Articles | Volume 16, issue 6
https://doi.org/10.5194/amt-16-1539-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1539-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Mar 2023
Research article |
| 24 Mar 2023
| 24 Mar 2023
Retrievals of precipitable water vapor and aerosol optical depth from direct sun measurements with EKO MS711 and MS712 spectroradiometers
Congcong Qiao
LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Song Liu
LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Xihan Mu
State Key Laboratory of Remote Sensing Science, Faculty of
Geographical Science, Beijing Normal University, Beijing, 100875, China
Ping Wang
Royal Netherlands Meteorological Institute (KNMI), De Bilt, 3731 GA, the
Netherlands
Shengjie Jia
Beijing Keytec Technology Co., Ltd., Beijing, 100029, China
Xuehua Fan
LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Minzheng Duan
CORRESPONDING AUTHOR
LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
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Minqiang Zhou, Yilong Wang, Minzheng Duan, Xiangjun Tian, Jinzhi Ding, Jianrong Bi, Yaoming Ma, Weiqiang Ma, and Zhenhua Xi
Atmos. Meas. Tech., 18, 4311–4324, https://doi.org/10.5194/amt-18-4311-2025, https://doi.org/10.5194/amt-18-4311-2025, 2025
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The Qinghai–Tibetan Plateau is a key system that impacts the global carbon balance. This study presents the greenhouse gas (GHG) mole fraction measurement campaign in May 2022 at Mt. Qomolangma station, including ground-based remote sensing and in situ measurements. The GHG measurements are carried out in this region for the first time and used for satellite validation.
Victor J. H. Trees, Ping Wang, Job I. Wiltink, Piet Stammes, Daphne M. Stam, David P. Donovan, and A. Pier Siebesma
EGUsphere, https://doi.org/10.5194/egusphere-2025-2197, https://doi.org/10.5194/egusphere-2025-2197, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We present MONKI (Monte Carlo KNMI), an efficient and accurate radiative transfer code written in Fortran. MONKI computes total and polarised radiances reflected and transmitted by planetary atmospheres, accounting for polarisation in all scattering orders. MONKI handles both homogeneous atmospheres and 3D cloud structures. MONKI has been validated, and produces reliable results even for planets with optically thick, strongly polarising atmospheres.
Ying Zhou, Congcong Qiao, Minqiang Zhou, Yilong Wang, Xiangjun Tian, Yinghong Wang, and Minzheng Duan
Atmos. Meas. Tech., 18, 1609–1619, https://doi.org/10.5194/amt-18-1609-2025, https://doi.org/10.5194/amt-18-1609-2025, 2025
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We developed an automated, flexible atmospheric sampling device for various platforms. During a 5 d field campaign in the Mount Qomolangma region, we performed 15 flights using the device mounted on a hexacopter unoccupied aerial vehicle (UAV). A total of 139 samples were analyzed using an Agilent 7890A gas chromatograph. Vertical profiles of four greenhouse gases (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and sulfur hexafluoride (SF6)) were analyzed and discussed.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Tian Zhao, Wanjuan Song, Xihan Mu, Yun Xie, Donghui Xie, and Guangjian Yan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-535, https://doi.org/10.5194/essd-2024-535, 2024
Revised manuscript under review for ESSD
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Our research aimed to provide reliable data for measuring fractional vegetation cover, essential for understanding climate patterns and ecological health. We used the MultiVI algorithm, which employs satellite images from various angles to enhance accuracy. Our method outperformed traditional statistical methods compared to field measurements, enabling precise large-scale mapping of vegetation cover for improved environmental monitoring and planning.
Ping Wang, David Patrick Donovan, Gerd-Jan van Zadelhoff, Jos de Kloe, Dorit Huber, and Katja Reissig
Atmos. Meas. Tech., 17, 5935–5955, https://doi.org/10.5194/amt-17-5935-2024, https://doi.org/10.5194/amt-17-5935-2024, 2024
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We describe the new feature mask (AEL-FM) and aerosol profile retrieval (AEL-PRO) algorithms developed for Aeolus lidar and present the evaluation of the Aeolus products using CALIPSO data for dust aerosols over Africa. We have found that Aeolus and CALIPSO show similar aerosol patterns in the collocated orbits and have good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The finding is applicable to Aeolus L2A product Baseline 17.
David Patrick Donovan, Gerd-Jan van Zadelhoff, and Ping Wang
Atmos. Meas. Tech., 17, 5301–5340, https://doi.org/10.5194/amt-17-5301-2024, https://doi.org/10.5194/amt-17-5301-2024, 2024
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ATLID (atmospheric lidar) is the lidar to be flown on the Earth Clouds and Radiation Explorer satellite (EarthCARE). EarthCARE is a joint European–Japanese satellite mission that was launched in May 2024. ATLID is an advanced lidar optimized for cloud and aerosol property profile measurements. This paper describes some of the key novel algorithms being applied to this lidar to retrieve cloud and aerosol properties. Example results based on simulated data are presented and discussed.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022, https://doi.org/10.5194/gmd-15-7031-2022, 2022
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We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
Ze Chen, Yufang Tian, Yinan Wang, Yongheng Bi, Xue Wu, Juan Huo, Linjun Pan, Yong Wang, and Daren Lü
Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, https://doi.org/10.5194/amt-15-4785-2022, 2022
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Small-scale turbulence plays a vital role in the vertical exchange of heat, momentum and mass in the atmosphere. There are currently three models that can use spectrum width data of MST radar to calculate turbulence parameters. However, few studies have explored the applicability of the three calculation models. We compared and analysed the turbulence parameters calculated by three models. These results can provide a reference for the selection of models for calculating turbulence parameters.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 15, 3121–3140, https://doi.org/10.5194/amt-15-3121-2022, https://doi.org/10.5194/amt-15-3121-2022, 2022
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Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a satellite spectrometer.
Siming Zheng, Juan Huo, Wenbing Cai, Yinhui Zhang, Peng Li, Gaoyuan Zhang, Baofeng Ji, Jiafeng Zhou, and Congzheng Han
Atmos. Meas. Tech., 15, 1675–1687, https://doi.org/10.5194/amt-15-1675-2022, https://doi.org/10.5194/amt-15-1675-2022, 2022
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We demonstrated the processing of millimeter wave link data with a time resolution of 1 min for 60 dry periods from August 2020 to July 2021. We have proposed a new method for extracting water vapor attenuation values and applied this method to data processing within this year. We found that the water vapor density value obtained from the millimeter wave link is highly correlated with the actual measurement value of the weather station.
Wenying He, Hongbin Chen, Yuejian Xuan, Jun Li, Minzheng Duan, and Weidong Nan
Atmos. Meas. Tech., 14, 7069–7078, https://doi.org/10.5194/amt-14-7069-2021, https://doi.org/10.5194/amt-14-7069-2021, 2021
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Large microwave surface emissivities (ε) cause difficulties in widely using satellite microwave data over land. Usually, ground-based radiometers are fixed to a scan field to obtain the temporal evolution of ε over a single land-cover area. To obtain the long-term temporal evolution of ε over different land-cover surfaces simultaneously, we developed a ground mobile observation system to enhance in situ ε observations and presented some preliminary results.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126, https://doi.org/10.5194/amt-14-5107-2021, https://doi.org/10.5194/amt-14-5107-2021, 2021
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The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Victor Trees, Ping Wang, and Piet Stammes
Atmos. Chem. Phys., 21, 8593–8614, https://doi.org/10.5194/acp-21-8593-2021, https://doi.org/10.5194/acp-21-8593-2021, 2021
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Given the time and location of a point on the Earth's surface, we explain how to compute the wavelength-dependent obscuration during solar eclipses. We restore the top-of-atmosphere reflectances and the absorbing aerosol index in the partial Moon shadow during the solar eclipses on 26 December 2019 and 21 June 2020 measured by TROPOMI. This correction method resolves eclipse anomalies and allows for study of the effect of solar eclipses on the composition of the Earth's atmosphere from space.
Ioana Elisabeta Popovici, Zhaoze Deng, Philippe Goloub, Xiangao Xia, Hongbin Chen, Luc Blarel, Thierry Podvin, Yitian Hao, Hongyan Chen, Disong Fu, Nan Yin, Benjamin Torres, Stéphane Victori, and Xuehua Fan
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1269, https://doi.org/10.5194/acp-2020-1269, 2021
Preprint withdrawn
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This study reports results from MOABAI campaign (Mobile Observation of Atmosphere By vehicle-borne Aerosol measurement Instruments) in North China Plain in may 2017, a unique campaign involving a van equipped with remote sensing and in situ instruments to perform on-road mobile measurements. Aerosol optical properties and mass concentration profiles were derived, capturing the fine spatial distribution of pollution and concentration levels.
Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, https://doi.org/10.5194/amt-14-2451-2021, 2021
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The high-resolution satellite Sentinel-5p TROPOMI observes several atmospheric gases. To account for cloud interference with the observations, S5P cloud data products (CLOUD OCRA/ROCINN_CAL, OCRA/ROCINN_CRB, and FRESCO) provide vital input: cloud fraction, cloud height, and cloud optical thickness. Here, S5P cloud parameters are validated by comparing with other satellite sensors (VIIRS, MODIS, and OMI) and with ground-based CloudNet data. The agreement depends on product type and cloud height.
Maurits L. Kooreman, Piet Stammes, Victor Trees, Maarten Sneep, L. Gijsbert Tilstra, Martin de Graaf, Deborah C. Stein Zweers, Ping Wang, Olaf N. E. Tuinder, and J. Pepijn Veefkind
Atmos. Meas. Tech., 13, 6407–6426, https://doi.org/10.5194/amt-13-6407-2020, https://doi.org/10.5194/amt-13-6407-2020, 2020
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We investigated the influence of clouds on the Absorbing Aerosol Index (AAI), an indicator of the presence of small particles in the atmosphere. Clouds produce artifacts in AAI calculations on the individual measurement (7 km) scale, which was not seen with previous instruments, as well as on large (1000+ km) scales. To reduce these artefacts, we used three different AAI calculation techniques of varying complexity. We find that the AAI artifacts are reduced when using more complex techniques.
Juan Huo, Yufang Tian, Xue Wu, Congzheng Han, Bo Liu, Yongheng Bi, Shu Duan, and Daren Lyu
Atmos. Chem. Phys., 20, 14377–14392, https://doi.org/10.5194/acp-20-14377-2020, https://doi.org/10.5194/acp-20-14377-2020, 2020
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A detailed analysis of ice cloud physical properties is presented based on 4 years of surface Ka-band radar measurements in Beijing, where the summer oceanic monsoon from the ocean and winter continental monsoon prevail alternately. More than 6000 ice cloud clusters were studied to investigate their physical properties, such as height, horizontal extent, temperature dependence and origination type, which can serve as a reference for parameterization and characterization in global climate models.
Cited articles
Augustine, J. A., Hodges, G. B., Dutton, E. G., Michalsky, J. J., and
Cornwall, C. R.: An aerosol optical depth climatology for NOAA's national
surface radiation budget network (SURFRAD), J. Geophys. Res., 113, D11204,
https://doi.org/10.1029/2007jd009504, 2008.
Barreto, A., Cuevas, E., Pallé, P., Romero, P. M., Guirado, C., Wehrli, C. J., and Almansa, F.: Recovering long-term aerosol optical depth series (1976–2012) from an astronomical potassium-based resonance scattering spectrometer, Atmos. Meas. Tech., 7, 4103–4116, https://doi.org/10.5194/amt-7-4103-2014, 2014.
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and
Ware, R. H.: GPS meteorology: Remote sensing of atmospheric water vapor
using the global positioning system, J. Geophys. Res.-Atmos., 97,
15787–15801, https://doi.org/10.1029/92JD01517, 1992.
Blanc, P., Espinar, B., Geuder, N., Gueymard, C., Meyer, R., Pitz-Paal, R.,
Reinhardt, B., Renné, D., Sengupta, M., Wald, L., and Wilbert, S.:
Direct normal irradiance related definitions and applications: The
circumsolar issue, Sol. Energy, 110, 561–577, https://doi.org/10.1016/j.solener.2014.10.001, 2014.
Bodhaine, B. A., Wood, N. B., Dutton, E. G., and Slusser, J. R.: On Rayleigh
Optical Depth Calculations, J. Atmos. Ocean. Technol., 16, 1854–1861,
https://doi.org/10.1175/1520-0426(1999)016<1854:Orodc>2.0.Co;2, 1999.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The Concept of Essential Climate Variables in Support of Climate
Research, Applications, and Policy, B. Am. Meteorol. Soc., 95, 1431–1443,
https://doi.org/10.1175/bams-d-13-00047.1, 2014.
Cachorro, V. E., Berjon, A., Toledano, C., Mogo, S., Prats, N., Frutos, M.
D., Vilaplana, J. M., Sorribas, M., Morena, B. A. D. L., and Groebner, J.:
Detailed Aerosol Optical Depth Intercomparison between Brewer and Li-Cor
1800 Spectroradiometers and a Cimel Sun Photometer, J. Atmos. Ocean.
Tech., 26, 1558–1571, https://doi.org/10.1175/2009JTECHA1217.1, 2009.
Campanelli, M., Estelles, V., Smyth, T., Tomasi, C., and Nakajima, T.:
Monitoring of Eyjafjallajoekull volcanic aerosol by the new European SkyRad
users (ESR) sun-sky radiometer network, Atmos. Environ., 48, 33–45,
https://doi.org/10.1016/j.atmosenv.2011.09.070, 2012.
Campanelli, M., Nakajima, T., Khatri, P., Takamura, T., Uchiyama, A., Estelles, V., Liberti, G. L., and Malvestuto, V.: Retrieval of characteristic parameters for water vapour transmittance in the development of ground-based sun–sky radiometric measurements of columnar water vapour, Atmos. Meas. Tech., 7, 1075–1087, https://doi.org/10.5194/amt-7-1075-2014, 2014.
Che, H., Gui, K., Chen, Q., Zheng, Y., Yu, J., Sun, T., Zhang, X., and Shi,
G.: Calibration of the 936 nm water-vapor channel for the China aerosol
remote sensing NETwork (CARSNET) and the effect of the retrieval water-vapor
on aerosol optical property over Beijing, China, Atmos. Pollut. Res., 7,
743–753, https://doi.org/10.1016/j.apr.2016.04.003, 2016.
Cuevas Agulló, E., Milford, C., and Tarasova, O.: Izaña Atmospheric
Research Center, Activity Report 2012–2014, https://doi.org/10.31978/281-15-004-2, 2015.
Dubovik, O., Li, Z., Mishchenko, M. I., Tanré, D., Karol, Y., Bojkov,
B., Cairns, B., Diner, D. J., Espinosa, W. R., Goloub, P., Gu, X., Hasekamp,
O., Hong, J., Hou, W., Knobelspiesse, K. D., Landgraf, J., Li, L., Litvinov,
P., Liu, Y., Lopatin, A., Marbach, T., Maring, H., Martins, V., Meijer, Y.,
Milinevsky, G., Mukai, S., Parol, F., Qiao, Y., Remer, L., Rietjens, J.,
Sano, I., Stammes, P., Stamnes, S., Sun, X., Tabary, P., Travis, L. D.,
Waquet, F., Xu, F., Yan, C., and Yin, D.: Polarimetric remote sensing of
atmospheric aerosols: Instruments, methodologies, results, and perspectives,
J. Quant. Spectrosc. Radiat. Transfer., 224, 474–511, https://doi.org/10.1016/j.jqsrt.2018.11.024, 2019.
Estellés, V., Utrillas, M. P., Martínez-Lozano, J. A.,
Alcántara, A., Alados-Arboledas, L., Olmo, F. J., Lorente, J., de Cabo,
X., Cachorro, V., Horvath, H., Labajo, A., Sorribas, M., Díaz, J. P.,
Díaz, A. M., Silva, A. M., Elías, T., Pujadas, M., Rodrigues, J.
A., Cañada, J., and García, Y.: Intercomparison of
spectroradiometers and Sun photometers for the determination of the aerosol
optical depth during the VELETA-2002 field campaign, J. Geophys. Res., 111, D17207,
https://doi.org/10.1029/2005jd006047, 2006.
García, R. D., Cuevas, E., Cachorro, V. E., García, O. E.,
Barreto, Á., Almansa, A. F., Romero-Campos, P. M., Ramos, R., Pó,
M., Hoogendijk, K., and Gross, J.: Water Vapor Retrievals from Spectral
Direct Irradiance Measured with an EKO MS-711
Spectroradiometer – Intercomparison with Other Techniques, Remote Sens., 13,
350, https://doi.org/10.3390/rs13030350, 2021.
García-Cabrera, R. D., Cuevas-Agulló, E., Barreto, Á., Cachorro, V. E., Pó, M., Ramos, R., and Hoogendijk, K.: Aerosol retrievals from the EKO MS-711 spectral direct irradiance measurements and corrections of the circumsolar radiation, Atmos. Meas. Tech., 13, 2601–2621, https://doi.org/10.5194/amt-13-2601-2020, 2020.
Gueymard, C. A.: Parameterized transmittance model for direct beam and
circumsolar spectral irradiance, Sol. Energy, 71, 325–346, https://doi.org/10.1016/S0038-092X(01)00054-8, 2001.
Güldner, J.: A model-based approach to adjust microwave observations for operational applications: results of a campaign at Munich Airport in winter 2011/2012, Atmos. Meas. Tech., 6, 2879–2891, https://doi.org/10.5194/amt-6-2879-2013, 2013.
Güldner, J. and Spänkuch, D.: Remote Sensing of the Thermodynamic
State of the Atmospheric Boundary Layer by Ground-Based Microwave
Radiometry, J. Atmos. Ocean. Tech., 18, 925–933, https://doi.org/10.1175/1520-0426(2001)018<0925:Rsotts>2.0.Co;2, 2001.
Hansen, J. E. and Travis, L. D.: Light scattering in planetary atmospheres,
Space Sci. Rev., 16, 527–610, https://doi.org/10.1007/BF00168069, 1974.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., and Nakajima, T.:
AERONET – A Federated Instrument Network and Data Archive for Aerosol
Characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Ingold, T., Schmid, B., Mätzler, C., Demoulin, P., and Kämpfer, N.:
Modeled and empirical approaches for retrieving columnar water vapor from
solar transmittance measurements in the 0.72, 0.82, and 0.94 µm
absorption bands, J. Geophys. Res.-Atmos., 105, 24327–24343, https://doi.org/10.1029/2000jd900392, 2000.
IPCC: The Physical Science Basis, Intergovernmental Panel on Climate Change, Cambridge University Press, 119–158 pp.,
https://doi.org/10.1017/CBO9781107415324, 2013.
IPCC: Climate Change 2021: The Physical Science Basis, Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, https://www.ipcc.ch/report/ar6/wg1/about/how-to-cite-this-report/ (last access: 11 October 2021), Cambridge University Press, 147–286 pp.,
2021.
Kaufman, Y. J., Tanré, D., and Boucher, O.: A satellite view of aerosols
in the climate system, Nature, 419, 215–223, https://doi.org/10.1038/nature01091, 2002.
Kazadzis, S., Veselovskii, I., Amiridis, V., Gröbner, J., Suvorina, A., Nyeki, S., Gerasopoulos, E., Kouremeti, N., Taylor, M., Tsekeri, A., and Wehrli, C.: Aerosol microphysical retrievals from precision filter radiometer direct solar radiation measurements and comparison with AERONET, Atmos. Meas. Tech., 7, 2013–2025, https://doi.org/10.5194/amt-7-2013-2014, 2014.
Kazadzis, S., Kouremeti, N., Diémoz, H., Gröbner, J., Forgan, B. W., Campanelli, M., Estellés, V., Lantz, K., Michalsky, J., Carlund, T., Cuevas, E., Toledano, C., Becker, R., Nyeki, S., Kosmopoulos, P. G., Tatsiankou, V., Vuilleumier, L., Denn, F. M., Ohkawara, N., Ijima, O., Goloub, P., Raptis, P. I., Milner, M., Behrens, K., Barreto, A., Martucci, G., Hall, E., Wendell, J., Fabbri, B. E., and Wehrli, C.: Results from the Fourth WMO Filter Radiometer Comparison for aerosol optical depth measurements, Atmos. Chem. Phys., 18, 3185–3201, https://doi.org/10.5194/acp-18-3185-2018, 2018a.
Kazadzis, S., Kouremeti, N., Nyeki, S., Gröbner, J., and Wehrli, C.: The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements, Geosci. Instrum. Method. Data Syst., 7, 39–53, https://doi.org/10.5194/gi-7-39-2018, 2018b.
Kokhanovsky, A. A.: Remote sensing of atmospheric aerosol using spaceborne
optical observations, Earth Sci. Rev., 116, 95–108, https://doi.org/10.1016/j.earscirev.2012.10.008, 2013.
Kurucz, R. L.: Synthetic Infrared Spectra, Iua. Symp., 154, 523–531,
https://doi.org/10.1007/978-94-011-1926-9_62,
1994.
Larar, A. M., Berk, A., Anderson, G. P., Bernstein, L. S., Acharya, P. K.,
Dothe, H., Matthew, M. W., Adler-Golden, S. M., Chetwynd, J. J. H.,
Richtsmeier, S. C., Pukall, B., Allred, C. L., Jeong, L. S., and Hoke, M.
L.: MODTRAN4 radiative transfer modeling for atmospheric correction,
P. Soc. Photo.-Opt. Ins., 3756, 348–353, https://doi.org/10.1117/12.366388, 1999.
Li, C., Li, J., Xu, H., Li, Z., Xia, X., and Che, H.: Evaluating VIIRS EPS
Aerosol Optical Depth in China: An intercomparison against ground-based
measurements and MODIS, J. Quant. Spectrosc. Radiat. Transfer., 224, 368–377,
https://doi.org/10.1016/j.jqsrt.2018.12.002, 2019.
Li, Z. Q., Xu, H., Li, K. T., Li, D. H., Xie, Y. S., Li, L., Zhang, Y., Gu,
X. F., Zhao, W., Tian, Q. J., Deng, R. R., Su, X. L., Huang, B., Qiao, Y.
L., Cui, W. Y., Hu, Y., Gong, C. L., Wang, Y. Q., Wang, X. F., Wang, J. P.,
Du, W. B., Pan, Z. Q., Li, Z. Z., and Bu, D.: Comprehensive Study of
Optical, Physical, Chemical, and Radiative Properties of Total Columnar
Atmospheric Aerosols over China: An Overview of Sun–Sky Radiometer
Observation Network (SONET) Measurements, B. Am. Meteorol. Soc., 99,
739–755, https://doi.org/10.1175/bams-d-17-0133.1, 2018.
Michalsky, J. J., Schlemmer, J. A., Berkheiser, W. E., Berndt, J. L.,
Harrison, L. C., Laulainen, N. S., Larson, N. R., and Barnard, J. C.:
Multiyear measurements of aerosol optical depth in the Atmospheric Radiation
Measurement and Quantitative Links programs, J. Geophys. Res.-Atmos., 106,
12099–12107, https://doi.org/10.1029/2001jd900096, 2001.
NASA: AERONET: AErosol RObotic NETwork, NASA [data set], https://aeronet.gsfc.nasa.gov (last access: 1 April 2022), 2023.
NOAA: NOAA, U.S. standard atmosphere 1976, 21–47 pp., NOAA-S/T 76-1526, 1976.
Ramachandran, S., Jayaraman, A., Acharya, Y., and Subbaraya, B.: Features of
aerosol optical depths over Ahmedabad as observed with a Sun-tracking
photometer, Beitr. Phys. Atmosph., 67, 57–70, 1994.
Raptis, P.-I., Kazadzis, S., Gröbner, J., Kouremeti, N., Doppler, L., Becker, R., and Helmis, C.: Water vapour retrieval using the Precision Solar Spectroradiometer, Atmos. Meas. Tech., 11, 1143–1157, https://doi.org/10.5194/amt-11-1143-2018, 2018.
Schmid, B., Michalsky, J., Halthore, R., Beauharnois, M., Harrison, L.,
Livingston, J., Russell, P., Holben, B., Eck, T., and Smirnov, A.:
Comparison of aerosol optical depth from four solar radiometers during the
fall 1997 ARM intensive observation period, Geophys. Res. Lett., 26,
2725–2728, https://doi.org/10.1029/1999gl900513, 1999.
Sinyuk, A., Holben, B. N., Smirnov, A., Eck, T. F., Slutsker, I., Schafer,
J. S., Giles, D. M., and Sorokin, M.: Assessment of error in aerosol optical
depth measured by AERONET due to aerosol forward scattering, Geophys. Res.
Lett., 39, L28306, https://doi.org/10.1029/2012gl053894, 2012.
Smirnov, A., Holben, B. N., Eck, T. F., Dubovik, O., and Slutsker, I.:
Cloud-Screening and Quality Control Algorithms for the AERONET Database,
Remote Sens. Environ., 73, 337–349, https://doi.org/10.1016/s0034-4257(00)00109-7, 2000.
Smirnov, A., Holben, B., Lyapustin, A., Slutsker, I., and Eck, T.: AERONET
processing algorithms refinement, AERONET Workshop, El Arenosillo, Spain,
10–14, 291795812, 2004.
Stamnes, K., Tsay, S. C., Wiscombe, W. J., and Jayaweera, K.: Numerically
stable algorithm for discrete-ordinate-method radiative transfer in multiple
scattering and emitting layered media, Appl. Opt., 27, 2502–2509,
https://doi.org/10.1364/AO.27.002502, 1988.
Swinehart, F. D.: The Beer-Lambert Law, J. Chem. Educ., 39, 333, https://doi.org/10.1021/ed039p333, 1962.
Wang, J., Zhang, L., Dai, A., Van Hove, T., and Van Baelen, J.: A
near-global, 2-hourly data set of atmospheric precipitable water from
ground-based GPS measurements, J. Geophys. Res., 112, D11107, https://doi.org/10.1029/2006jd007529, 2007.
Wehrli, C.: Calibrations of filter radiometers for determination of
atmospheric optical depth, Metrologia, 37, 419, https://doi.org/10.1088/0026-1394/37/5/16, 2003.
WMO: WMO/GAW Experts Workshop on a Global Surface-Based Network for Long
Term Observations of Column Aerosol Optical Properties, GAW Report No. 162,
WMO TD No. 1287, available at: https://library.wmo.int/doc_num.php?explnum_id=9299 (last access: 6 May 2022), 2005.
WMO: GAW Report-No 231, Fourth WMO Filter Radiometer Comparison (FRC-IV),
Davos, Switzerland, 28 September–16 October 2015, WMO, available at:
https://library.wmo.int/doc_num.php?explnum_id=3369 (last access: 6 May
2022), 2016.
Short summary
We established a spectral-fitting method to derive precipitable water vapor (PWV) and aerosol optical depth based on a strict radiative transfer theory by the spectral measurements of direct sun from EKO MS711 and MS712 spectroradiometers. The retrievals were compared with that of the colocated CE-318 photometer; the results showed a high degree of consistency. In the PWV inversion, a strong water vapor absorption band around 1370 nm is introduced to retrieve PWV in a relatively dry atmosphere.
We established a spectral-fitting method to derive precipitable water vapor (PWV) and aerosol...
