciences(CAS)in 2008.LAGEO emphasizes both fundamental research and high-tech research and development,and sets the major research directions as middle atmosphere and global environmental observation,which include the following four aspects:1)

Estimation of PM2.5 Mass Concentration from Visibility

Aerosols in the atmosphere not only degrade visibility,but are also detrimental to human health and transportation.In order to develop a method to estimate PM2.5 mass concentration from the widely measured visibility,a field campaign was conducted in Southwest China in January 2019.Visibility,ambient relative humidity(RH),PM2.5 mass concentrations and scattering coefficients of dry particles were measured.During the campaign,two pollution episodes,i.e.,from 4-9 January and from 10-16 January,were encountered.Each of the two episodes could be divided into two periods.High aerosol hygroscopicity was found during the first period,when RH was higher than 80%at most of the time,and sometimes even approached 100%.The second period experienced a relatively dry but more polluted condition and aerosol hygroscopicity was lower than that during the first period.An empirical relationship between PM2.5 mass concentration and visibility(ambient aerosol extinction)under different RH conditions could thus be established.Based on the empirical relationship,PM2.5 mass concentration could be well estimated from visibility and RH.This method will be useful for remote sensing of PM2.5 mass concentration.

A Thorough Evaluation of the Passive Microwave Radiometer Measurements onboard Three Fengyun-3 Satellites

Microwave Radiometer Imager(MWRI) is a key payload of China’s second generation polar meteorological satellite, i.e., Fengyun-3 series(FY-3). Up to now, 5 satellites including FY-3A(2008), FY-3B(2010), FY-3C(2013), FY-3D(2018), and FY-3E(2021) have been launched successfully to provide multiwavelength, all-weather, and global data for decades. Much progress has been made on the calibration of MWRI and a recalibrated MWRI brightness temperature(BT) product(V2) was recently released. This study thoroughly evaluates the accuracy of this new product from FY-3B, 3C, and 3D by using the simultaneous collocated Global Precipitation Measurement(GPM)Microwave Imager(GMI) measurements as a reference. The results show that the mean biases(MBEs) of the BT between MWRI and GMI are generally less than 0.5 K and the root mean squares(RMSs) between them are less than1.5 K. The previous notable ascending and descending difference of the MWRI has disappeared. This indicates that the new MWRI recalibration procedure is very effective in removing potential errors associated with the emission of the hot-load reflector. Analysis of the dependence of MBE on the latitude and earth scene temperature shows that MBE decreases with decreasing latitude over ocean. Furthermore, MBE over ocean decreases linearly with increasing scene temperature for almost all channels, whereas this does not occur over land. A linear regression fitting is then used to modify MWRI, which can reduce the MBE over ocean to be within 0.2 K. The standard deviation of error of GMI, FY-3B, and FY-3D MWRI BT data derived by using the three-cornered hat method(TCH) shows that GMI has the best overall performance over ocean except at 10.65 GHz where its standard deviation of error is slightly larger than that of FY-3D. Over land, the standard deviation of error of FY-3D is the lowest at almost all channels except at 89V. MWRI onboard FY-3 series satellites would serve as an important passive microwave radiometer member of the constellation to monitor key surface and atmospheric properties.

Variation in Sunshine Duration and Related Aerosol Influences at Shangdianzi GAW Station, China: 1958–2021

Sunshine duration(SD) is adopted widely to study global dimming/brightening. However, long-term simultaneous measurements of SD and closely related impact factors require further analysis to elucidate how and why SD has varied during the past decades. In this study, a long-term(1958–2021) SD data series obtained from the Shangdianzi Global Atmosphere Watch(GAW) station in China was analyzed to detect linear trends, climatic jumps, and climatic periods in SD using linear fitting, the Mann–Kendall trend test, and the continuous wavelet transform method. Annual SD exhibited steady dimming(-67.3 h decade-1) before 2010, followed by a period of brightening(189.9 h decade-1)during 2011–2020. An abrupt jump in annual SD occurred in 1995, and the annual SD anomaly exhibited significant oscillation with ~3-yr periodicity during 1960–1978. Partial least squares analysis revealed that annual SD anomaly was associated with variations in relative humidity, gale days, cloud cover, and black carbon(BC). Further analysis of the clear-sky daily sunshine percentage(DSP) and simultaneous measurements of aerosol properties, including aerosol optical depth, aerosol extinction coefficient, single scattering albedo(SSA), BC, and total suspended particulates, suggested that variation in DSP was affected primarily by aerosol scattering and absorption. Furthermore, the hourly clear-sky SD at high aerosol loading was approximately 60% and 56% of that at middle and low aerosol loadings, respectively. The pattern of diurnal variation in clear-sky hourly SD, as well as the actual values, can be affected by the fine particulate concentration, aerosol extinction coefficient, and SSA.

A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia

The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

Observation and analysis of near-surface atmospheric aerosol optical properties in urban Beijing

Year-round measurements of the mass concentration and optical properties of fine aerosols （PM2s） from June 2009 to May 2010 at an urban site in Beijing were analyzed. The annual mean values of the PM2.5 mass concentration, absorption coefficient （Ab）, scattering coefficient （Sc） and single scattering albedo （SSA） at 525 nm were 67 ± 66 μg/m^3, 64 ± 62 Mm^-1, 360±405 Mm^-1 and 0.82±0.09, respectively. The bulk mass absorption efficiency and scattering efficiency of the PM2.5 at 525 nm were 0.78 m^2/g and 5.55 m^2]g, respectively. The Ab and Sc showed a similar diurnal variation with a maximum at night and a minimum in the afternoon, whereas SSA displayed an opposite diurnal pattern. Significant increases in the Ab and Sc were observed in pollution episodes caused by the accumulation of pollutants from both local and regional sources under unfavorable weather conditions. Aerosol loadings in dust events increased by several times in the spring, which had limited effects on the Ab and Sc due to the low absorption and scattering efficiency of dust particles. The frequency of haze days was the highest in autumn because of the high aerosol absorption and scattering under unfavorable weather conditions. The daily PM2.5 concentration should be controlled to a level lower than 64 μg/m^3 to prevent the occurrence of haze days according to its exponentially decreased relationship with visibility.

Long-term trend in aerosol optical depth from 1980 to 2001 in north China

Using the Total Ozone Mapping Spectrometer （TOMS） monthly aerosol optical depth （AOD） at 500 nm data from 1980 to 2001 in north China, the spatial and temporal variations of AOD were examined. Seasonal AODs in Taklimakan Desert were 0.69 and 0.44 in spring and summer, respectively, which were mainly due to frequent occurrences of dust events in this region. Dust activities in spring also led to high aerosol loading in Gobi Desert and in northeast China where spring AODs were 0.33 and 0.29, respectively. Heavily impacted by events such as volcano eruption, forest fires and extraordinary dust storms, AODs showed large inter-annual variations. A decreasing tendency in AOD was observed in north China during 1980-1991, though a reverse tendency was revealed during 1997-2001, especially for spring AOD in northeast China. Further study is required to figure out how much human activities have contributed to the AOD tendency in north China.

Oscillation of Surface PM2.5 Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing： Mechanisms and Implications

We used simultaneous measurements of surface PM_（2.5） concentration and vertical profiles of aerosol concentration,temperature, and humidity, together with regional air quality model simulations, to study an episode of aerosol pollution in Beijing from 15 to 19 November 2016. The potential effects of easterly and southerly winds on the surface concentrations and vertical profiles of the PM_（2.5） pollution were investigated. Favorable easterly winds produced strong upward motion and were able to transport the PM_（2.5） pollution at the surface to the upper levels of the atmosphere. The amount of surface PM_（2.5） pollution transported by the easterly winds was determined by the strength and height of the upward motion produced by the easterly winds and the initial height of the upward wind. A greater amount of PM_（2.5） pollution was transported to upper levels of the atmosphere by upward winds with a lower initial height. The pollutants were diluted by easterly winds from clean ocean air masses. The inversion layer was destroyed by the easterly winds and the surface pollutants and warm air masses were then lifted to the upper levels of the atmosphere, where they re-established a multi-layer inversion. This region of inversion was strengthened by the southerly winds, increasing the severity of pollution. A vortex was produced by southerly winds that led to the convergence of air along the Taihang Mountains. Pollutants were transported from southern–central Hebei Province to Beijing in the boundary layer. Warm advection associated with the southerly winds intensified the inversion produced by the easterly winds and a more stable boundary layer was formed. The layer with high PM_（2.5） concentration became dee-per with persistent southerly winds of a certain depth. The polluted air masses then rose over the northern Taihang Mountains to the northern mountainous regions of Hebei Province.

Optical and Radiative Properties of Aerosols during a Severe Haze Episode over the North China Plain in December 2016

The optical and radiative properties of aerosols during a severe haze episode from 15 to 22 December 2016 over Beijing, Shijiazhuang, and Jiaozuo in the North China Plain were analyzed based on the ground-based and satellite data, meteorological observations, and atmospheric environmental monitoring data. The aerosol optical depth at 500 nm was 〈 0.30 and increased to 〉 1.4 as the haze pollution developed. The Angstr6m exponent was 〉 0.80 for most of the study period. The daily single-scattering albedo was 〉 0.85 over all of the North China Plain on the most polluted days and was 〉 0.97 on some particular days. The volumes of fine and coarse mode particles during the haze event were approximately 0.05-0.21 and 0.01-0.43 μm^3, respectively-that is, larger than those in the time without haze. The daily absorption aerosol optical depth was about 0.01-0.11 in Beijing, 0.01-0.13 in Shijiazhuang, and 0.01-0.04 in Jiaozuo, and the average absorption Angstrom exponent varied between 0.6 and 2.0. The aerosol radiative forcing at the bottom of the atmosphere varied from -23 to -227, -34 to -199, and -29 to -191 W m^-2 for the whole haze period, while the aerosol radiative forcing at the top of the atmosphere varied from -4 to -98, -10 to -51, and -21 to -143 W m^-2 in Beijing, Shijiazhuang, and Jiaozuo, respectively. Satellite observations showed that smoke, polluted dust, and polluted continental components of aerosols may aggravate air pollution during haze episodes. The analysis of the potential source contribution function and concentration-weighted trajectory showed that the contribu- tion from local emissions and pollutants transport from upstream areas were 190-450 and 100-410 btg m-3, respectively.

First in situ UV profile across the UTLS accompanied by ozone measurement over the Tibetan Plateau

Ultraviolet radiation(UV) and ozone can greatly affect human health and the Earth's ecological environment. By deploying a UV radiometer aboard a stratospheric balloon released at Qaidam(QDM) during the Asian summer monsoon(ASM) period in 2019, we provided in situ measurement of the UV profiles from the surface to the upper troposphere and lower stratosphere over the Tibetan Plateau(TP), China, for the first time. Based on two in situ UV profiles accompanied by four ozonesonde measurements, this study exhibited detailed variations of downwelling UV and vertical ozone distributions over the TP during the ASM period. The UV differences between the surface and stratospheric balloon flight altitudes were 16.7, 15.8, 12.6 and 18.0 Wm^-2 during the four ozonesonde launches. Due to the diurnal variations in photochemical production and the stratosphere-troposphere exchange, the integrated ozone columns below 30 km ranged from 184.4 to 221.6 DU from four ozonesonde measurements. A positive correlation between UV attenuation and ozone column was exhibited under low cloud cover and clear sky conditions. The results of this study are expected to improve our understanding of UV and ozone properties, as well as their potential effects on terrestrial ecosystems and living environments over this significant plateau.

Validation of MODIS and Deep Blue aerosol optical depth retrievals in an arid/semi-arid region of northwest China

The global aerosol optical depth （AOD or r） has been retrieved using the Dark Target algorithm （the C004 and C005 products） and the Deep Blue algorithm （DB product）. Few validations have thus far been performed in arid/semi-arid regions, especially in northwest China. The ground-based remote sensing of AOD from sun photometers at four sites in Xinjiang during the years 2002-2003 is used to validate aerosol products, including C004, C005 and DB of the Moderate Resolution Imaging Spectroradiometer （MODIS）. The results show substantial improvement in the C005 aerosol product over the C004 product. The average correlation coefficient of regression with ground measurements increased from 0.59 to 0.69, and the average offset decreased from 0.28 to 0.13. The slopes of the linear regressions tended to be close to unity. The percentage of AODs falling within the retrieval errors of 30% （or Δτ = ±0.1 ± 0.2τ） increased from 16.1% to 45.6%. The best retrievals are obtained over an oasis region, whereas the worst are obtained over urban areas. Both the MODIS C004 and C005 products overestimate AOD, which is likely related to improper assumptions of the aerosol model and of the estimation of surface reflectance. An encouraging result has been derived with regard to validation of the DB AOD. Overall, the average offset, slope and correlation coefficient of regression with sun-photometer measurements are -0.04, 0.88 and 0.85, respectively. Approximately 73% of the DB AOD retrievals fall within the expected error of 30%. Underestimation of the AOD by the DB products is observed. The aerosol model and estimations of surface reflectance in this region require further improvements.

Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques

Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.