A 3D multi-threshold, region-growing algorithm for identifying dust storm features from model simulations

References

• Akhlaq, M., Sheltami, T.R., and Mouftah, H.T., 2012. A review of techniques and technologies for sand and dust storm detection. Reviews in Environmental Science and Bio/Technology, 11 (3), 305–322. doi:10.1007/s11157-012-9282-y
   Web of Science ®Google Scholar
• Allwine, K.J., et al., 2006. DUSTRAN 1.0 user’s guide: a GIS-based atmospheric dust dispersion modeling system. Richland: Pacific Northwest National Lab.
   Google Scholar
• Ashpole, I. and Washington, R., 2013. A new high-resolution central and western Saharan summertime dust source map from automated satellite dust plume tracking. Journal of Geophysical Research: Atmospheres, 118 (13), 6981–6995.
   Web of Science ®Google Scholar
• Bankman, I.N., et al., 1997. Segmentation algorithms for detecting microcalcifications in mammograms. IEEE Transactions on Information Technology in Biomedicine, 1 (2), 141–149.
   PubMedGoogle Scholar
• Barker, H.W., et al., 2011. A 3D cloud-construction algorithm for the EarthCARE satellite mission. Quarterly Journal of the Royal Meteorological Society, 137 (657), 1042–1058. doi:10.1002/qj.824
   Web of Science ®Google Scholar
• Benedetti, A., et al., 2014. Operational dust prediction. In: P. Knippertz and J.B.W. Stuut, eds. Mineral dust. Dordrecht: Springer, 223–265.
   Google Scholar
• Beucher, S. and Lantuéjoul, C., 1979. Use of watersheds in contour detection. In: International workshop on image processing: real-time edge and motion detection/estimation, September 1979 Rennes, France. Available from: http://cmm.ensmp.fr/~beucher/publi/watershed.pdf
   Google Scholar
• Cai, Y., et al., 2007. Visual transformation for interactive spatiotemporal data mining. Knowledge and Information Systems, 13 (2), 119–142. doi:10.1007/s10115-007-0075-5
   Web of Science ®Google Scholar
• Comaniciu, D. and Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (5), 603–619. doi:10.1109/34.1000236
   Web of Science ®Google Scholar
• Dixon, M. and Wiener, G., 1993. TITAN: thunderstorm identification, tracking, analysis, and nowcasting – a radar-based methodology. Journal of Atmospheric and Oceanic Technology, 10 (6), 785–797. doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2
   Web of Science ®Google Scholar
• El-Askary, H., et al., 2005. Enhancing dust storm detection using PCA based data fusion. In: IEEE international geoscience and remote sensing symposium, July 2005 Seoul, Korea. New York: IEEE.
   Google Scholar
• Ester, M., et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, August 1996 Portland, Oregon. Menlo Park: AAAI Press.
   Google Scholar
• Faghmous, J.H., et al., 2015. A daily global mesoscale ocean eddy dataset from satellite altimetry. Scientific Data, 2, 150028. doi:10.1038/sdata.2015.28
   PubMed Web of Science ®Google Scholar
• Faghmous, J.H. and Kumar, V., 2014. Spatio-temporal data mining for climate data: advances, challenges, and opportunities. In: W. W. Chu, ed. Data mining and knowledge discovery for big data. Heidelberg: Springer, 83–116.
   Google Scholar
• Fiedler, S., et al., 2013. Climatology of nocturnal low-level jets over North Africa and implications for modeling mineral dust emission. Journal of Geophysical Research: Atmospheres, 118 (12), 6100–6121.
   PubMed Web of Science ®Google Scholar
• Gong, S.L. and Zhang, X.Y., 2008. CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia. Atmospheric Chemistry and Physics, 8 (9), 2333–2340. doi:10.5194/acp-8-2333-2008
   Web of Science ®Google Scholar
• Greenough, G., et al., 2001. The potential impacts of climate variability and change on health impacts of extreme weather events in the United States. Environmental Health Perspectives, 109 (Suppl 2), 191. doi:10.2307/3435009
   PubMed Web of Science ®Google Scholar
• Griffin, D.W. and Kellogg, C.A., 2004. Dust storms and their impact on ocean and human health: dust in Earth’s atmosphere. EcoHealth, 1 (3), 284–295. doi:10.1007/s10393-004-0120-8
   Google Scholar
• Han, L., et al., 2009. 3D convective storm identification, tracking, and forecasting – an enhanced TITAN algorithm. Journal of Atmospheric and Oceanic Technology, 26 (4), 719–732. doi:10.1175/2008JTECHA1084.1
   Web of Science ®Google Scholar
• Handwerker, J., 2002. Cell tracking with TRACE3D—a new algorithm. Atmospheric Research, 61 (1), 15–34. doi:10.1016/S0169-8095(01)00100-4
   Web of Science ®Google Scholar
• Heinold, B., et al., 2013. The role of deep convection and nocturnal low-level jets for dust emission in summertime West Africa: estimates from convection-permitting simulations. Journal of Geophysical Research: Atmospheres, 118 (10), 4385–4400.
   PubMed Web of Science ®Google Scholar
• Heus, T., et al., 2009. A statistical approach to the life cycle analysis of cumulus clouds selected in a virtual reality environment. Journal of Geophysical Research: Atmospheres, 114, D06208. doi:10.1029/2008JD010917
   Web of Science ®Google Scholar
• Heus, T. and Seifert, A., 2013. Automated tracking of shallow cumulus clouds in large domain, long duration large eddy simulations. Geoscientific Model Development, 6, 1261–1273. doi:10.5194/gmd-6-1261-2013
   Web of Science ®Google Scholar
• Hu, X.Q., et al., 2008. Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia. Atmospheric Chemistry and Physics, 8 (6), 1649–1659.
   Web of Science ®Google Scholar
• Jaccard, P., 1912. The distribution of the flora in the alpine zone. New Phytologist, 11 (2), 37–50. doi:10.1111/nph.1912.11.issue-2
   Google Scholar
• Janugani, S., et al., 2009. Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery. In: S.S. Shen and P.E. Lewis, eds. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XV, 13 April 2009 Orlando, FL, USA. Bellingham: SPIE. Proceedings of SPIE, 73341G-73341G-73312.
   Google Scholar
• Johnson, J.T., et al., 1998. The storm cell identification and tracking algorithm: an enhanced WSR-88D algorithm. Weather and Forecasting, 13 (2), 263–276. doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2
   Web of Science ®Google Scholar
• Jones, E., et al., 2001. {SciPy}: open source scientific tools for {Python}. Available from: https://www.scipy.org/citing.html
   Google Scholar
• Knippertz, P. and Stuut, J.B.W., 2014. Mineral dust. Dordrecht: Springer.
   Google Scholar
• Lakshmanan, V., Hondl, K., and Rabin, R., 2009. An efficient, general-purpose technique for identifying storm cells in geospatial images. Journal of Atmospheric and Oceanic Technology, 26 (3), 523–537. doi:10.1175/2008JTECHA1153.1
   Web of Science ®Google Scholar
• Lei, H. and Wang, J.X.L., 2014. Observed characteristics of dust storm events over the western United States using meteorological, satellite, and air quality measurements. Atmospheric Chemistry and Physics, 14 (15), 7847–7857. doi:10.5194/acp-14-7847-2014
   Web of Science ®Google Scholar
• Liao, P.S., Chen, T.S., and Chung, P.C., 2001. A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering, 17 (5), 713–727.
   Web of Science ®Google Scholar
• Liu, M., et al., 2003. A high-resolution numerical study of the Asian dust storms of April 2001. Journal of Geophysical Research: Atmospheres, 108 (D23). doi:10.1029/2002JD003178
   Web of Science ®Google Scholar
• Lorentzen, E.A., 2011. Fast seeded region growing in a 3D grid. Dissertation (Master of Science in Computer Science). Norwegian University of Science and Technology.
   Google Scholar
• Matthews, J. and Trostel, J., 2010. An improved storm cell identification and tracking (SCIT) algorithm based on DBSCAN clustering and JPDA tracking methods. In: 21st international lightning detection conference, 19–20 April 2010 Orlando, FL. Helsinki: Vaisala Oyj.
   Google Scholar
• Mishra, M.K., Chauhan, P., and Sahay, A., 2015. Detection of Asian dust storms from geostationary satellite observations of the INSAT-3D imager. International Journal of Remote Sensing, 36 (18), 4668–4682. doi:10.1080/01431161.2015.1084432
   Web of Science ®Google Scholar
• Moseley, C., Berg, P., and Haerter, J.O., 2013. Probing the precipitation life cycle by iterative rain cell tracking. Journal of Geophysical Research: Atmospheres, 118 (24), 13361–13370.
   Web of Science ®Google Scholar
• Nickovic, S., et al., 2001. A model for prediction of desert dust cycle in the atmosphere. Journal of Geophysical Research: Atmospheres, 106 (D16), 18113–18129. doi:10.1029/2000JD900794
   Web of Science ®Google Scholar
• Niu, T., et al., 2008. Data assimilation of dust aerosol observations for the CUACE/dust forecasting system. Atmospheric Chemistry and Physics, 8 (13), 3473–3482. doi:10.5194/acp-8-3473-2008
   Web of Science ®Google Scholar
• Oreskes, N., Shrader-Frechette, K., and Belitz, K., 1994. Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263 (5147), 641–646. doi:10.1126/science.263.5147.641
   PubMed Web of Science ®Google Scholar
• Otsu, N., 1975. A threshold selection method from gray-level histograms. Automatica, 11 (285–296), 23–27.
   Google Scholar
• Pedregosa, F., et al., 2011. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Peng, Z., et al., 2015. 3D cloud detection and tracking system for solar forecast using multiple sky imagers. Solar Energy, 118, 496–519. doi:10.1016/j.solener.2015.05.037
   Web of Science ®Google Scholar
• Ren, Y., et al., 2014. Boosted mean shift clustering. In: T. Calders, et al., eds. Joint European conference on machine learning and knowledge discovery in databases, 15–19 September 2014 Nancy, France. Heidelberg: Springer, 646–661.
   Google Scholar
• Romano, F., et al., 2013. Dust detection and optical depth retrieval using MSG-SEVIRI data. Atmosphere, 4 (1), 35–47. doi:10.3390/atmos4010035
   Web of Science ®Google Scholar
• Simon, J.L., Diederich, M., and Troemel, S., 2013. Applying mean-shift-clustering for 3D object detection in remote sensing data. In: European Geosciences Union, European geosciences union general assembly, 7–12 April 2013 Vienna, Austria, Vol. 15. Göttingen: Copernicus Publications, 9683.
   Google Scholar
• Uno, I., et al., 2008. 3D structure of Asian dust transport revealed by CALIPSO lidar and a 4DVAR dust model. Geophysical Research Letters, 35 (6), L06803. doi:10.1029/2007GL032329
   Web of Science ®Google Scholar
• Van Der Walt, S., Colbert, S.C., and Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13 (2), 22–30. doi:10.1109/MCSE.2011.37
   Web of Science ®Google Scholar
• Wei, K., Zhang, T., and He, B., 2008. Detection of sand and dust storms from meris image using FE-Otsu algorithm. In: 2008 2nd international conference on bioinformatics and biomedical engineering, 16–18 May 2008 Shanghai, China. New York: IEEE, 3852–3855.
   Google Scholar
• Wilkening, K.E., Barrie, L.A., and Engle, M., 2000. Trans-pacific air pollution. Science, 290 (5489), 65–67. doi:10.1126/science.290.5489.65
   PubMed Web of Science ®Google Scholar
• Wilks, D.S., 2006. Statistical methods in the atmospheric sciences. 2nd ed. Cambridge: Academic Press, 627.
   Google Scholar
• World Meteorological Organization, 2011. WMO sand and dust storm warning advisory and assessment system (SDSWAS)—science and implementation plan 2011–2015. Geneva: WMO.
   Google Scholar
• Xie, J., et al., 2010. High-performance computing for the simulation of dust storms. Computers, Environment and Urban Systems, 34 (4), 278–290. doi:10.1016/j.compenvurbsys.2009.08.002
   Web of Science ®Google Scholar
• Yang, C., et al., 2011. Using spatial principles to optimize distributed computing for enabling the physical science discoveries. Proceedings of the National Academy of Sciences, 108 (14), 5498–5503. doi:10.1073/pnas.0909315108
   PubMed Web of Science ®Google Scholar
• Yang, C., et al., 2015. Contemporary computing technologies for processing big spatiotemporal data. In: M. Kwan, et al., eds. Space-time integration in geography and GIScience. Netherlands: Springer, 327–351.
   Google Scholar
• Yang, C., et al., 2016. Big data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth. doi:10.1080/17538947.2016.1239771
   Web of Science ®Google Scholar
• Yang, Y.Q., et al., 2008. Sand/dust storm processes in Northeast Asia and associated large-scale circulations. Atmospheric Chemistry and Physics, 8 (1), 25–33. doi:10.5194/acp-8-25-2008
   Web of Science ®Google Scholar
• Yumimoto, K., et al., 2007. Adjoint inverse modeling of dust emission and transport over East Asia. Geophysical Research Letters, 34 (8), L08806. doi:10.1029/2006GL028551
   Web of Science ®Google Scholar
• Zhang, Z., et al., 2016. Observed 3D structure, generation, and dissipation of oceanic mesoscale eddies in the South China Sea. Scientific Reports, 6, 24349. doi:10.1038/srep24349
   PubMed Web of Science ®Google Scholar
• Zucker, S.W., 1976. Region growing: childhood and adolescence. Computer Graphics and Image Processing, 5 (3), 382–399. doi:10.1016/S0146-664X(76)80014-7
   Google Scholar