Integrated satellite and groundbased regional monitoring of atmospheric aerosol by lidar and radiometric systems using data assimilation

1Miatselskaya, NS, Bril, AI, 1Chaikovsky, AP, 1Fedarenka, AS, 2Milinevsky, GP
1B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus, Minsk, Belarus
2Main Astronomical Observatory of the National Academy of Sciences of Ukraine, Kyiv, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Space Sci. & Technol. 2020, 26 ;(1):59-71
https://doi.org/10.15407/knit2020.01.059
Publication Language: Russian
Abstract: 
The data processing algorithm for combined ground-based measurements by lidars and radiometers is modified for complex data processing of ground-based radiometers and the space lidar. This allows one to expand the scope of the lidar-radiometric sensing method. In the present work, collocated measurements by AERONET network radiometers and CALIOP/CALIPSO space lidar were used for complex data processing. The CALIOP lidar backscatter signals were averaged over a sample of individual lidar measurements on a segment of the satellite trajectory in the vicinity of the AERONET station. The length of the segment was approximately 100...200 km, which corresponds to samples of 300...600 measurements. This makes it possible to reduce the contribution of noise to the CALIOP signals significantly. To validate this approach, an integrated LRS experiment was conducted, in which collocated ground-based multi-wave lidar measurements, CALIOP lidar measurements, and AERONET radiometric measurements were used. For further expansion of the lidar-radiometric sensing method scope, optimal interpolation was implemented using both the observations at the AERONET stations and the simulations by the GEOS-Chem chemical transport model.
Keywords: aerosol, chemical transport model GEOS-Chem, lidar-radiometric sounding, optimal interpolation
References: 
1. Gandin L. S. (1963). An Objective analysis of meteorological fields. Leningrad: Gidrometeorologicheskoye izdatel’stvo [in Russian].
2. Kolmogorov A. N. (1941). Interpolation and extrapolation of stationary random sequences. Izv. AN SSSR. Ser. matemat., 5 (1), 3—14 [in Russian].
3. Yaglom A. M. (1952). Introduction to the theory of stationary random functions. Uspekhi matem. nauk, 7 (5), 3—168 [in Russian].
4. Bey I., Jacob D. J., Yantosca R. M., et al. (2001). Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation. J. Geophys. Res., 106(D19), 23073—23095.
5. Chaikovsky A., Bril A., Dubovik O., et al. (2004). CIMEL and multiwavelength lidar measurements for troposphere aerosol altitude distributions investigation, long-range transfer monitoring and regional ecological problems solution: field validation of retrieval techniques. Optica Pura y Aplicada, 37, 3241—3246.
6. Chaikovsky A., Chaikovskaya L., Denishchik-Nelubina N., et al. (2018). Lidar & radiometer inversion code (LIRIC) for synergetic processing of EARLINET, AERONET and CALIPSO lidar data. EPJ Web of Conf., 176, 08007.
7. Chaikovsky A., Dubovik O., Holben B., et al. (2016). 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., 9, 1181—1205.
8. Dubovik O., King M. (2000). A Flexible Inversion Algorithm for Retrieval of Aerosol Optical Properties from Sun and Sky Radiance Measurements. J. Geophys. Res., 105(D16), 20673—20696.
9. Dubovik O., Smirnov A., Holben B. N., et al. (2000). Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET): Sun and sky radiance measurements. J. Geophys. Res.: Atmospheres, 105(D8), 9791—9806.
10. Ford B., Heald C. L. (2013). Aerosol loading in the Southeastern United States: reconciling surface and satellite observations. Atmos. Chem. Phys, 13, 9269—9283.
11. GEOS-5 system. URL: http://gmao.gsfc.nasa.gov/systems/geos5/ (Last accesed 06.08.2019).
12. Granados–Muñoz M. J., Guerrero Rascado J. L., Bravo–Aranda J. A., et al. (2014). Retrieving aerosol microphysical properties by Lidar — Radiometer Inversion Code (LIRIC) for different aerosol types. J. Geophys. Res.: Atmospheres, 119, 4836—4858.
13. Holben B. N., Eck T. F., Slutsker I., et al. (1998). AERONET — A federated instrument network and data archive for aerosol characterization. Remote sensing of env., 66 (1), 1—16.
14. Jo D. S., et al. (2013). Effects of chemical aging on global secondary organic aerosol using the volatility basis set approach. Atmos. Environ., 81, 230—244.
15. Miatselskaya N., Kabashnikov V., Milinevsky G., et al. (2016). Atmospheric aerosol distribution in the Belarus-Ukraine region by the GEOS-Chem model and AERONET measurements. Int. J. Remote Sensing, 37(14), 3181—3195.
16. Pye H. O. T., Chan A. W. H., Barcley M. P., et al. (2010). Global modeling of organic aerosol: the importance of reactive nitrogen (NOx and NO3). Atmos. Chem. Phys., 10, 11261—11276.
17. Shenshen L., Garay M. J., Chen L., et al. (2013). Comparison of GEOS-Chem aerosol optical depth with AERONET and MISR data over the contiguous United States. J. Geophys. Res., 118, 1—14.
18. Tackett J. L., Winker D. M., Getzewich B. J., et al. (2018). CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design. Atmos. Meas. Tech., 11, 4129—4152.
19. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). URL:: http://wwwcalipso.larc.nasa. gov/ (Last accesed 06.08.2019).
20. Wagner J., Ansmann A., Wandinger U., et al. (2013). Evaluation of the Lidar/Radiometer Inversion Code (LIRIC) to determine microphysical properties of volcanic and desert dust. Atmos. Meas. Tech., 6, 1707—1724.
21. Wiener N. (1949). Extrapolation, interpolation and smoothing of stationary time series. New York.
22. Yu F. (2011). A secondary organic aerosol formation model considering successive oxidation aging and kinetic condensation of organic compounds: global scale implications. Atmos. Chem. Phys, 11, 1083—1099.