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An Object-Based Verification Method for Microscale Weather Analysis Module: Application to a Wind Speed Forecasting Model for the Korean Peninsula
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 Title & Authors
An Object-Based Verification Method for Microscale Weather Analysis Module: Application to a Wind Speed Forecasting Model for the Korean Peninsula
Kim, Hea-Jung; Kwak, Hwa-Ryun; Kim, Sang-il; Choi, Young-Jean;
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A microscale weather analysis module (about 1km or less) is a microscale numerical weather prediction model designed for operational forecasting and atmospheric research needs such as radiant energy, thermal energy, and humidity. The accuracy of the module is directly related to the usefulness and quality of real-time microscale weather information service in the metropolitan area. This paper suggests an object based verification method useful for spatio-temporal evaluation of the accuracy of the microscale weather analysis module. The method is a graphical method comprised of three steps that constructs a lattice field of evaluation statistics, merges and identifies objects, and evaluates the accuracy of the module. We develop lattice fields using various evaluation spatio-temporal statistics as well as an efficient object identification algorithm that conducts convolution, masking, and merging operations to the lattice fields. A real data application demonstrates the utility of the verification method.
lattice field of evaluation statistics;microscale weather analysis module;object-based verification method;spatio-temporal data;time series statistic;
 Cited by
Boo, K. O. and Oh, S. N. (2000). Characteristics of spatial and temporal distribution of air temperature in Seoul, Journal of the Korean Meteorological Society, 36, 499-506.

Christian, B., David, R. H. and Pierre, H. (2004). Medical image computing and computer-assisted intervention-MICCAI 2004, In Proceeding of the 7th International Conference, 26-29.

Davis, C. A., Brown, B. G. and Bullock, R. G. (2006a). Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas, Monthly Weather Review, 134, 1772-1784. crossref(new window)

Davis, C. A., Brown, B. G. and Bullock, R. G. (2006b). Object-based verification of precipitation forecasts. Part II: Application to convective rain systems, Monthly Weather Review, 134, 1785-1795. crossref(new window)

Dette, H. and Pararoditis, E. (2007). Testing equality of spectral densities (Technical Report), Komplexitatsreduktion in Multivariaten Datenstrukturen, Universitat Dortmund.

Developmental Testbed Center (2013). Model evaluation tools version 4.1 (MET v4.1), Boulder, Colorado, USA.

Doswell, C. A., Davies-Jones, R. and Keller, D. L. (1990). On summary measures of skill in rare event forecasting based on contingency tables, Weather and Forecasting, 5, 576-585. crossref(new window)

Giri, N. (1965). On the complex analogues of and tests, The Annals of Mathematical Statistics, 36, 664-670. crossref(new window)

Gomez, R. M. P. and Drouiche, K. (2002). A test of homogeneity for autoregressive processes, International Journal of Adaptive Control and Signal Processing, 16, 231-242. crossref(new window)

Ha, J. C., Lee, D. H., Lee, J. S., Lee, H. C. and Chang, D. E. (2010). Korea local analysis and prediction system, In Proceedings of the Autumn Meeting of Korean Meteorological Society, 218-219.

Hannan, E. J. (1970). Multiple Time Series, Wiley, New York.

Hitchens, N. M., Baldwin, M. E. and Trapp, R. J. (2012). An object-oriented characterization of extreme precipitation-producing convective systems in the midwestern United States, Monthly Weather Review, 140, 1356-1366. crossref(new window)

Kim, Y. H., Ryoo, S. B., Park, I. S., Koo, H. J. and Nam, J. C. (2008). Does the restoration of an inner-city stream in Seoul affect local thermal environment, Theoretical and Applied Climatology, 92, 239-248. crossref(new window)

Korea Meteorological Administration (2011). Public satisfaction survey on national weather service in 2011, Korea Meteorological Administration, Korea.

Li, J., Hsu, K., AghaKouchak, A. and Sorooshian, S. (2015). An object-based approach for verification of precipitation estimation, International Journal of Remote Sensing, 36, 513-529. crossref(new window)

Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed, Wiley, New York.

Marahaj, E. A. (2002). Comparison of non-stationary time series in the frequency domain, Computational Statistics and Data Analysis, 40, 131-141. crossref(new window)

Mora-Ramirez, M. A. and Garcia, A. R. (2012). Evaluation of WRF-CHEM simulations with the unified post processor (UPP) and model evaluation tool (MET), In Proceeding of the 11th Annual CMAS Conference, 15-17.

Shumway, R. H. and Stoffer, D. S. (2010). Time Series Analysis and Its Applications With R Examples, 3nd ed., Springer.

Skok, G., Tribbia, J., Rakovec, J. and Brown, B. (2009). Object-based analysis of satellite-derived precipitation systems over the low and midlatitude Pacific Ocean, Monthly Weather Review, 137, 3196-3218. crossref(new window)

Skok, G., Tribbia, J. and Rakovec, J. (2010). Object-based analysis and verification of WRF model precipitation in the low and midlattitude Pacific Ocean, Monthly Weather Review, 138, 4561-4575. crossref(new window)

Toshiaki, I., Shimodozono, K. and Hanaki, K. (1999). Impact of anthropogenic heat on urban climate in Tokyo, Atmospheric Environment, 33, 3897-3909. crossref(new window)

Wilks, D. S. (1995). Statistical Method in the Atmospheric Sciences, Academic Press, San Diego.

Wold, H. O. A. (1954). A Study in the Analysis of Stationary Time Series, 2nd Ed., Almqvist and Wiksell, Uppsala.