Comparison of Columnar, Surface, and UAS Profiles of Absorbing Aerosol Optical Depth and Single-Scattering Albedo in South-East Poland
Abstract
:1. Introduction
2. Instrumentation
2.1. In-Situ
2.1.1. Aethalometer AE-51
2.1.2. Aethalometer AE-31
2.1.3. Nephelometer Aurora 4000
2.1.4. Photoacoustic Extinctiometer (PAX)
2.2. Remote Sensing
2.2.1. The Near-Range Aerosol Raman Lidar Receiver—NARLa
2.2.2. Aerosol Tropospheric Lidar
2.2.3. Ceilometer
2.2.4. Sunphotometer
2.3. Vertical Profiler
3. Methods
3.1. Absorption
3.2. SSA
3.3. Boundary Layer Height Estimation
3.4. AAOD Estimation Based on In-Situ and Lidar Measurements
4. Results
4.1. Comparison of In-Situ Surface and AERONET Photometric Columnar Absorption Quantities
4.2. Comparison of Surface and Low-Level (Drone) In-Situ Measurements of Absorption Coefficient
4.3. Comparison of SSA Retrieved with Different Methods
4.4. AAOD Estimated from In-Situ and Lidar Measurements
5. Summary
- We found moderate correlation between AERONET AOD and AAOD (Pearson coefficient ≈0.47 ± 0.08 for lev. 1.5 and ≈0.56 ± 0.25 for lev. 2.0). AAOD correlated with ground in-situ absorption coefficient measurements gave very similar results (Pearson coefficient ≈0.44 ± 0.09 for lev. 1.5 and ≈0.59 ± 0.24 for lev. 2.0). However, significantly higher correlations were found for AOD with ground-based in-situ extinction coefficient (r ≈0.77 ± 0.05) and also for ground-based in-situ absorption and scattering (r ≈0.79 ± 0.04). AOD reported by AERONET is much better correlated with ground measurements of scattering and absorption (extinction) than AAOD. AAOD and AOD reported by AERONET over Strzyżów site were weakly correlated, despite high correlation of in-situ ground measurements of extinction and absorption coefficients.
- AAOD reported by AERONET was higher than measured from UAS (in the lowermost 1–1.5 km layer of the troposphere), which was expected due to a comparison of the whole atmosphere AAOD with one only in the lowest layer. There was no statistical correlation between measurements, however due to limited availability of data from AERONET estimations are insignificant. There were cases when AAOD measured in PBL was higher than reported by AERONET, which suggest possible underestimation of absorption in photometric measurements, under particular conditions. Based on lidar observations contribution of aerosols located in PBL to total AAOD was estimated to ≈37%.
- AAOD measurements from drone correlating with ground-based measurements of absorption showed highest correlations (r ≈ 0.70) during daytime (06–18 UTC). Night time correlation was slightly lower (r ≈ 0.63). Results fit well into expectations, when during convective day conditions, mixing in PBL is stronger and difference between ground and PBL smaller. During night, especially if temperature inversion occurs, layered and inhomogeneous conditions are possible in PBL, which result in lower correlation with ground measurements.
- Correlation for the lower 500 m of profile (r ≈ 0.77 ± 0.10) with ground measurements was according to expectations much higher than correlation for layer above 500 m above ground (r ≈ 0.49 ± 0.18). Correlation of ground measurement with absorption in a layers decrease with altitude, which was checked for 21 layers in the range of UAS flight.
- Comparison of SSA level 2.0 data from AERONET and surface in-situ SSA on 525 nm shows weak correlation and higher values of columnar data by ≈0.02, which mainly could be explained by hygroscopic effects. Ground measurements were done in dry conditions, while columnar data represent ambient conditions. For such reason correction for ambient condition with mean hygroscopic growth factor was applied. Correction reduced difference between both types of data. Difference is the smallest for AOD@441nm higher than 0.35 (≈0.005). This result confirms validity of using correction for ambient conditions.
- Ground-based in-situ SSA measurements (aethalometer + nephelometer) and vertical in-situ SSA profiles (aethalometer on a drone + lidar) were juxtaposed with AERONET retrieval. Both in-situ methods with aethalometer gave quite similar mean values (≈0.92 ± 0.01 on ground and ≈0.92±0.02 in profile), with two times wider confidence interval for drone measurements. For the same period AERONET reported SSA at level ≈0.97 ± <0.01, which confirmed smaller contribution of absorption in AERONET retrieval in comparison to in-situ measurements.
- Very limited availability of AERONET AAOD measurements was the reason for verification of simple methods for estimation of AAOD. Proposed methods base on AOD scaling by SSA retrieved from different methods (ground measurements, drone profiles and lidar/ceilometer data).
- Obtained values of AAOD were correlated with AAOD delivered by AERONET and from combined drone and lidar measurement, but receiving statistically significant correlations for it was difficult due to small sample of AERONET data. Significant correlations were achieved for all estimations with AAOD measured from drone and lidar. The best correlation was achieved for AAOD estimated as AOD scaled by SSA from lidar (r ≈ 0.94 ± 0.04) with lowest RMSE (0.0022). The weakest correlations were observed for methods with the integration of the absorption coefficient measured on the ground up to PBL/ABL (almost the same for both) with r ≈ 0.39 ± 0.27 and RMSE ≈ 0.0063. All AERONET correlations were insignificant, but it could be mentioned that the highest correlation coefficient was found for the integration of the absorption up to the PBL top (r ≈ 0.32).
- For clean atmosphere there are high systematic uncertainties of AAOD and SSA on level 1.5 and there are no data for level 2.0. Therefore, for clean atmosphere (AOD below 0.2) there is overestimation of absorption on level 1.5, which results in the underestimation of SSA (0.946 on lev. 1.5 and 0.966 on lev. 2.0)
- In five years, analysis mean AAOD from level 2.0 is overestimated due to threshold, whose filter cases with smaller amount of aerosols. For AAOD@441nm at SolarAOT station, values from level 2.0 (0.0182) are twice as high than for level 1.5 (0.0089).
- Both levels are weakly correlated with in-situ measurements (ground and drone based). Additionally, results delivered by AERONET show, under particular conditions, possible underestimation of aerosols absorption. It was especially visible in cases where directly measured AAOD in PBL was higher than AAOD reported by AERONET. The mentioned issue is a premise for further investigation on the subject and further development of absorbing aerosols vertical profiling research, together with their results verification with collocated AERONET data.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013; p. 1535. [Google Scholar]
- Koch, D.; Del Genio, A.D. Black carbon semi-direct effects on cloud cover: Review and synthesis. Atmos. Chem. Phys. 2010, 10, 7685–7696. [Google Scholar] [CrossRef]
- Ramanathan, V.; Carmichael, G. Global and regional climate changes due to black carbon. Nat. Geosci. 2008, 1, 221–227. [Google Scholar] [CrossRef]
- Bond, T.C.; Doherty, S.J.; Fahey, D.W.; Forster, P.M.; Berntsen, T.; DeAngelo, B.J.; Flanner, M.G.; Ghan, S.; Karcher, B.; Koch, D.; et al. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res.-Atmos. 2013, 118, 5380–5552. [Google Scholar] [CrossRef]
- Samset, B.H.; Myhre, G.; Schulz, M.; Balkanski, Y.; Bauer, S.; Berntsen, T.K.; Bian, H.; Bellouin, N.; Diehl, T.; Easter, R.C.; et al. Black carbon vertical profiles strongly affect its radiative forcing uncertainty. Atmos. Chem. Phys. 2013, 13, 2423–2434. [Google Scholar] [CrossRef] [Green Version]
- Cook, J.; Highwood, E.J. Climate response to tropospheric absorbing aerosols in an intermediate general-circulation model. Q. J. R. Meteorol. Soc. 2004, 130, 175–191. [Google Scholar] [CrossRef] [Green Version]
- Choi, J.O.; Chung, C.E. Sensitivity of aerosol direct radiative forcing to aerosol vertical profile. Tellus Ser. B-Chem. Phys. Meteorol. 2014, 66. [Google Scholar] [CrossRef]
- Ban-Weiss, G.A.; Cao, L.; Bala, G.; Caldeira, K. Dependence of climate forcing and response on the altitude of black carbon aerosols. Clim. Dyn. 2012, 38, 897–911. [Google Scholar] [CrossRef]
- Meloni, D.; di Sarra, A.; Di Iorio, T.; Fiocco, G. Influence of the vertical profile of Saharan dust on the visible direct radiative forcing. J. Quant. Spectrosc. Radiat. Transf. 2005, 93, 397–413. [Google Scholar] [CrossRef]
- Johnson, B.T.; Heese, B.; McFarlane, S.A.; Chazette, P.; Jones, A.; Bellouin, N. Vertical distribution and radiative effects of mineral dust and biomass burning aerosol over West Africa during DABEX. J. Geophys. Res.-Atmos. 2008, 113. [Google Scholar] [CrossRef]
- Gomez-Amo, J.L.; di Sarra, A.; Meloni, D.; Cacciani, M.; Utrillas, M.P. Sensitivity of shortwave radiative fluxes to the vertical distribution of aerosol single scattering albedo in the presence of a desert dust layer. Atmos. Environ. 2010, 44, 2787–2791. [Google Scholar] [CrossRef]
- Holben, B.N.; Eck, T.F.; Slutsker, I.; Tanre, D.; Buis, J.P.; Setzer, A.; Vermote, E.; Reagan, J.A.; Kaufman, Y.J.; Nakajima, T.; et al. AERONET–A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 1998, 66, 1–16. [Google Scholar] [CrossRef]
- Dubovik, O.; King, M.D. A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. J. Geophys. Res.-Atmos. 2000, 105, 20673–20696. [Google Scholar] [CrossRef] [Green Version]
- Andrews, E.; Ogren, J.A.; Kinne, S.; Samset, B. Comparison of AOD, AAOD and column single scattering albedo from AERONET retrievals and in situ profiling measurements. Atmos. Chem. Phys. 2017, 17, 6041–6072. [Google Scholar] [CrossRef] [Green Version]
- Schafer, J.S.; Eck, T.F.; Holben, B.N.; Thornhill, K.L.; Anderson, B.E.; Sinyuk, A.; Giles, D.M.; Winstead, E.L.; Ziemba, L.D.; Beyersdorf, A.J.; et al. Intercomparison of aerosol single-scattering albedo derived from AERONET surface radiometers and LARGE in situ aircraft profiles during the 2011 DRAGON-MD and DISCOVER-AQ experiments. J. Geophys. Res.-Atmos. 2014, 119, 7439–7452. [Google Scholar] [CrossRef] [Green Version]
- Saturno, J.; Pohlker, C.; Massabo, D.; Brito, J.; Carbone, S.; Cheng, Y.F.; Chi, X.G.; Ditas, F.; de Angelis, I.H.; Moran-Zuloaga, D.; et al. Comparison of different Aethalometer correction schemes and a reference multi-wavelength absorption technique for ambient aerosol data. Atmos. Meas. Tech. 2017, 10, 2837–2850. [Google Scholar] [CrossRef] [Green Version]
- Weingartner, E.; Saathoff, H.; Schnaiter, M.; Streit, N.; Bitnar, B.; Baltensperger, U. Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers. J. Aerosol Sci. 2003, 34, 1445–1463. [Google Scholar] [CrossRef]
- Schmid, O.; Artaxo, P.; Arnott, W.P.; Chand, D.; Gatti, L.V.; Frank, G.P.; Hoffer, A.; Schnaiter, M.; Andreae, M.O. Spectral light absorption by ambient aerosols influenced by biomass burning in the Amazon Basin. I: Comparison and field calibration of absorption measurement techniques. Atmos. Chem. Phys. 2006, 6, 3443–3462. [Google Scholar] [CrossRef] [Green Version]
- White, W.H.; Trzepla, K.; Hyslop, N.P.; Schichtel, B.A. A critical review of filter transmittance measurements for aerosol light absorption, and de novo calibration for a decade of monitoring on PTFE membranes. Aerosol Sci. Technol. 2016, 50, 984–1002. [Google Scholar] [CrossRef]
- Coen, M.C.; Weingartner, E.; Apituley, A.; Ceburnis, D.; Fierz-Schmidhauser, R.; Flentje, H.; Henzing, J.S.; Jennings, S.G.; Moerman, M.; Petzold, A.; et al. Minimizing light absorption measurement artifacts of the Aethalometer: Evaluation of five correction algorithms. Atmos. Meas. Tech. 2010, 3, 457–474. [Google Scholar] [CrossRef]
- Bueno, P.A.; Havey, D.K.; Mulholland, G.W.; Hodges, J.T.; Gillis, K.A.; Dickerson, R.R.; Zachariah, M.R. Photoacoustic Measurements of Amplification of the Absorption Cross Section for Coated Soot Aerosols. Aerosol Sci. Technol. 2011, 45, 1217–1230. [Google Scholar] [CrossRef]
- Markowicz, K.M.; Ritter, C.; Lisok, J.; Makuch, P.; Stachlewska, I.S.; Cappelletti, D.; Mazzola, M.; Chilinski, M.T. Vertical variability of aerosol single-scattering albedo and equivalent black carbon concentration based on in-situ and remote sensing techniques during the iAREA campaigns in Ny-Alesund. Atmos. Environ. 2017, 164, 431–447. [Google Scholar] [CrossRef]
- Ferrero, L.; Cappelletti, D.; Busetto, M.; Mazzola, M.; Lupi, A.; Lanconelli, C.; Becagli, S.; Traversi, R.; Caiazzo, L.; Giardi, F.; et al. Vertical profiles of aerosol and black carbon in the Arctic: A seasonal phenomenology along 2 years (2011-2012) of field campaigns. Atmos. Chem. Phys. 2016, 16, 12601–12629. [Google Scholar] [CrossRef]
- Villa, T.F.; Gonzalez, F.; Miljievic, B.; Ristovski, Z.D.; Morawska, L. An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives. Sensors 2016, 16. [Google Scholar] [CrossRef] [PubMed]
- Corrigan, C.E.; Roberts, G.C.; Ramana, M.V.; Kim, D.; Ramanathan, V. Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles. Atmos. Chem. Phys. 2008, 8, 737–747. [Google Scholar] [CrossRef] [Green Version]
- Chilinski, M.T.; Markowicz, K.M.; Markowicz, J. Observation of vertical variability of black carbon concentration in lower troposphere on campaigns in Poland. Atmos. Environ. 2016, 137, 155–170. [Google Scholar] [CrossRef]
- Chiliński, M.T.; Markowicz, K.M.; Kubicki, M. UAS as a Support for Atmospheric Aerosols Research: Case Study. Pure Appl. Geophys. 2018. [Google Scholar] [CrossRef]
- Muller, D.; Wandinger, U.; Ansmann, A. Microphysical particle parameters from extinction and backscatter lidar data by inversion with regularization: theory. Appl. Opt. 1999, 38, 2346–2357. [Google Scholar] [CrossRef] [PubMed]
- Veselovskii, I.; Kolgotin, A.; Griaznov, V.; Muller, D.; Wandinger, U.; Whiteman, D.N. Inversion with regularization for the retrieval of tropospheric aerosol parameters from multiwavelength lidar sounding. Appl. Opt. 2002, 41, 3685–3699. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Veselovskii, I.; Kolgotin, A.; Griaznov, V.; Muller, D.; Franke, K.; Whiteman, D.N. Inversion of multiwavelength Raman lidar data for retrieval of bimodal aerosol size distribution. Appl. Opt. 2004, 43, 1180–1195. [Google Scholar] [CrossRef]
- Kolgotin, A.; Muller, D. Theory of inversion with two-dimensional regularization: Profiles of microphysical particle properties derived from multiwavelength lidar measurements. Appl. Opt. 2008, 47, 4472–4490. [Google Scholar] [CrossRef]
- Chaikovsky, A.; Dubovik, O.; Holben, B.; Bril, A.; Goloub, P.; Tanre, D.; Pappalardo, G.; Wandinger, U.; Chaikovskaya, L.; Denisov, S.; et al. Lidar-Radiometer Inversion Code (LIRIC) for the retrieval of vertical aerosol properties from combined lidar/radiometer data: Development and distribution in EARLINET. Atmos. Meas. Tech. 2016, 9, 1181–1205. [Google Scholar] [CrossRef]
- Tsekeri, A.; Amiridis, V.; Lopatin, A.; Marinou, E.; Giannakaki, E.; Pikridas, M.; Sciare, J.; Liakakou, E.; Gerasopoulos, E.; Duesing, S.; et al. Aerosol absorption profiling from the synergy of lidar and sun-photometry: The ACTRIS-2 campaigns in Germany, Greece and Cyprus. EPJ Web Conf. 2018, 176, 08005. [Google Scholar] [CrossRef]
- Hansen, A.D.A.; Rosen, H.; Novakov, T. The aethalometer–an instrument for the real-time measurement of optical-absorption by aerosol-particles. Sci. Total Environ. 1984, 36, 191–196. [Google Scholar] [CrossRef]
- Segura, S.; Estelles, V.; Titos, G.; Lyamani, H.; Utrillas, M.P.; Zotter, P.; Prevot, A.S.H.; Mocnik, G.; Alados-Arboledas, L.; Martinez-Lozano, J.A. Determination and analysis of in situ spectral aerosol optical properties by a multi-instrumental approach. Atmos. Meas. Tech. 2014, 7, 2373–2387. [Google Scholar] [CrossRef] [Green Version]
- Chamberlain-Ward, S.; Sharp, F. Advances in Nephelometry through the Ecotech Aurora Nephelometer. Sci. World J. 2011, 11, 2530–2535. [Google Scholar] [CrossRef] [PubMed]
- Muller, T.; Laborde, M.; Kassell, G.; Wiedensohler, A. Design and performance of a three-wavelength LED-based total scatter and backscatter integrating nephelometer. Atmos. Meas. Tech. 2011, 4, 1291–1303. [Google Scholar] [CrossRef] [Green Version]
- Anderson, T.L.; Ogren, J.A. Determining Aerosol Radiative Properties Using the TSI 3563 Integrating Nephelometer. Aerosol Sci. Technol. 1998, 29, 57–69. [Google Scholar] [CrossRef] [Green Version]
- Truex, T.J.; Anderson, J.E. Mass monitoring of carbonaceous aerosols with a spectrophone. Atmos. Environ. 1979, 13, 507–509. [Google Scholar] [CrossRef]
- Stachlewska, I.S.; Markowicz, K.M.; Ritter, C.; Neuber, R.; Heese, B.; Engelmann, R.; Linne, H. Near-range receiver unit of next generation pollyxt used with koldeway aerosol raman lidar in arctic. EPJ Web Conf. 2016, 119, 06015. [Google Scholar] [CrossRef]
- Engelmann, R.; Kanitz, T.; Baars, H.; Heese, B.; Althausen, D.; Skupin, A.; Wandinger, U.; Komppula, M.; Stachlewska, I.S.; Amiridis, V.; et al. The automated multiwavelength Raman polarization and water-vapor lidar Polly(XT): The neXT generation. Atmos. Meas. Tech. 2016, 9, 1767–1784. [Google Scholar] [CrossRef]
- Klett, J.D. Stable analytical inversion solution for processing lidar returns. Appl. Opt. 1981, 20, 211–220. [Google Scholar] [CrossRef] [Green Version]
- Klett, J.D. Lidar inversion with variable backscatter extinction ratios. Appl. Opt. 1985, 24, 1638–1643. [Google Scholar] [CrossRef]
- Fernald, F.G. Analysis of atmospheric lidar observations - some comments. Appl. Opt. 1984, 23, 652–653. [Google Scholar] [CrossRef]
- Stachlewska, I.S.; Piadlowski, M.; Migacz, S.; Szkop, A.; Zielinska, A.J.; Swaczyna, P.L. Ceilometer Observations of the Boundary Layer over Warsaw, Poland. Acta Geophys. 2012, 60, 1386–1412. [Google Scholar] [CrossRef]
- Markowicz, K.M.; Chilinski, M.T.; Lisok, J.; Zawadzka, O.; Stachlewska, I.S.; Janicka, L.; Rozwadowska, A.; Makuch, P.; Pakszys, P.; Zielinski, T.; et al. Study of aerosol optical properties during long-range transport of biomass burning from Canada to Central Europe in July 2013. J. Aerosol Sci. 2016, 101, 156–173. [Google Scholar] [CrossRef]
- Giles, D.M.; Sinyuk, A.; Sorokin, M.G.; Schafer, J.S.; Smirnov, A.; Slutsker, I.; Eck, T.F.; Holben, B.N.; Lewis, J.R.; Campbell, J.R.; et al. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database–automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmos. Meas. Tech. 2019, 12, 169–2019. [Google Scholar] [CrossRef]
- Hagler, G.S.W.; Yelverton, T.L.B.; Vedantham, R.; Hansen, A.D.A.; Turner, J.R. Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data. Aerosol Air Qual. Res. 2011, 11, 539–546. [Google Scholar] [CrossRef] [Green Version]
- Arnott, W.P.; Hamasha, K.; Moosmuller, H.; Sheridan, P.J.; Ogren, J.A. Towards aerosol light-absorption measurements with a 7-wavelength Aethalometer: Evaluation with a photoacoustic instrument and 3-wavelength nephelometer. Aerosol Sci. Technol. 2005, 39, 17–29. [Google Scholar] [CrossRef]
- Ferrero, L.; Mocnik, G.; Ferrini, B.S.; Perrone, M.G.; Sangiorgi, G.; Bolzacchini, E. Vertical profiles of aerosol absorption coefficient from micro-Aethalometer data and Mie calculation over Milan. Sci. Total Environ. 2011, 409, 2824–2837. [Google Scholar] [CrossRef]
- Ran, L.; Deng, Z.Z.; Xu, X.B.; Yan, P.; Lin, W.L.; Wang, Y.; Tian, P.; Wang, P.C.; Pan, W.L.; Lu, D.R. Vertical profiles of black carbon measured by a micro-aethalometer in summer in the North China Plain. Atmos. Chem. Phys. 2016, 16, 10441–10454. [Google Scholar] [CrossRef] [Green Version]
- Janicka, L.; Stachlewska, I.S. Properties of biomass burning aerosol mixtures derived at fine temporal and spatial scales from Raman lidar measurements: Part I optical properties. Atmos. Chem. Phys. Discuss. 2019, 1–46. [Google Scholar] [CrossRef]
- Janicka, L.; Stachlewska, I.S.; Veselovskii, I.; Baars, H. Temporal variations in optical and microphysical properties of mineral dust and biomass burning aerosol derived from daytime Raman lidar observations over Warsaw, Poland. Atmos. Environ. 2017, 169, 162–174. [Google Scholar] [CrossRef]
- Stachlewska, I.S.; Costa-Surós, M.; Althausen, D. Raman lidar water vapor profiling over Warsaw, Poland. Atmos. Res. 2017, 194, 258–267. [Google Scholar] [CrossRef]
- Brooks, I.M. Finding boundary layer top: Application of a wavelet covariance transform to lidar backscatter profiles. J. Atmos. Ocean. Technol. 2003, 20, 1092–1105. [Google Scholar] [CrossRef]
- Baars, H.; Ansmann, A.; Engelmann, R.; Althausen, D. Continuous monitoring of the boundary-layer top with lidar. Atmos. Chem. Phys. 2008, 8, 7281–7296. [Google Scholar] [CrossRef] [Green Version]
- Seibert, P.; Beyrich, F.; Gryning, S.E.; Joffre, S.; Rasmussen, A.; Tercier, P. Review and intercomparison of operational methods for the determination of the mixing height. Atmos. Environ. 2000, 34, 1001–1027. [Google Scholar] [CrossRef]
- Zhang, L.; Henze, D.K.; Grell, G.A.; Torres, O.; Jethva, H.; Lamsal, L.N. What factors control the trend of increasing AAOD over the United States in the last decade? J. Geophys. Res. Atmos. 2017, 122, 1797–1810. [Google Scholar] [CrossRef]
- Konovalov, I.B.; Lvova, D.A.; Beekmann, M.; Jethva, H.; Mikhailov, E.F.; Paris, J.D.; Belan, B.D.; Kozlov, V.S.; Ciais, P.; Andreae, M.O. Estimation of black carbon emissions from Siberian fires using satellite observations of absorption and extinction optical depths. Atmos. Chem. Phys. 2018, 18, 14889–14924. [Google Scholar] [CrossRef] [Green Version]
- Szczepanik, D.; Markowicz, K.M. The relation between columnar and surface aerosol optical properties in a background environment. Atmos. Pollut. Res. 2018, 9, 246–256. [Google Scholar] [CrossRef]
- Zawadzka, O.; Markowicz, K.; Pietruczuk, A.; Zielinski, T.; Jaroslawski, J. Impact of urban pollution emitted in Warsaw on aerosol properties. Atmos. Environ. 2013, 69, 15–28. [Google Scholar] [CrossRef]
AERONET Level 1.5 | AERONET Level 2.0 | |||||||
---|---|---|---|---|---|---|---|---|
[nm] | 440 | 525 | 675 | 870 | 440 | 525 | 675 | 870 |
AOD | - | - | - | - | 0.280 | 0.216 | 0.134 | 0.086 |
AOD | 0.288 | 0.213 | 0.139 | 0.09 | 0.571 | 0.421 | 0.273 | 0.168 |
AAOD | 0.011 | 0.01 | 0.008 | 0.007 | 0.018 | 0.015 | 0.012 | 0.01 |
SSA | 0.955 | 0.947 | 0.934 | 0.914 | 0.967 | 0.962 | 0.954 | 0.939 |
SSA[RH] | 0.938 | 0.939 | 0.932 | 0.928 | 0.942 | 0.942 | 0.934 | 0.929 |
SSA[RH] | 0.947 | 0.948 | 0.942 | 0.938 | 0.953 | 0.953 | 0.946 | 0.942 |
SSA[<RH>] | 0.965 | 0.966 | 0.962 | 0.959 | 0.970 | 0.971 | 0.966 | 0.964 |
Absorption Coefficient on Ground + Anomaly (2013–2017) | ||||
---|---|---|---|---|
Absorption [Mm] | June–August | June | July | August |
ABS@520 nm | 3.80 | 3.00 | 2.60 | 5.80 |
dABS@520 nm | +0.70 | +0.43 | −0.04 | +1.72 |
ABS@870 nm | 2.01 | 1.41 | 1.41 | 3.04 |
dABS@870 nm | +0.32 | +0.11 | −0.05 | +0.89 |
AERONET AOD@500 nm + 2013–2017 + Anomaly | ||||
AOD | June–August | June | July | August |
2013–2017 | 0.186 | 0.180 | 0.179 | 0.196 |
2015 | 0.233 | 0.202 | 0.189 | 0.282 |
dAOD | +0.05 | +0.02 | +0.01 | +0.09 |
Absorption Coefficient during Flights in 2015 | ||||
Absorption [Mm] | All Profiles | June | July | August |
on ground | 2.74 ± 0.42 [1.00] | 1.73 ± 0.33 [1.00] | 1.25 ± 0.14 [1.00] | 3.95 ± 0.58 [1.00] |
profile mean | 2.78 ± 0.33 [0.67 ± 0.13] | 1.80 ± 0.58 [—] | 1.51 ± 0.27 [ — ] | 3.78 ± 0.34 [0.73 ± 0.11] |
lower 500 m | 2.62 ± 0.35 [0.77 ± 0.10] | 1.62 ± 0.53 [—] | 1.15 ± 0.24 [ — ] | 3.74 ± 0.34 [0.79 ± 0.09] |
upper 500 m | 3.01 ± 0.35 [0.49 ± 0.18] | 2.26 ± 0.79 [—] | 1.86 ± 0.33 [ — ] | 3.85 ± 0.44 [0.65 ± 0.13] |
Source of SSA Measurement | Mean SSA | std |
---|---|---|
Aethelomter + nephelometer on ground | 0.923 ± 0.006 | 0.020 |
UAS (AE-51) + Lidar | 0.917 ± 0.016 | 0.056 |
SSA Lidar | 0.869 ± 0.025 | 0.085 |
SSA Ceilometer | 0.840 ± 0.034 | 0.115 |
AERONET | 0.974 ± 0.002 | 0.003 |
Only Common Points of AERONET | ||||||
AERONET | UAS + LIDAR | |||||
r | RMSE | bias | r | RMSE | bias | |
Surface SSA | 0.42 [−0.06:0.74] | 0.0041 (0.75) | 0.0011 (0.21) | 0.34 [−0.15:0.7] | 0.0053 (0.64) | 0.004 (0.49) |
Surface SSA | 0.42 [−0.06:0.74] | 0.0043 (0.81) | 0.002 (0.37) | 0.19 [−0.3:0.61] | 0.0061 (0.74) | 0.0049 (0.59) |
Drone SSA | −0.05 [−0.51:0.43] | 0.0072 (1.33) | −0.0043 (−0.81) | 0.77 [0.48:0.91] | 0.0028 (0.34) | −0.0014 (−0.17) |
Drone SSA | −0.05 [−0.51:0.43] | 0.0058 (1.08) | −0.0026 (−0.48) | 0.67 [0.3:0.87] | 0.0027 (0.32) | 0.0003 (0.04) |
SSA Lidar | 0.25 [−0.25:0.64] | 0.0071 (1.32) | −0.0038 (−0.70) | 0.9 [0.74:0.96] | 0.0029 (0.35) | −0.0009 (0.04) |
SSA Ceilometer | 0.006 [−0.42:51] | 0.0106 (1.98) | −0.0063 (−1.16) | 0.79 [0.51:0.92] | 0.0063 (0.77) | −0.0034 (−0.41) |
ABS to PBL | 0.36 [−0.13:0.71] | 0.0056 (1.04) | 0.0038 (0.70) | 0.22 [−0.28:0.62] | 0.0075 (0.91) | 0.0067 (0.81) |
ABS to ABL | −0.01 [−0.48:0.46] | 0.0005 (0.99) | 0.0028 (0.53) | 0.28 [−0.22:0.66] | 0.0067 (0.81) | 0.00057 (0.69) |
UAS + Ceilometer | 0.36 [−0.13:0.71] | 0.0040 (0.75) | 0.0008 (0.14) | 0.66 [0.27:0.86] | 0.0046 (0.55) | 0.0037 (0.44) |
All Data Points | ||||||
AERONET | UAS + LIDAR | |||||
r | RMSE | BIAS | r | RMSE | BIAS | |
Surface SSA | 0.09 [−0.34:0.48] | 0.0051 (0.96) | 0.0004 (0.07) | 0.55 [0.29:0.74] | 0.0043 (0.58) | 0.0025 (0.34) |
Surface SSA | 0.07 [−0.35:0.47] | 0.0045 (0.96) | 0.0012 (0.23) | 0.49 [0.21:0.7] | 0.005 (0.67) | 0.0035 (0.47) |
Drone SSA | −0.15 [−0.53:0.28] | 0.0071 (1.35) | 0.0038 (−0.73) | 0.86 [0.74:0.92] | 0.0026 (0.34) | −0.0012 (−0.16) |
Drone SSA | −0.16 [−0.54:0.27] | 0.006 (1.31) | −0.0023 (−0.44) | 0.8 [0.65:0.89] | 0.0025 (0.33) | 0.0004 (0.06) |
SSA Lidar | 0.17 [−0.26:0.54] | 0.0069 (1.31) | −0.0031 (−0.58) | 0.94 [0.89:0.97] | 0.0022 (0.29) | −0.0007 (−0.09) |
SSA Ceilometer | 0.04 [−0.38:0.45] | 0.0098 (1.86) | −0.005 (−0.95) | 0.89 [0.8:0.94] | 0.0054 (0.73) | −0.0028 (−0.38) |
ABS to PBL | 0.32 [−0.11:0.65] | 0.0056 (1.07) | 0.0037 (0.70) | 0.4 [0.1:0.63] | 0.0037 (0.88) | 0.0053 (0.72) |
ABS to ABL | 0.05 [−0.37:0.45] | 0.0052 (0.99) | 0.0027 (0.51) | 0.38 [0.08:0.62] | 0.0061 (0.82) | 0.0031 (0.42) |
UAS + Ceilometer | 0.08 [−0.35:0.48] | 0.0048 (0.91) | 0.0003 (0.06) | 0.76 [0.58:0.86] | 0.0036 (0.49) | 0.0024 (0.33) |
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Share and Cite
Chiliński, M.T.; Markowicz, K.M.; Zawadzka, O.; Stachlewska, I.S.; Lisok, J.; Makuch, P. Comparison of Columnar, Surface, and UAS Profiles of Absorbing Aerosol Optical Depth and Single-Scattering Albedo in South-East Poland. Atmosphere 2019, 10, 446. https://doi.org/10.3390/atmos10080446
Chiliński MT, Markowicz KM, Zawadzka O, Stachlewska IS, Lisok J, Makuch P. Comparison of Columnar, Surface, and UAS Profiles of Absorbing Aerosol Optical Depth and Single-Scattering Albedo in South-East Poland. Atmosphere. 2019; 10(8):446. https://doi.org/10.3390/atmos10080446
Chicago/Turabian StyleChiliński, Michał T., Krzysztof M. Markowicz, Olga Zawadzka, Iwona S. Stachlewska, Justyna Lisok, and Przemysław Makuch. 2019. "Comparison of Columnar, Surface, and UAS Profiles of Absorbing Aerosol Optical Depth and Single-Scattering Albedo in South-East Poland" Atmosphere 10, no. 8: 446. https://doi.org/10.3390/atmos10080446