×

A spatio-temporal modeling framework for weather radar image data in tropical southeast Asia. (English) Zbl 1393.62125

Summary: Tropical storms are known to be highly chaotic and extremely difficult to predict. In tropical countries such as Singapore, the official lead time for the warnings of heavy storms is usually between 15 and 45 minutes because weather systems develop quickly and are of very short lifespan. A single thunderstorm cell, for example, typically lives for less than an hour. Weather radar echoes, correlated in both space and time, provide a rich source of information for short-term precipitation nowcasting. Based on a large dataset of 276 tropical storms events, this paper investigates a spatio-temporal modeling approach for two-dimensional radar reflectivity (echo) fields. Under a Lagrangian integration scheme, we model the radar reflectivity field by a spatio-temporal conditional autoregressive process with two components. The first component is the dynamic velocity field which determines the motion of the storm, and the second component governs the growth or decay of the returned radar echoes. The proposed method is demonstrated and compared with existing methods using real radar image data collected from a number of 276 tropical storm events from 2010 to 2011 in Singapore. The numerical comparison results show the advantage of the proposed method, in terms of the mean-squared-error, in modeling small-scale localized convective weather systems based on the 77 inter-monsoon season thunderstorm events.

MSC:

62P12 Applications of statistics to environmental and related topics
62M30 Inference from spatial processes
62M40 Random fields; image analysis

Software:

AS 136; spBayes
PDF BibTeX XML Cite
Full Text: DOI Euclid

References:

[1] Assunção, R. and Krainski, E. (2009). Neighborhood dependence in Bayesian spatial models. Biom. J.51 851-869.
[2] Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data, 2nd ed. Monographs on Statistics and Applied Probability 135. CRC Press, Boca Raton, FL. · Zbl 1358.62009
[3] Bertsekas, D. P. (1982). Constrained Optimization and Lagrange Multiplier Methods. Academic Press, Boston. · Zbl 0572.90067
[4] Besag, J. and Kooperberg, C. (1995). On conditional and intrinsic autoregressions. Biometrika 82 733-746. · Zbl 0899.62123
[5] Bowler, N. E. H., Pierce, C. E. and Seed, A. (2004). Development of a precipitation nowcasting algorithm based upon optical flow techniques. J. Hydrol.288 74-91.
[6] Bowler, N. E., Pierce, C. E. and Seed, A. W. (2006). A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Q. J. R. Meteorol. Soc.132 2127-2155.
[7] Brown, P. E., Diggle, P. J., Lord, M. E. and Young, P. C. (2001). Space-time calibration of radar rainfall data. J. Roy. Statist. Soc. Ser. C 50 221-241.
[8] Browning, K. A., Collier, C. G., Lark, P. R., Menmuir, P., Monk, G. A. and Owens, R. G. (1982). On the forecasting of frontal rain using a weather radar network. Mon. Weather Rev.110 534-552.
[9] Carlin, B. P. and Banerjee, S. (2003). Hierarchical multivariate CAR models for spatio-temporally correlated survival data (with discussion). In Bayesian Statistics, 7 (Tenerife, 2002) 45-63. Oxford Univ. Press, New York.
[10] Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York. · Zbl 1347.62005
[11] Dixon, M. and Wiener, G. (1993). TITAN: Thunderstorm identification, traking, analysis, and nowcasting—a radar-based methodology. J. Atmos. Ocean. Technol.10 785-797.
[12] Fuentes, M., Reich, B. and Lee, G. (2008). Spatial-temporal mesoscale modeling of rainfall intensity using gage and radar data. Ann. Appl. Stat.2 1148-1169. · Zbl 1168.62103
[13] Gelpke, V. and Künsch, H. R. (2001). Estimation of motion from sequences of images. In Spatial Statistics: Methodological Aspects and Applications. Lect. Notes Stat.159 141-167. Springer, New York. · Zbl 1021.62082
[14] Germann, U. and Zawadzki, I. (2002). Scale-dependence of the predicability of precipitation from continental radar images. Part I: Description of the methodology. Mon. Weather Rev.130 2859-2873.
[15] Haining, R. (1990). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge Univ. Press, Cambridge.
[16] Han, L., Fu, S. X., Zhao, L. F., Zheng, Y. G., Wang, H. Q. and Lin, Y. J. (2009). 3D convective storm identification, tracking, and forecasting – an enhanced TITAN algorithm. J. Atmos. Ocean. Technol.26 719-732.
[17] Hartigan, J. A. and Wong, M. A. (1979). A K-means clustering algorithm. Appl. Stat.28 100-108. · Zbl 0447.62062
[18] Horn, B. K. P. and Schunck, B. G. (1981). Determining optical flow. Artificial Intelligence 17 185-203.
[19] Jin, X., Carlin, B. P. and Banerjee, S. (2005). Generalized hierarchical multivariate CAR models for areal data. Biometrics 61 950-961. · Zbl 1087.62127
[20] Leese, J. A., Novak, C. S. and Clark, B. B. (1971). An automated technique for obtaining cloud motion from geosynchronous saellite data using cross correlation. J. Appl. Meteorol.10 118-132.
[21] Li, P. and Lai, S. T. (2004). Short-range quantitative precipitation forecasting in Hong Kong. J. Hydrol.288 189-209.
[22] Li, L., Schmid, W. and Joss, J. (1995). Nowcasting of motion and growth of precipitation with radar over a complex orography. J. Appl. Meteorol.34 1286-1300.
[23] Mariella, L. and Tarantino, M. (2010). Spatial temporal conditional auto-regressive model: A new autoregressive matrix. Aust. J. Stat.39 223-244.
[24] Marshall, J. S. and Palmer, W. M. (1948). The distribution of raindrops with size. J. Meteorol.5 165-166.
[25] National Environmental Agency Singapore (2014). Weather Statistics. Available at http://app2.nea.gov.sg/weather-climate/climate-information/weather-statistics.
[26] National Environmental Agency Singapore (2017). Challenges in Weather Forecasting. Available at http://www.nea.gov.sg/training-knowledge/weather-climate/weather-forecast/challenges-in-weather-forecasting/.
[27] Radhakrishna, B., Zawadzki, I. and Fabry, F. (2012). Predictability of precipitation from continental rada images. Part V: Growth and decay. J. Atmos. Sci.69 3336-3349.
[28] Rinehart, R. E. and Garvey, E. T. (1978). Three-dimensional storm motion detection by conventional weather radar. Nature 273 287-289.
[29] Royal Meteorological Institute of Belgium (2008). Quantitative Precipitation Forecasts based on radar observations: Principles. algorithms and operational systems, 2008/0224/52.
[30] Seed, A. W. (2003). A dynamic and spatial scaling approach to advection forecasting. J. Appl. Meteorol.42 381-388.
[31] Seed, A. W., Pierce, C. E. and Norman, K. (2013). Formulation and evaluation of a scale decomposition-based stochastic precipitation nowcast scheme. Water Resour. Res.49 6624-6641.
[32] Sigrist, F., Künsch, H. R. and Stahel, W. A. (2012). A dynamic nonstationary spatio-temporal model for short term prediction of precipitation. Ann. Appl. Stat.6 1452-1477. · Zbl 1257.62121
[33] Staniforth, A. and Cote, J. (1991). Semi-Lagrangian integration schemes for atmospheric models—a review. Mon. Weather Rev.119 2206-2223.
[34] Stern, H. and Cressie, N. (2000). Posterior predictive model checks for disease mapping models. Stat. Med.19 2377-2397.
[35] Stroud, J. R., Müller, P. and Sansó, B. (2001). Dynamic models for spatiotemporal data. J. R. Stat. Soc. Ser. B. Stat. Methodol.63 673-689. · Zbl 0986.62074
[36] Testik, F. and Gebremichael, M. (2013). Rainfall: State of the Science. American Geophysical, Union.
[37] Wall, M. M. (2004). A close look at the spatial structure implied by the CAR and SAR models. J. Statist. Plann. Inference 121 311-324. · Zbl 1036.62097
[38] Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J. and Dixon, M. (1998). Nowcasting thunderstorms: A status report. Bull. Am. Meteorol. Soc.78 2079-2099.
[39] Wolfson, M. M., Forman, B. E., Hallowell, R. G. and Moore, M. P. (1999). The Growth and Decay Storm Tracker, The 8th Conference on Aviation, Range, and Aerospace Meteorology.
[40] Xu, K., Wikle, C. K. and Fox, N. I. (2005). A kernel-based spatio-temporal dynamical model for nowcasting weather radar reflectivities. J. Amer. Statist. Assoc.100 1133-1144. · Zbl 1117.62448
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.