A global process-oriented sea surface temperature anomaly dataset retrieved from remote sensing products

References

• Banzon, V. F., Reynolds, R. W., Stokes, D., & Xue, Y. (2014). A 1/4°-Spatial-resolution daily sea surface temperature climatology based on a blended satellite and in Situ analysis. Journal of Climate, 27(21), 8221–8228.
   Web of Science ®Google Scholar
• Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., … Yuan, Z. (2021). A new global gridded sea surface temperature data product based on multisource data. Earth System Science Data, 13(5), 2111–2134.
   Web of Science ®Google Scholar
• Dai, A. (2016). Future warming patterns linked to today’s climate variability. Scientific Reports, 6(1), 19110.
   Google Scholar
• Ding, R., Tseng, Y., Li, J., Sun, C., Xie, F., & Hou, Z. (2019). Relative contributions of North and South Pacific sea surface temperature anomalies to ENSO. Journal of Geophysical Research: Atmospheres, 124, 6222–6237.
   Web of Science ®Google Scholar
• Dixon, M., & Wiener, G. (1993). TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. Journal of Atmospheric and Oceanic Technology, 10(6), 785–797.
   Web of Science ®Google Scholar
• GCOS. (2011). Systematic observation requirements for satellite-based products for climate, 2011 update. GCOS Report 154. WMO.
   Google Scholar
• Guo, Y., Zhang, R., Wen, Z., Li, J., Zhang, C., & Zhou, Z. (2021). Understanding the role of SST anomaly in extreme rainfall of 2020 Meiyu season from an interdecadal perspective. Science China Earth Sciences, 64(10), 1–14.
   Web of Science ®Google Scholar
• Hollmann, R., Merchant, C. R., Saunders, R., Downy, C., Buchwitz, M., Cazenave, A., … Wagner, W. (2013). The ESA climate change initiative: Satellite data records for essential climate variables. Bulletin of the American Meteorological Society, 94(10), 1541–1552.
   Web of Science ®Google Scholar
• Kaplan, A., Cane, M., Kushnir, Y., Clement, A., Blumenthal, M., & Rajagopalan, B. (1998). Analyses of global sea surface temperature 1856-1991. Journal of Geophysical Research, 103(C9), 18567–18589.
   Web of Science ®Google Scholar
• Kawale, J., Stefan, L., Arjun, K., Michael, S., Peter, S., Vipin, K., … Semazzi, F. (2013). A graph-based approach to find teleconnections in climate data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 6(3), 158–179.
   Web of Science ®Google Scholar
• Kilpatrick, K. A., Podestá, G. P., & Evans, R. (2001). Overview of the NOAA/NASA advanced very high resolution radiometer pathfinder algorithm for sea surface temperature and associated matchup database. Journal of Geophysical Research: Oceans, 106(C5), 9179–9197.
   Web of Science ®Google Scholar
• Kuhn, H. W. (2005). The Hungarian method for the assignment problem. Naval Research Logistics, 52(1), 7–21.
   Web of Science ®Google Scholar
• Legeckis, R., & Zhu, T. (1997). Sea surface temperatures from the GOES-8 Geostationary Satellite. Bulletin of the American Meteorological Society, 78(9), 1971–1983.
   Web of Science ®Google Scholar
• Liao, Z. H., Dong, Q., Xue, C. J., Bi, J. W., & Wan, G. T. (2017). Reconstruction of daily sea surface temperature based on radial basis function networks. Remote Sensing, 9(11), 1204.
   Web of Science ®Google Scholar
• Liu, J. Y., Xue, C. J., Dong, Q., Wu, C. B., & Xu, Y. F. (2019). A process-oriented spatiotemporal clustering method for complex trajectories of dynamic geographic phenomena. IEEE Access, 7, 155951.
   Web of Science ®Google Scholar
• Liu, J. Y., Xue, C. J., He, Y. W., Dong, Q., Kong, F. P., & Hong, Y. L. (2018). Dual-constraint spatiotemporal clustering approach for exploring marine anomaly patterns using remote sensing products. Journal of Selected Topics in Applied Earth Observations and Remote SensSensing, 11(11), 3693–3796.
   Web of Science ®Google Scholar
• McClain, E. P., Pichel, W. G., & Walton, C. C. (1985). Comparative performance of AVHRR-based multichannel sea surface temperatures. Journal of Geophysical Research, 90(C6), 11587–11601.
   Web of Science ®Google Scholar
• McPhaden, M. J., Zebiak, S. E., & Glantz, M. H. (2006). ENSO as an integrating concept in earth science, 314(5806), 1740–1745.
   Google Scholar
• Merchant, C. J., Borgne, P., Borgne, L. E., Marsouin, A. M., & Roquet, H. R. (2008). Optimal estimation of sea surface temperature from split-window observations. Remote Sensing of Environment, 112(5), 2469–2484.
   Web of Science ®Google Scholar
• Murtugudde, R., Wang, L. P., Hackert, E., Beauchamp, J., Christian, J., & Busalacchi, A. J. (2004). Remote sensing of the Indo-Pacific region: Ocean colour, sea level, winds and sea surface temperatures. International Journal of Remote Sensing, 25(7–8), 1423–1435.
   Web of Science ®Google Scholar
• Ping, B., Su, F. Z., & Meng, Y. S. (2015). Reconstruction of satellite-derived sea surface temperature data based on an improved DINEOF algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8), 4181–4188.
   Web of Science ®Google Scholar
• Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., & Wang, W. Q. (2002). An improved in situ and satellite SST analysis for climate. Journal of Climate, 15(13), 1609–1625.
   Web of Science ®Google Scholar
• Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22), 5473–5496.
   Web of Science ®Google Scholar
• Saha, K.…, Zhao, K., Xuepeng, X., Zhang, H.-M., Casey, K. S., Zhang, D., Sheekela, B-Y.,Kilpatrick, K.D., Evans, R.H., Ryan, T., Relph, J.M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. doi: https://doi.org/10.7289/v52j68xx.
   Google Scholar
• Saulquin, B., Fablet, R., Mercier, G., Demarcq, H., Mangin, A., & Andon, O. H. F. (2014). Multiscale event-based mining in geophysical time series: Characterization and distribution of significant time-scales in the sea surface temperature anomalies relatively to ENSO periods from 1985 to 2009. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3543–3552.
   Web of Science ®Google Scholar
• Song, W. J., Dong, Q., & Xue, C. J. (2016). A classified El Niño index using AVHRR remote-sensing SST data. International Journal of Remote Sensing, 37(2), 403–417.
   Web of Science ®Google Scholar
• Steinbach, M., Tan, P., Boriah, S., Kumar, V., Klooster, S., & Potter, C. (2006, May 23–24). The application of clustering to earth science data: Progress and challenges. In Proceedings of the 2nd NASA data mining workshop: issues and applications in earth science data (pp. 1–6). Pasadena, CA.
   Google Scholar
• Walton, C. C., Pichel, W. G., Sapper, J. F., & May, D. A. (1998). The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. Journal of Geophysical Research, 103(C12), 27999–28012.
   Web of Science ®Google Scholar
• Wentz, F. J. T., Meer, C. T., Gentemann, M. C., & Brewer, M. (2014). Remote sensing systems AQUA AMSR-E daily, weekly, and monthly. Environmental suite on 0.25 deg grid, Version 8.2. Remote Sensing Systems. Santa Rosa, CA. Retrieved from www.remss.com/missions/amsr
   Google Scholar
• Wu, T. S., Song, G. J., Ma, X. J., Xie, K. Q., Gao, X. P., & Jin, X. X. (2008). Mining geographic episode association patterns of abnormal events in global earth science data. Science in China Series E: Technological Sciences, 51(S1), 155–164.
   Google Scholar
• Xu, Y. F., Xue, C. J., & He, Y. W. (2021, August 20). A dataset of sea surface temperature abnormal change process objects (GDPoSSTA V1.0), Global ocean 1982-2009. V1. Science Data Bank. Retrieved from https://datapid.cn/31253.11.sciencedb.j00076.00090.
   Google Scholar
• Xue, C. J., Dong, Q., & Qin, L. (2015b). A cluster-based method for marine sensitive object extraction and representation. Journal of Ocean University of China, 14(4), 612–620.
   Web of Science ®Google Scholar
• Xue, C. J., Liu, J. Y., Yang, G., & Wu, C. (2019b). A process-oriented method for tracking rainstorms with a time-series of raster datasets. Applied Sciences, 9(12), 2468.
   Google Scholar
• Xue, C. J., Song, W. J., Qin, L. J., Dong, Q., & Wen, X. Y. (2015a). A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 38, 105–114.
   Web of Science ®Google Scholar
• Xue, C. J., Wu, C. B., Liu, J. Y., & Su, F. Z. (2019a). A novel process-oriented graph storage for dynamic geographic phenomena. ISPRS International Journal of Geo-Information, 8(2), 100.
   Web of Science ®Google Scholar
• Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., … Dickinson, R. (2013). The role of satellite remote sensing in climate change studies. Nature Climate Change, 3(10), 875–883.
   Web of Science ®Google Scholar
• Yu, S. Y., Fan, L., Zhang, Y., Zheng, X. T., & Li, Z. (2021). Reexamining the Indian summer monsoon rainfall–ENSO relationship from its recovery in the 21st century: Role of the Indian ocean SST anomaly associated with types of ENSO evolution. Geophysical Research Letters, 48(12), e2021GL092873.
   Web of Science ®Google Scholar
• Zhang, P. S., Tan, P. N., Steinbach, M., Kumar, V., Shekhar, S., Klooster, S., & Potter, C. (2005). Discovery of patterns in the earth science data using data mining. In J. Zurada & M. Kantardzic (Eds.), Next generation of data mining applications.  Wiley-IEEE Press, 167-188, February 2005, ISBN: 0-471-65605-4. (eBook).
   Google Scholar