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http://dx.doi.org/10.7780/kjrs.2015.31.6.4

Development and Analysis of COMS AMV Target Tracking Algorithm using Gaussian Cluster Analysis  

Oh, Yurim (Department of Atmospheric sciences, Division of Earth Environmental System, Pusan National University)
Kim, Jae Hwan (Department of Atmospheric sciences, Division of Earth Environmental System, Pusan National University)
Park, Hyungmin (Department of Atmospheric sciences, Division of Earth Environmental System, Pusan National University)
Baek, Kanghyun (Department of Atmospheric sciences, Division of Earth Environmental System, Pusan National University)
Publication Information
Korean Journal of Remote Sensing / v.31, no.6, 2015 , pp. 531-548 More about this Journal
Abstract
Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.
Keywords
Atmospheric motion vector; target search; COMS; gaussian mixture model;
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1 Bedka, K.M. and J.R. Mecikalski, 2005. Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows. Journal of Applied Meteorology, 44(11): 1761-1772.   DOI
2 Bedka, K.M., C.S. Velden, R.A. Petersen, W.F. Feltz, and J.R. Mecikalski, 2009. Comparisons of satellite-derived atmospheric motion vectors, rawinsondes, and NOAA Wind Profiler observations. Journal of Applied Meteorology and Climatology, 48(8): 1542-1561.   DOI
3 Bigdeli, E., M. Mohammadi, B. Raahemi, and S. Matwin, 2015. Incremental Cluster Updating Using Gaussian Mixture Model. In Advances in Artificial Intelligence, Springer International Publishing, pp. 264-272.
4 Blyth, A.M., 1993. Entrainment in cumulus clouds. Journal of applied meteorology, 32(4): 626-641.   DOI
5 Borde, R. and R. Oyama, 2008. A direct link between feature tracking and height assignment of operational atmospheric motion vectors. Proc. of 2008 International Wind Workshop, Maryland, USA, Apr. pp. 14-18
6 Borde, R., M. Doutriaux-Boucher, G. Dew, and M. Carranza, 2014. A direct link between feature tracking and height assignment of operational EUMETSAT atmospheric motion vectors Journal of Atmospheric and Oceanic Technology, 31(1): 33-46.   DOI
7 Bresky, W.C., J.M. Daniels, A.A. Bailey, and S.T. Wanzong, 2012. New methods toward minimizing the slow speed bias associated with atmospheric motion vectors. Journal of Applied Meteorology and Climatology, 51(12): 2137-2151.   DOI
8 Choi, Y.S. and C.H. Ho, 2015. Earth and environmental remote sensing community in South Korea: A review. Remote Sensing Applications: Society and Environment.
9 Da, Cheng, 2015. Preliminary assessment of the Advanced Himawari Imager (AHI) measurement onboard Himawari-8 geostationary satellite. Remote Sensing Letters, 6(8): 637-646.   DOI
10 De Rooy, W.C., P. Bechtold, K. Frohlich, C. Hohenegger, H. Jonker, D. Mironov, and J.I. Yano, 2013. Entrainment and detrainment in cumulus convection: an overview. Quarterly Journal of the Royal Meteorological Society, 139(670): 1-19.   DOI
11 Deb, S.K., S. Wanzong, C.S. Velden, I. Kaur, C.M. Kishtawal, P.K. Pal, and W.P. Menzel, 2014. Height assignment improvement in Kalpana-1 atmospheric motion vectors. Journal of the Indian Society of Remote Sensing, 42(4): 679-687.   DOI
12 Han, H.Y., 2014. Introduction to Pattern Recognition. Revision. HanbitMedia.
13 Dew, G. and K. Holmlund, 2000. Investigations of cross-correlation and euclidian distance target matching techniques in the mpef environment. Proc. of 2000 International Wind Workshop, Lorne, Australia, Feb. 28-Mar. 3.
14 Fritz, S. and J.S. Winston, 1962. Synoptic use of radiation measurements from satellite TIROS II 1. Monthly Weather Review, 90(1): 1-9.   DOI
15 Genkova, I., R. Borde, J. Schmetz, J. Daniels, C. Velden, and K. Holmlund, 2008. Global atmospheric motion vector inter-comparison study. Proc. of 2008 International Wind Workshop, Maryland, USA, Apr. 14-18.
16 Holmlund, K., 1998. The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators. Weather and Forecasting, 13(4): 1093-1104.   DOI
17 Kim, D.H., C.R. Kim, K.T. Sohn, K.M. Jeong, Y.S. Chung, Y. Cho, Y.S. Choi, and H.K. Hong, 2008. Statistics, Third Edition. Freeacademy.
18 Kim, D.H. and M.H. Ahn, 2014. Introduction of the inorbit test and its performance for the first meteorological imager of the Communication, Ocean, and Meteorological Satellite. Atmospheric Measurement Techniques, 7(8): 2471-2485.   DOI
19 Kim, S., J.H. Park, M.L. Ou, H. Cho, and E.H. Sohn, 2012. Optimization of Mesoscale Atmospheric Motion Vector Algorithm Using Geostationary Meteorological Satellite Data. Atmosphere, 22(1): 1-12.   DOI
20 Kim, T.M., E.H. Lee, S.R. Chung, and J.G. Won, 2014. Study of the target selection methods for amv derivation of GEO-KOMPSAT-2A. Proc. of 2014 International Winds Working Group, Copenhagen, Denmark, Jun. 16-20.
21 National Institute of Meteorological Research, 2009. Development of Meteorological Data Processing System for Communication, Ocean and Meteorological Satellite.
22 Le Marshall, J., J. Jung, T. Zapotocny, C. Redder, M. Dunn, J. Daniels, and L.P. Riishojgaard, 2008. Impact of MODIS atmospheric motion vectors on a global NWP system. Australian Meteorological Magazine, 57(1): 45.
23 Masahiro, H., 2012. Recent status and development of atmospheric motion vectors at JMA. Proc. of 2012 International Wind Workshop, Auckland, New Zealand, Feb. 20-24.
24 Menzel, W.P., 2001. Cloud tracking with satellite imagery: From the pioneering work of Ted Fujita to the present. Bulletin of the American Meteorological Society, 82(1): 33-47.   DOI
25 Park, J.H., M.L. Ou, S. Kim, and H. Cho, 2012. Sensitivity of satellite-derived wind retrieval over cloudy scenes to target selection in tracking and pixel selection in height assignment. Geoscience and Remote Sensing, IEEE Transactions on, 50(5): 2063-2073.   DOI
26 Schmetz, J., K. Holmlund, J. Hoffman, B. Strauss, B. Mason, V. Gaertner, and L. Van De Berg, 1993. Operational cloud-motion winds from Meteosat infrared images. Journal of applied meteorology, 32(7): 1206-1225.   DOI
27 Sohn, E.H., M.J. Jang, and M.H. Ahn, 2006. Current status of AMV system at KMA, Proc. of 2006 International Winds Working Group, Beijing, China, Apr. 23-28.
28 Sohn, E.H., S.R. Chung, and J.S. Park, 2012. Current status of COMS AMV in NMSC/KMA, Proc. of 2012 International Winds Working Group, Auckland, New Zealand, Feb. pp. 16-20.
29 Velden, C.S. and K.M. Bedka, 2009. Identifying the uncertainty in determining satellite-derived atmospheric motion vector height attribution. Journal of Applied Meteorology and Climatology, 48(3): 450-463.   DOI
30 Zhang, Z., C. Chen, J. Sun, and K.L. Chan, 2003. EM algorithms for Gaussian mixtures with split-and-merge operation. Pattern recognition, 36(9): 1973-1983.   DOI
31 Zhang, L., X. Sun, and H. Zhuge, 2015. Topic discovery of clusters from documents with geographical location. Concurrency and Computation: Practice and Experience.