Comparison of aerosol optical depth of UV-B Monitoring and Research Program (UVMRP), AERONET and MODIS over continental United States

doi:10.1007/s11707-013-0376-9

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Issue () : 129-140 https://doi.org/10.1007/s11707-013-0376-9

RESEARCH ARTICLE

Comparison of aerosol optical depth of UV-B Monitoring and Research Program (UVMRP), AERONET and MODIS over continental United States

Hongzhao TANG¹(

), Maosi CHEN¹, John DAVIS¹, Wei GAO^1,2

1. USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80521, USA; 2. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80521, USA

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Abstract

The concern about the role of aerosols as to their effect in the Earth-Atmosphere system requires observation at multiple temporal and spatial scales. The Moderate Resolution Imaging Spectroradiameters (MODIS) is the main aerosol optical depth (AOD) monitoring satellite instrument, and its accuracy and uncertainty need to be validated against ground based measurements routinely. The comparison between two ground AOD measurement programs, the United States Department of Agriculture (USDA) Ultraviolet-B Monitoring and Research Program (UVMRP) and the Aerosol Robotic Network (AERONET) program, confirms the consistency between them. The intercomparison between the MODIS AOD, the AERONET AOD, and the UVMRP AOD suggests that the UVMRP AOD measurements are suited to be an alternative ground-based validation source for satellite AOD products. The experiments show that the spatial-temporal dependency between the MODIS AOD and the UVMRP AOD is positive in the sense that the MODIS AOD compare more favorably with the UVMRP AOD as the spatial and temporal intervals are increased. However, the analysis shows that the optimal spatial interval for all time windows is defined by an angular subtense of around 1° to 1.25°, while the optimal time window is around 423 to 483 minutes at most spatial intervals. The spatial-temporal approach around 1.25° & 423 minutes shows better agreement than the prevalent strategy of 0.25° & 60 minutes found in other similar investigations.

Keywords aerosol optical depth (AOD) United States Department of Agriculture (USDA) UV-B Monitoring and Research Program (UVMRP) Aerosol Robotic Network (AERONET) Moderate Resolution Imaging Spectroradiameters (MODIS) validation spatial-temporal approach

Corresponding Author(s): TANG Hongzhao,Email:hongzhao.tang@colostate.edu

Issue Date: 05 June 2013

Cite this article:

Hongzhao TANG,Maosi CHEN,John DAVIS, et al. Comparison of aerosol optical depth of UV-B Monitoring and Research Program (UVMRP), AERONET and MODIS over continental United States[J]. Front Earth Sci, 0, (): 129-140.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-013-0376-9
https://academic.hep.com.cn/fesci/EN/Y0/V/I/129

Fig.1 USDA UVMRP climatological sites and the two collocated AERONET sites in continental U.S.

Fig.2 Scatter plots of AOD against AOD

Fig.3 Shows the statistical information of the monthly average AOD and AOD, for wavelengths of 415, 500, 610, 665, and 870 nm at the TX41, IL01, and OK01 sites. The vertical error bars and the symbols in the middle represent the standard deviation and the average values, respectively.

Fig.4 shows the discrepancy between the monthly average UVMRP and AERONET AODs at the OK01, TX41, and IL01 sites.

Fig.5 shows the intercomparison of AOD, AOD, and AOD at the OK01 and IL01 sites from 2000 to 2010. Regression line parameters and correlation coefficients (R) are shown on the top of each panel.

Fig.6 Histograms of the relative difference between AOD and AOD (a) and between AOD than AOD (b).

Fig.7 Illustration of the spatial-temporal approach for AOD and AOD.

Fig.8 Shows scatter plots of AOD against AOD from 01/01/2000 to 12/31/2010 over the continental United States, with the spatial-temporal intervals of 0.09° & 3 minutes, 0.25° & 63 minutes, 0.50° & 123 minutes, and 1.25° & 543 minutes. Regression line parameters and correlation coefficients () are shown on the top of each panel.

Tab.1 List of the spatial intervals and the time windows.

Fig.9 shows the Pearson’s correlation coefficient matrixes for AOD and AOD within 30 days (a) and one day (b) over the continental United States.

Fig.10 The variation of correlation coefficients with spatial intervals at given time windows (a) and the variation of correlation coefficients with time windows at given spatial intervals (b).

1	Alexandrov M D, Marshak A, Cairns B, Lacis A A, Carlson B E (2004). Automated cloud screening algorithm for MFRSR data. Geophys Res Lett , 31(4): L04118 doi: 10.1029/2003GL019105
2	?ngstr?m A (1929). On the atmospheric transmission of sun radiation and on dust in the air. Geogr Ann , 11(1): 156–166 doi: 10.2307/519399
3	Chu D A, Kaufman Y J, Ichoku C, Remer L A, Tanré D, Holben B N (2001). Validation of MODIS aerosol optical depth retrieval over land. Geophys Res Lett , 29(12): 1–4 doi: 10.1029/2001GL013205
4	Eck T F, Holben B N, Dubovik O, Smirnov1 A, Gxoloub P, Chen H B, Chatenet B, Gomes L, Zhang X Y, Tsay S C, Ji Q, Giles D, Slutsker I(2005). Columnar aerosol optical properties at AERONET sites in central eastern Asia and aerosol transport to the tropical mid-Pacific. J Geophys Res , 110, D06202 doi: 10.1029/2004JD005274
5	Eck T F, Holben B N, Reid J, Dubovik O, Smirnov A, O’Neill N, Slutsker I, Kinne S (1999). Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J Geophys Res , 104(D24): 31333–31349 doi: 10.1029/1999JD900923
6	Fu P, Wang S, Chen C (1998). The hot discussion about climate change: the climate effects of aerosols. Advance in Earth Sciences , 13(4): 387–392 (in Chinese)
7	Gao W, Davis J M, Tree R, Slusser J R, Schmoldt D L (2010). An ultraviolet radiation monitoring and research program for agriculture. In: GaoW, Schmoldt D L, Slusser J R, eds. UV Radiation in Global Climate Change: Measurements, Modeling and Effects on Ecosystems . Beijing: Tsinghua University Press
8	Gupta P S, Christopher A, Wang J, Gehrig R, Lee Y, Kumar N (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos Environ , 40(30): 5880–5892 doi: 10.1016/j.atmosenv.2006.03.016
9	Haywood J, Boucher O (2000). Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: a review. Rev Geophys , 38(4): 513–543 doi: 10.1029/1999RG000078
10	Higurashi A, Nakajima T, Holben B N, Smirnov A, Frouin R, Chatenet B (2000). A study of global aerosol optical climatology with two-channel AVHRR remote sensing. J Clim , 13(12): 2011–2027 doi: 10.1175/1520-0442(2000)013<2011:ASOGAO>2.0.CO;2
11	Holben B N, Eck T F, Slutsker I D, Tanré D, Buis J P, Setzer A, Vermote E, Reagan J A, Kaufman Y J, Nakajima T, Lavenu F, Jankowiak I, Smirnov A (1998). AERONET-a federated instrument network and data archive for aerosol characterization. Remote Sens Environ , 66(1): 1–16 doi: 10.1016/S0034-4257(98)00031-5
12	Ichoku C, Chu A, Mattoo S, Kaufman Y J, Remer L A, Tanré D, Slutsker I, Holben B N (2002). A spatio-temporal approach for global validation and analysis of MODIS aerosol products. Geophys Res Lett , 29(12): 8006-8009 doi: 10.1029/2001GL013206
13	Jacobson M Z (2001). Global direct radiative forcing due to multi-component anthropogenic and natural aerosols. J Geophys Res , 106(D2): 1551–1568 doi: 10.1029/2000JD900514
14	Kahn R A, Gaitley B J, Martonchik J V, Diner D J, Crean K A, Holben B (2005). Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations. J Geophys Res , 110(D10): D10S04 doi: 10.1029/2004JD004706
15	Kahn R A, Garay M J, Nelson D L, Levy R C, Bull M A, Diner D J, Martonchik J V, Hansen E G, Remer L A, Tanré D (2011). Response to “Toward unified satellite climatology of aerosol proper- ties. 3. MODIS versus MISR versus AERONET”. J Quant Spectrosc Radiat Transf , 112(5): 901–909 doi: 10.1016/j.jqsrt.2010.11.001
16	Kahn R A, Garay M J, Nelson D L, Yau K K, Bull M A, Gaitley B J, Martonchik J V, Levy R C (2007). Satellite-derived aerosol optical depth over dark water from MISR and MODIS: comparisons with AERONET and implications for climatological studies. J Geophys Res , 112(D18): D18205 doi: 10.1029/2006JD008175
17	Kampa M, Castanas E (2008). Human health effects of air pollution. Environ Pollut , 151(2): 362–367 doi: 10.1016/j.envpol.2007.06.012 pmid:17646040
18	Kaufman Y J, Gitelson A, Karnieli A, Ganor E, Fraser R S, Nakajima T, Mattoo S, Holben B M (1994). Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements. J Geophys Res , 99(D5): 10341–10356 doi: 10.1029/94JD00229
19	Kaufman Y J, Tanré D, Gordon H R, Nakajima T, Lenoble J, Frouin R, Grassl H, Herman B M, King M D, Teillet P M (1997a). Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. J Geophys Res , 102(D14): 16815–16830 doi: 10.1029/97JD01496
20	Kaufman Y J, TanréD, Remer L A, Vermote E F, Chu A, Holben B N (1997b). Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J Geophys Res , 102(D14): 17051–17067 doi: 10.1029/96JD03988
21	Kokhanovsky A, Breon F M, Cacciari A, Carboni E, Diner D, Di Nicolantonio W, Grainger R G, Grey W M F, H?ller R, Lee K H, Li Z, North P R J, Sayer A M, Thomas G E, von Hoyningen-Huene W (2007). Aerosol remote sensing over land: a comparison of satellite retrievals using different algorithms and instruments. Atmos Res , 85(3-4): 372–394 doi: 10.1016/j.atmosres.2007.02.008
22	Lee T E, Miller S D, Turk F J, Schueler C, Julian R, Deyo S, Dills P, Wang S (2006). The NPOESS VIIRS day/night visible sensor. Bull Am Meteorol Soc , 87(2): 191–199 doi: 10.1175/BAMS-87-2-191
23	Levy R C, Pinker R (2007). Remote sensing of spectral aerosol properties: a classroom experience. Bull Am Meteorol Soc , 88(1): 25–30 doi: 10.1175/BAMS-88-1-25
24	Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R, Eck T F (2010). Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmos Chem Phys , 10(21): 10399–10420 doi: 10.5194/acp-10-10399-2010
25	Liu L, Mishchenko M I (2008). Toward unified satellite climatology of aerosol properties: direct comparisons of advanced level 2 aerosol products. J Quant Spectrosc Radiat Transf , 109(14): 2376–2385 doi: 10.1016/j.jqsrt.2008.05.003
26	Liu P, Zhang Y, Kenneth L. Schere (2011). Use of a process analysis tool for diagnostic study on fine particulate matter predictions in the U.S. -Part II: Analyses and sensitivity simulations. Atmospheric Pollution Research 2 , 2(1): 61–71
27	Mishchenko M I, Geogdzhayev I V, Rossow W B, Cairns B, Carlson B E, Lacis A A, Liu L, Travis L D (2007). Long-term satellite record reveals likely recent aerosol trend. Science , 315(5818): 1543 doi: 10.1126/science.1136709 pmid:17363666
28	Mishchenko M I, Liu L, Geogdzhayev I V, Travis L D, Cairns B, Lacis A A (2010). Toward unified satellite climatology of aerosol properties. J Quant Spectrosc Radiat Transf , 111(4): 540–552 doi: 10.1016/j.jqsrt.2009.11.003
29	Smirnov A, Holben B N, Eck T F, Dubovik O, Slutsker I (2000). Cloud screening and quality control algorithms for the AERONET database. Remote Sens Environ , 73(3): 337–349 doi: 10.1016/S0034-4257(00)00109-7
30	Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M, Miller H L(2007). Climate Change 2007: the Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge , United Kingdom and New York, NY, USA: Cambridge University Press
31	Steven A A, Kathleen I S, Menzel W P, Richard A F, Christopher C M, Liam E G (1998). Discriminating clear sky from clouds with MODIS. J Geophys Res , 103(D24): 32141–32157 doi: 10.1029/1998JD200032
32	Tanré D, Kaufman Y J, Herman M, Mattoo S (1997). Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. J Geophys Res , 102(D14): 16971–16988 doi: 10.1029/96JD03437

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