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Statistical Analysis of Aerosols Characteristics from Satellite Measurements over East Africa Using Autoregressive Moving Average (ARIMA)

DOI: 10.4236/oalib.1109496, PP. 1-14

Subject Areas: Atmospheric Sciences

Keywords: Series, ARIMA, Differencing, Forecast, ACF, PACF

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Abstract

Aerosols have become a major subject of concern at global, regional and local scales. They influence Earth’s radiation budget by scattering and absorbing solar energy resulting in atmospheric cooling and warming respectively. However, immense efforts have been devoted to monitoring atmospheric aerosols using various techniques ranging from in-situ, ground and satellite-based remote sensing and modeling techniques. Thus, time series analysis and forecasting have gained momentum over recent decades. The current study performed a time series analysis using Box-Jenkins procedure-based ARIMA (Autoregressive Integrated Moving Average) model for aerosol properties (Total Aerosol Optical Depth, TAOD; Absorption Aerosol Optical Depth, AAOD; Scattering Aerosol Optical Depth, SAOD and Direct Aerosol Radiative Forcing, DARF) over EA derived from satellite platforms. The formulation process in MATLAB followed by the current study has been outlined with a view to generating the best fitting seasonal ARIMA (p, q, d) × (P Q D) model. The finding for the forementioned characteristics reveals clear seasonal variation, hence, differencing was done. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of differenced series are estimated and the significant lags are used to find out the order of the model. The statistical parameters (RMSE, MAE, MAPE, MASE and normalized BIC) were estimated for testing the validity of ARIMA models so formulated. The current study found that ARIMA (1, 0, 0) × (2, 1, 2)12 model is adequate for forecasting and was therefore used to forecast aerosol characteristics for the year 2022- 2025 over EA domain. ARIMA model ascertained can be applied to other fields of study such as climatology, and climate change among other areas to predict future values so that timely control measures can effectively be planned.

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Khamala, G. W. , Makokha, J. W. and Boiyo, R. (2022). Statistical Analysis of Aerosols Characteristics from Satellite Measurements over East Africa Using Autoregressive Moving Average (ARIMA). Open Access Library Journal, 9, e9496. doi: http://dx.doi.org/10.4236/oalib.1109496.

References

[1]  Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, D., Coakley, J.A., Hansen, J.E. and Hofmann, D.J. (1992) Climate Forcing by Anthropogenic Aerosols. Science, 255, 423-430. https://doi.org/10.1126/science.255.5043.423
[2]  Satheesh, S.K, Ramanathan, V., Holben B.N., Moorthy, K.K., Loeb, N.G., Maring, H., Prospero, J.M. and Savoie, D. (2002) Chemical, Microphysical, and Radiative Effects of Indian Ocean Aerosols. Journal of Geophysical Research: Atmospheres, 107, AAC 20-1-AAC 20-13. https://doi.org/10.1029/2002JD002463
[3]  Khamala, G.W., Makokha, J.W., Boiyo, R. and Kumar, R.K. (2022) Long-Term Climatology and Spatial Trends of Absorption, Scattering and Total Aerosol Optical Depths over East Africa during 2001-2019. Environmental Science and Pollution Research, 29, 61283-61297. https://doi.org/10.1007/s11356-022-20022-6
[4]  Twomey, S.A., Piepgrass, M. and Wolfe, T.L. (1984) An Assessment of the Impact of Pollution on the Global Cloud Albedo. Tellus B: Chemical and Physical Meteorology, 36, 356-366.https://doi.org/10.1111/j.1600-0889.1984.tb00254.x
[5]  Ramanathan, V., Crutzen, P.J., Kiehl, J.T. and Rosenfeld, D. (2001) Aerosols, Climate, and the Hydrological Cycle. Science, 294, 2119-2124. https://doi.org/10.1126/science.1064034
[6]  Holben, B.N., Eck, T.F., Slutsker, I., Tanré, D., Buis, J.P., Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y.J., Nakajima, T., Lavenu, F., Jankowiak, I. and Smirnov, A. (1998) AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sensing of Environment, 66, 1-16. https://doi.org/10.1016/S0034-4257(98)00031-5
[7]  Amiridis, V., Balis, D.S., Kazadzis, S., Bais, A., Giannakaki, E., Papayannis, A. and Zerefos, C. (2005) Four-Year Aerosol Observations with a Raman Lidar at Thessaloniki, Greece, in the Framework of European Aerosol Research Lidar Network (EARLINET). Journal of Geophysical Research: Atmospheres, 110, D21203. https://doi.org/10.1029/2005JD006190
[8]  Che, H., Zhang, X.Y., Chen H.B., Damiri, B., Goloub, P., Li, Z.Q., et al. (2009) Instrument Calibration and Aerosol Optical Depth Validation of the China Aerosol Remote Sensing Network. Journal of Geophysical Research: Atmospheres, 114, D03206. https://doi.org/10.1029/2008JD011030
[9]  Remer, L.A., Kaufman, Y.J., Tanre, D., Matto, S., Chu, D.A., Martins, J.V., et al. (2005) The MODIS Aerosol Algorithm, Products, and Validation. Journal of the Atmospheric Sciences, 62, 947-973. https://doi.org/10.1175/JAS3385.1
[10]  Rienecker, M.M., Suarez, J.M., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, G.M., Schubert, D.S., Takacs, L., Kim, G., Bloom, S., Chen, J., Collins, D., Conaty, A., Dasilva, A., Gu, W., Joiner, J., Koster, R.D., Lucchesi, R., et al. (2011) MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. Journal of Climate, 24, 3624-3648. https://doi.org/10.1175/JCLI-D-11-00015.1
[11]  Liu, Z., Liu, Q., Lin, H.C., Schwartz, C.S., Lee, Y.H. and Wang, T. (2011) Three-Dimensional Variational Assimilation of MODIS Aerosol Optical Depth: Implementation and Application to a Dust Storm over East Asia. Journal of Geophysical Research: Atmospheres, 116, D23206. https://doi.org/10.1029/2011JD016159
[12]  Liu, D. and Li, L. (2015) Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China. International Journal of Environmental Research and Public Health, 12, 7085-7099. https://doi.org/10.3390/ijerph120607085
[13]  Taneja, K., Ahmad, S., Ahmad, K. and Attri, S.D. (2016) Time Series Analysis of Aerosol Optical Depth over New Delhi Using Boxe-Jenkins ARIMA Modeling Approach. Atmospheric Pollution Research, 7, 585-596. https://doi.org/10.1016/j.apr.2016.02.004
[14]  Ahmad, S., Khan, I.H. and Parida, B.P. (2002) Performance of Stochastic Approaches for Forecasting River Water Quality. Water Research, 35, 4261-4266. https://doi.org/10.1016/S0043-1354(01)00167-1
[15]  Wang, Z.F., Li, J., Wang, Z., Yang, W.Y., Tang, X., Ge, B.Z., Yan, P.Z., Zhu, L.L., Chen, X.S., et al. (2014) Modeling Study of Regional Severe Hazes over Mid-Eastern China in January 2013 and Its Implications on Pollution Prevention and Control. Science China Earth Sciences, 57, 3-13. https://doi.org/10.1007/s11430-013-4793-0
[16]  Cadenas, E. and Rivera, W. (2010) Wind Speed Forecasting in Three Different Regions of Mexico, Using a Hybrid ARIMA-ANN Model. Renewable Energy, 35, 2732-2738. https://doi.org/10.1016/j.renene.2010.04.022
[17]  Kripalani, R.H. and Kulkarni, A. (2001). Monsoon Rainfall Variations and Teleconnections over South and East Asia. International Journal of Climatology, 21, 603-616. https://doi.org/10.1002/joc.625
[18]  Soltani, S., Modarres, R. and Eslamian, S.S. (2007) The Use of Time Series Modeling for the Determination of Rainfall Climates of Iran. International Journal of Climatology, 27, 819-829. https://doi.org/10.1002/joc.1427
[19]  Liang, W.-M., Wei, H.-Y. and Kuo, H.-W. (2009) Association between Daily Mortality from Respiratory and Cardiovascular Diseases and Air Pollution in Taiwan. Environmental Research, 109, 51-58. https://doi.org/10.1016/j.envres.2008.10.002
[20]  Chattopadhyay, G. and Chattopadhyay, S. (2009) Autoregressive Forecast of Monthly Total Ozone Concentration: A Neurocomputing Approach. Computers & Geosciences, 35, 1925-1932. https://doi.org/10.1016/j.cageo.2008.11.007
[21]  Soni, K., Kapoor, S., Parmar, K.S. and Kaskaoutis, D.G. (2014) Statistical Analysis of Aerosols over the Gangetic-Himalayan Region Using ARIMA Model Based on Long-Term MODIS Observations. Atmospheric Research, 149, 174-192. https://doi.org/10.1016/j.atmosres.2014.05.025
[22]  Jere, S. and Moyo, E. (2016) Modelling Epidemiological Data Using Box-Jenkins Procedure. Open Journal of Statistics, 6, 295-302. https://doi.org/10.4236/ojs.2016.62025
[23]  Zhang, G.P. (2003) Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 50, 159-175. https://doi.org/10.1016/S0925-2312(01)00702-0
[24]  Khan, M.F. and Gupta, R. (2020) ARIMA and NAR Based Prediction Model for Time Series Analysis of COVID-19 Cases in India. Journal of Safety Science and Resilience, 1, 12-18. https://doi.org/10.1016/j.jnlssr.2020.06.007
[25]  Bhatnagar, S., Lal, V., Gupta, S.D. and Gupta, O.P. (2021) Forecasting Incidence of Dengue in Rajasthan, Using Time Series Analyses. Indian Journal of Public Health, 56, 281-285. https://doi.org/10.4103/0019-557X.106415
[26]  Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994) Time Series Analysis Forecasting and Control. 3rd Edition, Prentice-Hall, Englewood Cliffs.
[27]  Schwarz, G. (1978) Estimating the Dimension of a Model. The Annals of Statistics, 6, 461-464. https://doi.org/10.1214/aos/1176344136
[28]  Elsayir, H.A. (2019) Residual Analysis for Auto-Correlated Econometric Model. Open Journal of Statistics, 9, 48-61. https://doi.org/10.4236/ojs.2019.91005

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