Atmospheric Measurement Techniques最新文献

{"title":"Innovative aerosol hygroscopic growth study from Mie-Raman-Fluorescence lidar and Microwave Radiometer synergy","authors":"Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, Fabrice Ducos","doi":"10.5194/egusphere-2024-84","DOIUrl":"https://doi.org/10.5194/egusphere-2024-84","url":null,"abstract":"<strong>Abstract.</strong> This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie-Raman-Fluorescence lidar (LILAS), developed at the ATOLL platform, Laboratoire d’Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the aerosol layer type, determined through a clustering method, the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ, which depends on the aerosol type. C<span>ase studies conducted on July 29 and March 9, 2021 examine respectively an urban and a smoke aerosol layer</span>. For the urban case, γ is estimated as 0.47±0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5±0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie-Raman-Fluorescence lidar and Microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol-cloud interactions, and climate modeling.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"31 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands","authors":"Linda Bogerd, Hidde Leijnse, Aart Overeem, Remko Uijlenhoet","doi":"10.5194/amt-17-247-2024","DOIUrl":"https://doi.org/10.5194/amt-17-247-2024","url":null,"abstract":"Abstract. The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"23 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Airborne observation with a low-cost hyperspectral instrument: retrieval of NO2 vertical column densities (VCDs) and the satellite sub-grid variability over industrial point sources","authors":"Jong-Uk Park, Hyun-Jae Kim, Jin-Soo Park, Jinsoo Choi, Sang Seo Park, Kangho Bae, Jong-Jae Lee, Chang-Keun Song, Soojin Park, Kyuseok Shim, Yeonsoo Cho, Sang-Woo Kim","doi":"10.5194/amt-17-197-2024","DOIUrl":"https://doi.org/10.5194/amt-17-197-2024","url":null,"abstract":"Abstract. High-spatial-resolution NO2 vertical column densities (VCDs) were retrieved from airborne observations using the low-cost hyperspectral imaging sensor (HIS) at three industrial areas (i.e., Chungnam, Jecheon, and Pohang) in South Korea, where point sources (i.e., power plant, petrochemical complex, steel yard, and cement kiln) with significant NO2 emissions are located. An innovative and versatile approach for NO2 VCD retrieval, hereafter referred to as the modified wavelength pair (MWP) method, was developed to overcome the excessively variable radiometric and spectral characteristics of the HIS attributed to the absence of temperature control during the flight. The newly developed MWP method was designed to be insensitive to broadband spectral features, including the spectral dependency of surface and aerosol reflectivity, and can be applied to observations with relatively low spectral resolutions. Moreover, the MWP method can be implemented without requiring precise radiometric calibration of the instrument (i.e., HIS) by utilizing clean-pixel data for non-uniformity corrections and is also less sensitive to the optical properties of the instrument and offers computational cost competitiveness. In the experimental flights using the HIS, NO2 plumes emitted from steel yards were particularly conspicuous among the various NO2 point sources, with peak NO2 VCDs of 2.0 DU (Dobson unit) at Chungnam and 1.8 DU at Pohang. Typical NO2 VCD uncertainties ranged between 0.025–0.075 DU over the land surface and 0.10–0.15 DU over the ocean surface, and the discrepancy can be attributable to the lower signal-to-noise ratio over the ocean and higher sensitivity of the MWP method to surface reflectance uncertainties under low-albedo conditions. The NO2 VCDs retrieved from the HIS with the MWP method showed a good correlation with the collocated Tropospheric Monitoring Instrument (TROPOMI) data (r=0.73, mean absolute error equals 0.106 DU). However, the temporal disparities between the HIS frames and the TROPOMI overpass, their spatial mismatch, and their different observation geometries could limit the correlation. The comparison of TROPOMI and HIS NO2 VCDs further demonstrated that the satellite sub-grid variability could be intensified near the point sources, with more than a 3-fold increase in HIS NO2 VCD variability (e.g., difference between 25th and 75th quantiles) over the TROPOMI footprints with NO2 VCD values exceeding 0.8 DU compared to footprints with NO2 VCD values below 0.6 DU.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"3 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement uncertainties of scanning microwave radiometers and their influence on temperature profiling","authors":"Tobias Böck, Bernhard Pospichal, Ulrich Löhnert","doi":"10.5194/amt-17-219-2024","DOIUrl":"https://doi.org/10.5194/amt-17-219-2024","url":null,"abstract":"Abstract. In order to improve observations of the atmospheric boundary layer (ABL), the European Meteorological Network, EUMETNET, and the Aerosol, Clouds, and Trace Gases Research Infrastructure, ACTRIS, are currently working on building networks of microwave radiometers (MWRs). Elevation-scanning MWRs are well suited to obtain temperature profiles of the atmosphere, especially within the ABL. Understanding and assessing measurement uncertainties of state-of-the-art scanning MWRs is therefore crucial for accurate temperature profiling. In this paper, we discuss measurement uncertainties due to the instrument setup and originating from external sources, namely (1) horizontal inhomogeneities of the atmosphere, (2) pointing errors or a tilt of the instrument, (3) physical obstacles in the line of sight of the instrument, and (4) radio frequency interference (RFI). Horizontal inhomogeneities from observations at the Jülich Observatory for Cloud Evolution (JOYCE) are shown to have a small impact on retrieved temperature profiles (<|0.22K| in the 25th and 75th percentiles below 3000 m). Typical instrument tilts, that could be caused by uncertainties during the instrument setup, also have a very small impact on temperature profiles and are smaller than 0.1 K below 3000 m for up to 1∘ of tilt. Physical obstacles at ambient temperatures and in the line of sight and filling the complete beam of the MWR at the lowest elevation angle of 5.4∘ have to be at least 600 m away from the instrument in order to have an impact of less than 0.1 K on obtained temperature profiles. If the obstacle is 5 K warmer than its surroundings then the obstacle should be at least 2700 m away. Finally, we present an approach on how to detect RFI with an MWR with azimuth and elevation-scanning capabilities. In this study, we detect RFIs in a water vapor channel that does not influence temperature retrievals but would be relevant if the MWR were used to detect horizontal humidity inhomogeneities.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"57 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Sensitivity Analysis of simulated polarimetric remote sensing observations over snow","authors":"Gabriel Harris Myers, Nan Chen, Matteo Ottaviani","doi":"10.5194/egusphere-2023-3023","DOIUrl":"https://doi.org/10.5194/egusphere-2023-3023","url":null,"abstract":"<strong>Abstract.</strong> This study presents a detailed theoretical assessment of the information content of polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses parameter covariance across the entire parameter space. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing impurities (LAIs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAIs in the VIS-NIR, and to grain size at different depths in the snowpack in the NIR-SWIR. When present in the snow or in the atmosphere, LAIs introduce correlated information on grain size in the VIS. Such sensitivity can be disentangled by including multi-spectral and multi-angle polarimetric measurements, leading to a better estimate of grain shape and ice crystal roughness, and in turn of the asymmetry parameter which is critical for the determination of albedo. Polarimetry in the SWIR also contains information on aerosol optical thickness while remaining essentially unaffected when the same impurities are embedded in the snow, so that it can effectively partition LAIs between the atmosphere and the surface (a notorious challenge for snow remote sensing based on measurements of total reflectance only) as prospected in a precursor study. The GSA results were used to select state parameters in retrievals performed on data simulated for plausible polar conditions and for multiple instrument configurations. Mono-angle measurements of total reflectance in the VIS-SWIR (akin to MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS-NIR provides information on grain shape and microscale roughness, significantly decreasing the uncertainty in the derived impurity concentration and aerosol optical depth. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors, like those onboard the upcoming PACE and 3MI spaceborne missions. The better characterization of surface and atmospheric parameters in snow-covered regions of the cryosphere ultimately benefits albedo estimates in climate models.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"24 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139461856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparisons and quality control of wind observations in a mountainous city using wind profile radar and the Aeolus satellite","authors":"Hua Lu, Min Xie, Wei Zhao, Bojun Liu, Tijian Wang, Bingliang Zhuang","doi":"10.5194/amt-17-167-2024","DOIUrl":"https://doi.org/10.5194/amt-17-167-2024","url":null,"abstract":"Abstract. Observations of the vertical wind profile in Chongqing, a typical mountainous city in China, are important, but they are sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) with WPR data was then performed. The conclusions can be summarized as follows: (1) a clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 0.71. Their root mean square deviation increased with height but decreased at heights between 3 and 4 km. (2) After quality control using Gaussian filtering (GF) and empirical orthogonal function construction (EOFc; G=87.23 %) of the WPR data, the R between the WPR and RS reached 0.83 and 0.95, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed within ±5 m s−1 for Rayleigh-clear and Mie-cloudy winds (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) vs WPR winds, respectively. Vertically, large mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing; in this case the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences at a height range between 4 and 8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere of Chongqing. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated within ±5 m s−1 for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared within ±5 m s−1 for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with spars","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"98 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139461824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response of protonated, adduct, and fragmented ions in Vocus proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS)","authors":"Fangbing Li, Dan Dan Huang, Linhui Tian, Bin Yuan, Wen Tan, Liang Zhu, Penglin Ye, Douglas Worsnop, Ka In Hoi, Kai Meng Mok, Yong Jie Li","doi":"10.5194/egusphere-2024-16","DOIUrl":"https://doi.org/10.5194/egusphere-2024-16","url":null,"abstract":"<strong>Abstract.</strong> Volatile organic compounds (VOCs) affect secondary pollutant formation via active chemistry. Proton-transfer-reaction mass spectrometry (PTR-MS) is one of the most important techniques to study the highly variable spatial and temporal characteristics of VOCs. The response of protonated, adduct, and fragmented ions in PTR-MS in changing instrument settings and varying relative humidity (RH) requires rigorous characterization. Herein, dedicatedly designed laboratory experiments were conducted to investigate the response of these ions for 21 VOCs, including 12 oxygenated VOCs and two nitriles, using the recently developed Vocus PTR-MS. Our results show that the focusing ion-molecule reactor (FIMR) axial voltage increases sensitivity by three to four orders of magnitude but does not significantly change the fractions of protonated ions. Reducing the FIMR pressure, however, substantially increases fragmentation. Applying a high radio frequency (RF) amplitude radially on FIMR can enhance sensitivity by one to two orders of magnitude without affecting the protonated ion fractions. The change in big segmented quadrupole (BSQ) amplitude mainly affects sensitivity and protonated ion fraction by modifying ion transmission. The relationship between sensitivity and proton-transfer reaction rate constant is complicated by the influences from both ion transmission and protonated ion fraction. The protonated ions of most VOCs studied (19 out of 21) show less than 15 % variations in sensitivity as RH increases from ~5 % to ~85 %, except for some long-chain aldehydes which show a positive RH variation of up to 30 %. Our results suggest that the Vocus PTR-MS can reliably quantify the majority of VOCs under ambient conditions with varying RH. However, caution is advised for small oxygenates such as formaldehyde and methanol due to their low sensitivity, as well as for long-chain aldehydes for their slight RH dependence and fragmentation.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"14 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139461791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-process correction improves the accuracy of satellite PM2.5 retrievals","authors":"Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, Antti Lipponen","doi":"10.5194/egusphere-2023-2635","DOIUrl":"https://doi.org/10.5194/egusphere-2023-2635","url":null,"abstract":"<strong>Abstract.</strong> Estimates of PM_2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM_2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM_2.5 retrieval, which involves conversion of high-resolution satellite retrieval of Aerosol Optical Depth (AOD) into high-resolution PM_2.5 retrieval. To improve the accuracy of the AOD to PM_2.5 conversion, we employ the machine learning based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high resolution geographical indicators, meteorological data and ground station observations for learning a predictor for the approximation error in the AOD to PM_2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM_2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. Our model produces PM_2.5 estimates with a spatial resolution of 100 meters at satellite overpass times. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine learning related uncertainty in the PM_2.5 estimates.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"103 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139461858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface to the inter-journal special issue “RUSTED: Reducing Uncertainty in Soluble aerosol Trace Element Deposition”","authors":"Morgane M. G. Perron, Susanne Fietz, Douglas S. Hamilton, Akinori Ito, Rachel U. Shelley, Mingjin Tang","doi":"10.5194/amt-17-165-2024","DOIUrl":"https://doi.org/10.5194/amt-17-165-2024","url":null,"abstract":"Abstract not available","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"13 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139461900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerosol layer height (ALH) retrievals from oxygen absorption bands: Intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI","authors":"Hyerim Kim, Xi Chen, Jun Wang, Zhendong Lu, Meng Zhou, Gregory Carmichael, Sang Seo Park, Jhoon Kim","doi":"10.5194/egusphere-2023-3115","DOIUrl":"https://doi.org/10.5194/egusphere-2023-3115","url":null,"abstract":"<strong>Abstract.</strong> Although containing only single piece of information, aerosol layer height (ALH) indicates the altitude of aerosol layer in vertical coordinate which is essential for assessment of surface air quality and aerosol climate impact. Passive remote sensing measurements in oxygen (O₂) absorption bands are sensitive to ALH, providing an opportunity to derive global or regional ALH information from satellite observations. In this study, we compare ALH products retrieved from near-infrared O₂ absorption measurements from multiple satellite platforms including Geostationary Environment Monitoring Spectrometer (GEMS) focusing on Asia, Earth Polychromatic Imaging Camera (EPIC) in deep space, and polar orbiting satellite TROPOspheric Monitoring Instrument (TROPOMI), and validate them using spaceborne lidar (CALIOP) measurements for typical dust and smoke plumes. Adjustments have been made to account for the inherent variations in the definitions of ALH among different products, ensuring an apple-to-apple comparison. In comparison with CALIOP ALH, both EPIC and TROPOMI ALH display a high correlation coefficient (R) higher than 0.7 and an overestimation by ~ 0.8 km, whereas GEMS ALH exhibits minimal bias (0.1 km) but a slightly lower correlation with R of 0.64. Categorizing GEMS retrievals with UVAI ≥ 3 improves the agreement with CALIOP. GEMS ALH demonstrates a narrower range and lower mean value compared to EPIC and TROPOMI, and their correlation is further improved when UVAI ≥ 3. Furthermore, diurnal variation of GEMS and EPIC ALH, especially for UVAI ≥ 3, aligns with boundary layer development. Considering the important role of AOD in ALH retrieval, we found GEMS AOD at 680 nm correlates well with AERONET AOD (R ~ 0.9) but features a negative bias of -0.2. EPIC and TROPOMI tend to overestimate ALH by 0.33 km and 0.23 km, respectively, in dust cases. Finally, a dust and a smoke case are analysed in detail to explore the variation of ALH during plume transport from multiple data.","PeriodicalId":8619,"journal":{"name":"Atmospheric Measurement Techniques","volume":"4 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139423406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}