Skip to main content
SpringerLink
Log in

Modelling and Analysis of Aerosol and Cloud-Precipitable Water Inter-Hemispheric Interactions of Aerosol-Satellite Data Using Ground Observation

  • Original Paper
  • Published:
Aerosol Science and Engineering Aims and scope Submit manuscript

Abstract

This study investigates the seasonal and spatiotemporal variations of aerosol optical depth (AOD) retrieval algorithm with cloud fraction (CF) and precipitable water (PW) from Ozone Monitoring Instrument (OMI/Aura) as satellite-based and Aeronet Robotic Network (AERONET) as ground-based stations for the period from 2008 to 2019 and optimized from 2020 to 2025 using neural network model. The exact interaction of AOD-CF-PW remains a good area of scientific interest but has remained poorly represented. However, the idea of the statistical approach of data interpolation and extrapolation to extract AOD-CF-PW data gaps using inter-hemispheric approach of the Northern hemisphere (Ilorin 8.484 N, 4.675 E and Saada 31.626 N, 8.156 W) and Southern hemisphere (ICIPE-Mbita 0.432 S, 34.206 E and Skukuza 24.992 S, 31.587 E) of the African continent. An estimation of relative bias error values of both AODs-CF-PW data was validated by correlating the results and performing the standard deviation (SD) analysis. To conclude the interaction error in AOD-CF-PW values, root mean square error (RMSE), relative bias (RD), absolute bias (AD) and mean absolute error (MAE) were performed. Furthermore, to provide knowledge of major aerosols contributors over both hemispheres, an estimation of jet winds to see the zonal and meridional impact through 7 days kinematic back trajectories at various initial pressures was performed. Winter season of Skukuza presents correlation results of OMI-PW (r = 0.125), CF (r = − 0.041) and AERONET-derived-PW (r = − 0.075) and CF (r = − 0.010) with corresponding analysis of (%EE = 22.33), (RMSE = 0.017), (RB = 0.287), (MAE = 0.002), (SD = 0.285), (AD = 0.243). Interestingly, the Skukuza of the southern hemisphere during the month of winter shows the least value of (RMSE = 0.017), indicating good agreement of OMI and AERONET AODs. This result indicated that OMI satellite remote sensing does not give an accountable result of AOD-CF-PW interactions but depends more on geographic terrain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

The data used in this study can be provided upon request.

References

  • Ackerman AS, Kirkpatrick MP, Stevens DE, Toon OB (2004) The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature 432:1014–1017

    Google Scholar 

  • Alam K, Iqbal MJ, Blaschke T, Qureshi S (2010) Monitoring spatio-temporal variations in aerosols and aerosol–cloud interactions over Pakistan using MODIS data Gulzar Khan. Adv Space Res 46:1162–1176

    Google Scholar 

  • Alam K, Sahar N, Iqbal Y (2014) Aerosol characteristics and radioactive forcing during premonsoon and post-monsoon seasons in an urban environment. Aerosol Air Qual Res 14:99–107

    Google Scholar 

  • Angstrom A (1961) Techniques of determining the turbidity of the atmosphere. Tellus A 13:214–223

    Google Scholar 

  • Bender FA, Frey L, McCoy DT, Grosvenor DP, Mohrmann JK (2019) Assessment of aerosol–cloud–radiation correlations in satellite observations, climate models and reanalysis. Clim Dyn 52:4371–4392

    Google Scholar 

  • Benkhalifa J, Léon JF, Chaabane M (2017) Aerosol optical properties of Western Mediterranean basin from multi-year AERONET data. J Atmos Sol-Ter Phys 164:222–228

    Google Scholar 

  • Boiyo R, Kumar KR, Zhao T (2018) Optical, microphysical and radiative properties of aerosols over a tropical rural site in Kenya, East Africa: source identification, modification and aerosol type discrimination. Atmos Environ 177:234–252

    Google Scholar 

  • Boucher O et al (2013) Clouds and aerosols. In: Stocker TF, Qin D, Plattnes G-K, Tignos M, Allen SK, Boshung J, Naules A, Xia Y, Bex V, Midgley PM (eds) 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

    Google Scholar 

  • Bovchaliuk A, Milinevsky G, Danylevsky V, Goloub P, Dubovik O, Holdak A, Sosonkin M (2013) Variability of aerosol properties over Eastern Europe observed from ground and satellites in the period from 2003 to 2011. Atmos Chem Phys 13:6587–6602

    Google Scholar 

  • Che H, Yang L, Liu C, Xia X, Wang Y, Wang H, Zhang X (2019) Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET. Chemos 236:124268

    Google Scholar 

  • Dubovik O, King MD (2000) A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. J Geophys Res 105:20673–20696

    Google Scholar 

  • Dubovik O, Holben BN, Eck TF, Smirnov A, Kaufmann YJ, King MD, Tanre D, Slusker I (2002) Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J Atmos Sci 59:590–608

    Google Scholar 

  • Dubovik O et al (2006) Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. J Geophys Res Atmos 111:D11

    Google Scholar 

  • Elansky NF, Ponomarev NA, Verevkin YM (2018) Air quality and pollutant emissions in the Moscow megacity in 2005–2014. Atmos Environ 175:54–64

    Google Scholar 

  • Fawole O, Cai X, Levine J et al (2016) Detection of gas flaring signature in the AERONET optical properties of aerosols at a tropical station in West Africa. J Geophys Res Atmos 121:14513–14524

    Google Scholar 

  • Filonchyk M, Yan H (2018) Urban air pollution monitoring by ground-based stations and satellite data: multi-season characteristics from Lanzhou City. Springer, Lanzhou City

    Google Scholar 

  • Filonchyk M, Hurynovich V, Yan H, Yang S (2020) Atmospheric pollution assessment near potential source of natural aerosols in the South Gobi Desert region, China. Gisci Rem Sens 57:227–244

    Google Scholar 

  • Founda D, Kazadzis S, Mihalopoulos N, Gerasopoulos E, Lianou M, Raptis PI (2016) Long- term visibility variation in Athens (1931–2013): a proxy for local and regional atmospheric aerosol loads. Atmos Chem Phys 16:11219–11236

    Google Scholar 

  • Galytska E, Danylevsky V, Hommel R, Burrows JP (2018) Increased aerosol content in the atmosphere over Ukraine during summer 2010. Atmos Meas Tech 11:2101–2118

    Google Scholar 

  • Gryspeerdt E et al (2017) Constraining the instantaneous aerosol influence on cloud albedo. Proc Natl Acad Sci 114:4899–4904

    Google Scholar 

  • Gupta P, Khan MN, da Silva A, Patadia F (2013) MODIS aerosol optical depth observations over urban areas in Pakistan: quantity and quality of the data for air quality monitoring. Atmos Pollut Res 4:43–52

    Google Scholar 

  • He Q, Zhang M, Huang B (2016) Spatio-temporal variation and impact factors analysis of satellite-based aerosol optical depth over China from 2002–2015. Atmos Environ 129:79–90

    Google Scholar 

  • Holben BN et al (1998) AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens Environ 6:1–16

    Google Scholar 

  • Hsu CN, Tsay SC, King DM, Herman JR (2004) Aerosol properties over brightreflecting source regions. IEEE Trans Geo Remote Sens 42(2):557–569

    Google Scholar 

  • Humera B, Khan A, Farrukh C, Samina B, Imran S, Thomas B (2015) Intercomparison of MODIS, MISR, OMI and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data. Atmos Environ 111:133–136

    Google Scholar 

  • Jiang H, Feingold G (2006) Effect of aerosol on warm convective clouds: aerosol–cloud-surface flux feedbacks in a new coupled large eddy model. J Geophys Res 111:D01202

    Google Scholar 

  • Jiang JH, Su H, Zhai C, Massie ST, Schoeberl MR, Colarco PR, Platnick S, Gu Y, Liou KN (2011) Influence of convection and aerosol pollution on ice cloud particle effective radius. Atmos Chem Phys 11:457–463

    Google Scholar 

  • Kang N, Kumar KR, Yin Y, Diao Y, Yu X (2015) Correlation analysis between AOD and cloud parameters to study their relationship over China using MODIS data (2003–2013): impact on cloud formation and climate change. Aerosol Air Qual Res 15:958–973

    Google Scholar 

  • Kang N, Kumar KR, Hu K, Yu XN, Yin Y (2016) Longterm (2002–2014) evolution and trend in Collection 5.1 Level-2 aerosol products derived from the MODIS and MISR sensors over the Chinese Yangtze River Delta. Atmos Res 181:29–43

    Google Scholar 

  • Kaufman YJ, Koren I (2006) Smoke and pollution aerosol effect on cloud cover. Sci 313:655–658

    Google Scholar 

  • Kaufman YJ, Tanre D, Remer LA, Vermote EF, Chu A, Holben BN (1997) Operational remote sensing of tropospheric aerosol over land from EOS Moderate-resolution Imaging Spectroradiometer. J Geophys Res 102:17051–17067

    Google Scholar 

  • Kinne S et al (2003) Monthly averages of aerosol properties: a global comparison among models, satellite data, and AERONET ground data. J Geophys Res 108(D20):4634. https://doi.org/10.1029/2001JD001253

    Article  Google Scholar 

  • Lee LA, Reddington CL, Carslaw KS (2016) On the relationship between aerosol model uncertainty and radiative forcing uncertainty. Proc Natl Acad Sci 113:5820–5827

    Google Scholar 

  • Levy RC, Remer LA, Mattoo S, Vermote EF, Kaufman YJ (2007) Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of moderate resolution imaging spectroradiometer spectral reflectance. J Geophys Res Atmos 112:1–21

    Google Scholar 

  • Li Z (1998) Influence of absorbing aerosols on the inference of solar surface radiation budget and cloud absorption. J Clim 11:5–17

    Google Scholar 

  • Li Z et al (2009) Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective. Ann Geophys 27:2755–2770

    Google Scholar 

  • Liu J, Zheng Y, Li Z, Wu R (2008) Ground-based remote sensing of aerosol optical properties in one city in Northwest China. Atmos Res 89:194–205

    Google Scholar 

  • McClain CR, Cleave ML, Feldman GC, Gregg WW, Hooker SB, Kuring N (1998) Science quality SeaWiFS data for global biospheric research. Sea Technol 39:10–16

    Google Scholar 

  • Michibata T, Suzuki K, Sato Y, Takemura T (2016) The sources of discrepancies in aerosol and cloud-precipitation interactions between GCM and A-train retrievals. Atmos Chem Phys 16:15413–15424

    Google Scholar 

  • More S, Kumar PP, Gupta P, Devara P, Aher G (2013) Comparison of aerosol products retrieved from AERONET, MICROTOPS and MODIS over a tropical urban city, Pune, India. Aerosol Air Qual Res 13:107–121

    Google Scholar 

  • Nakajima T, Yoon SC, Ramanathan V, Shi GY, Takemura T, Higurashi A, Tsuruta H (2007) Overview of the Atmospheric Brown Cloud East Asian Regional Experiment 2005 and a study of the aerosol direct radiative forcing in East Asia. J Geophy Res Atmos 112(D24):1–23

    Google Scholar 

  • Neubauer D, Christensen MW, Poulsen C, Lohmann U (2017) Unveiling aerosol and cloud interactions part 2: minimixing the effects of aerosol swelling and wet scavengint in ECHAM6-HAM2 for comparison to satellite data. Chem Phys Discuss Atmos. https://doi.org/10.5194/acp-2017-449

    Article  Google Scholar 

  • Ogunjobi KO, Awoleye PO (2019) Intercomparison and validation of satellite and ground-based aerosol optical depth (AOD) retrievals over six AERONET sites in West Africa. Aesol Sci Eng. https://doi.org/10.1007/s41810-019-00040-7

    Article  Google Scholar 

  • Ooi MCG, Chan A, Ashfold MJ, Oozeer MY, Morris KI, Kong SSK (2019) The role of land use on the local climate and air quality during calm inter-monsoon in a tropical city. Geosci Front 10:405–415

    Google Scholar 

  • Penner JE, Xu L, Wang M (2011) Satellite methods underestimate indirect climate forcing by aerosols. Proc Natl Acad Sci 108:13404–13408

    Google Scholar 

  • Posyniak M, Szkop A, Pietruczuk A, Podgorski J, Scin JK (2016) The long-term (1964–2014) variability of aerosol optical thickness and its impact on solar irradiance based on the data taken at Belsk, Poland. Acta Geophys. 64:1858–1874

    Google Scholar 

  • Puig-Arnavat M, Bruno JC (2015) Artificial neural networks for thermochemical conversion of biomass. Recent Advan Thermo-Chem Conv Bio 5:133–156

    Google Scholar 

  • Quaas J et al (2009) Aerosol indirect effects general circulation model intercomparison and evaluation with satellite data. Atmos Chem Phys 9:8697–8717

    Google Scholar 

  • Ramanathan V, Crutzen PJ, Kiehl JT, Rosenfeld D (2001) Aerosols, climate, and the hydrological cycle. Science 294:2119–2124

    Google Scholar 

  • Randles CA, Russell LM, Ramaswamy V (2004) Hygroscopic and optical properties of organic sea salt aerosol and consequences for climate forcing. Geophys Res Lett 31:L16108. https://doi.org/10.1029/2004GL020628

    Article  Google Scholar 

  • Rawat P, Sarkar S, Jia S, Khillare PS, Sharma B (2019) Regional sulfate drives longterm rise in AOD over megacity Kolkata, India. Atmos Environ 209:167–181

    Google Scholar 

  • Remer LA et al (2005) The MODIS aerosol algorithm, products and validation. J Atmos Sci 62:947–973

    Google Scholar 

  • Richard BK, Kumar R, Zhao T (2017) Statistical intercomparison and validation of multisensory aerosol optical depth retrievals over three AERONET sites in Kenya, East Africa. Atmos Res 197:277–288

    Google Scholar 

  • Rosenfeld D, Sherwood S, Wood R, Donner L (2014) Climate effects of aerosol and cloud interactions. Science 343:379–380

    Google Scholar 

  • Ruiz-Arias JA, Dudhia J, Gueymard CA, Pozo-Vázquez A (2013) Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling. Atmos Chem Phys 13:675–692

    Google Scholar 

  • Rupakheti D, Kang S, Bilal M, Gong J, Xia X, Cong Z (2019) Aerosol optical depth climatology over Central Asian countries based on Aqua-MODIS Collection 6.1 data: aerosol variations and sources. Atmos Environ 207:205–214

    Google Scholar 

  • Santese M, De Tomasi F, Perrone MR (2007) AERONET versus MODIS aerosol parameters at different spatial resolutions over southeast Italy. J Geophys Res 112:D10214. https://doi.org/10.1029/2006JD007742

    Article  Google Scholar 

  • Sayer AM, Hsu NC, Bettenhausen C, Jeong MJ (2013) Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data. J Geophy Res Atmos 118:7864–7872

    Google Scholar 

  • Schneider J, Balis D, Böckmann C, Bösenberg J, Calpini B, Chaikovsky AP, Mattis IA (2000) European aerosol research lidar network to establish an aerosol climatology (EARLINET). J Aerol Sci 31:592–593

    Google Scholar 

  • Schutgens NA, Gryspeerdt E, Weigum N, Tsyro S, Goto D, Schulz M, Stier P (2016) Will a perfect model agree with perfect observations? The impact of spatial sampling. Atmos Chem Phys 16:6335–6353

    Google Scholar 

  • Sharif F, Alam K, Afsar S (2015) Spatio-temporal distribution of aerosol and cloud properties over sindh using MODIS satellite data and a HYSPLIT model. Aerosol Air Qual Res 15:657–672

    Google Scholar 

  • Shi JJ, Matsui T, Tao W-K, Peters-Lidard C, Chin M, Tan Q, Kemp E (2014) Implementation of an aerosol-cloud microphysics-radiation coupling into the nasa unified WRF: simulation results for the 6–7 August 2006 AMMA special observing period. Q J R Meteorol Soc. https://doi.org/10.1002/qj.2286

    Article  Google Scholar 

  • Sitnov SA, Gorchakov GI, Sviridenkov MA, Gorchakova IA, Karpov AV, Kolesnikova AB (2013) Aerospace monitoring of smoke aerosol over the European part of Russia in the period of massive forest and peatbog fires in July–August of 2010. Atmos Ocean Opt 26:265–280

    Google Scholar 

  • Srivastava R (2017) Trends in aerosol optical properties over South Asia. Int J Climatol 37:371–380

    Google Scholar 

  • Takahashi A, Hiyama T, Nishikawa M, Fujinami H, Higuchi A, Li W, Liu WZ, Fukushima Y (2008) Diurnal variation of water vapor mixing between the atmospheric boundary layer and free atmosphere over Changwu, the Loess Plateau in China. Sola 4:33–36

    Google Scholar 

  • Torghabeh AK, Pradhan B, Jahandari A (2019) Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz, southwest Iran. Geosci Front. https://doi.org/10.1016/j.gsf.2019.08.004

    Article  Google Scholar 

  • Wang L, Xin J, Wang Y, Li Z, Wang P, Liu G, Wen T (2007) Validation of MODIS aerosol products by CSHNET over China. Chin Sci Bull 52:1708–1718

    Google Scholar 

  • Welton E, Campbell J, Spinhirne J, Scott V (2001) Global monitoring of clouds and aerosols using a network of micropulse lidar systems. Proc SPIE 4153:151–158

    Google Scholar 

  • Xia X (2011) Variability of aerosol optical depth and Angstrom wavelength exponent derived from AERONET observations in recent decades. Environ Res Lett 6:4–11

    Google Scholar 

  • Xiao N, Shi T, Calder CA, Munroe DK, Berrett C, Wolfinbarger S, Li D (2009) Spatial characteristics of the difference between MISR and MODIS aerosol optical depth retrievals over mainland Southeast Asia. Remote Sens Environ 113:1–9

    Google Scholar 

  • Xie Y, Zhang Y, Xiong X, Qu JJ, Che H (2011) Validation of MODIS aerosol optical depth product over China using CARSNET measurements. Atmos Env 45:5970–5978

    Google Scholar 

  • Zhou L, Zhou C, Yang F, Che L, Wang B, Sun D (2019) Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015. J Geo Sci 29:253–270

    Google Scholar 

Download references

Acknowledgements

The author is grateful to the Principal Investigators of the Sahel AERONET sites used in the study for the data and for maintaining the stations. Also, author gratefully acknowledge OMI and MODIS for the provision of data and the comments of the anonymous reviewers which were valuable to improve the initial draft.

Author information

Authors and Affiliations

  1. Department of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria

    C. M. Anoruo

Authors
  1. C. M. Anoruo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. M. Anoruo.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 198 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anoruo, C.M. Modelling and Analysis of Aerosol and Cloud-Precipitable Water Inter-Hemispheric Interactions of Aerosol-Satellite Data Using Ground Observation. Aerosol Sci Eng 4, 331–350 (2020). https://doi.org/10.1007/s41810-020-00078-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41810-020-00078-y

Keywords

Search

Navigation