Cloud Fraction Retrieval of EMI and Comparison
With TROPOMI

Abstract

Abstract: As cloud parameters are important input parameters for retrieval of trace gases, it is of great significance for their accurate acquisition. Based on environmental trace gas monitoring instrument (EMI) observation on March 2019, the effective cloud fraction is retrieved by using the absorption feature of O4 at 477 nm. To verify the accuracy of EMI cloud algorithm, the retrieval cloud fraction of EMI is compared with TROPOMI cloud products, and the correlation analysis of EMI and TROPOMI cloud fraction on March 2, 6, 9 and 10, 2019 is carried out, and the cloud fraction correlation coefficient (R) is 0.752, 0.712, 0.764, and 0.762 respectively, indicating that the two results have good correlation. Furthermore, the cloud fractions of three different regions of desert, ocean, and land are selected to study the distribution characteristics of cloud fraction in different underlying surface. It is found that the cloud fractions of EMI and TROPOMI in the three regions are consistent, and the cloud fraction over the ocean is relatively low, the cloud fraction over the land is relatively high, and the cloud fraction over the desert changes frequently.

Key words: cloud, environmental trace gas monitoring instrument, effective cloud fraction, TROPOMI

CLC Number:

X87
X511

References

[1] Rossow W B, Schiffer R A. Advances in understanding clouds from ISCCP[J]. Bulletin of the American Meteorological Society, 1999, 80(11):2261-2287
[2] Kuze A, Chance K V. Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands[J]. Journal of Geophysical Research: Atmospheres, 1994, 99(D7):14481-14491
[3] Loyola D, Ruppert T. A new PMD cloud-recognition algorithm for GOME[J]. ESA Earth Observation Quarterly, 1998, 58: 45-47.
[4] Koelemeijer R B A, Stammes P, Hovenier J W, et al. Global distributions of effective cloud fraction and cloud top pressure derived from oxygen A band spectra measured the Global Ozone Monitoring Experiment: Comparison to ISCCP data[J]. Journal of Geophysical Research, 2002, 107(D12): 4151.
[5] Fournier N, Stammes P, de Graaf M, et al. Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements [J]. Atmospheric Chemistry and Physics, 2006, 6:163–172.
[6] Wang P, Stammes P, van der A R, et al. FRESCO+: an improved O2 A-band cloud retrieval algorithm for tropospheric trace gas retrievals[J]. Atmospheric Chemistry and Physics, 2008, 8: 6565-6576.
[7] Wang P, Stammes P. Evaluation of SCIAMACHY Oxygen A band cloud heights using Cloudnet measurements[J]. Atmospheric Measurement Techniques, 2014, 7: 1331-2014.
[8] Loyola D, Thomas W, Livschitz Y, et al. Cloud properties derived from GOME/ERS-2 backscatter data for trace gas retrieval [J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(9): 2747–2758.
[9] Rozanov V V. Semianalytical cloud retrieval algorithm as applied to the cloud top altitude and die cloud geometrical thickness determination from top-of-atmosphere reflectance measurements in the oxygen A band[J]. Journal of Geophysical Research: Atmospheres, 2004, 109(D5):1-21.
[10] Acarreta J R, De Haan J F, Stammes P. Cloud pressure retrieval using the O4 absorption band at 477 nm[J], Journal of Geophysical Research:Atmospheres, 2004,109(D5):1-11.
[11] Joiner J, Vasilkov A P, Flittner D E, et al. Retrieval of cloud pressure and chlorophyll content using Raman scattering in GOME ultraviolet spectra[J]. Journal of Geophysical Research:Atmospheres, 2004, 109(D1): 1-13.
[12] Veefkind J P, de Haan J F, Sneep M, et al. Improvements to the OMI operational cloud algorithm and comparisons with ground-based radar-lidar observations[J]. Atmospheric Measurement Techniques, 2016, 9(12): 6035-6049.
[13] Loyola D G, Gimeno García S, Lutz R, et al. The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor[J]. Atmospheric Measurement Techniques, 2018, 11: 409-427.
[14] Loyola D, Lutz R, Argyrouli A, et al. S5P/TROPOMI ATBD Cloud Products [EB/OL]. (2018-04-30) [2019-11-25]https://sentinels.copernicus.eu/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-Clouds.
[15] Zhao M J, Si F Q, Zhou H J, et al. Preflight calibration of the Chinese Environmental Trace Gases Monitoring Instrument (EMI)[J]. Atmospheric Measurement Techniques, 2018, 11: 5403–5419.
[16] Yang Taiping, Si Fuqi, PingWang, et al. Research on Cloud Fraction Inversion Algorithm of Environmental Trace Gas Monitoring Instrument [J]. Acta Optica Sinica, 2020, 40(9): 0901001 (in Chinese)
杨太平, 司福祺, PingWang, 等. 大气痕量气体差分吸收光谱仪云量反演算法研究[J]. 光学学报, 2020, 40(9): 0901001

Cloud Fraction Retrieval of EMI and Comparison With TROPOMI

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