Browse > Article
http://dx.doi.org/10.7780/kjrs.2017.33.5.2.6

A Study on the Method of Producing the 1 km Resolution Seasonal Prediction of Temperature Over South Korea for Boreal Winter Using Genetic Algorithm and Global Elevation Data Based on Remote Sensing  

Lee, Joonlee (Division of Earth Environmental System, Pusan National University)
Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
Jung, Myung-Pyo (Climate Change and Agroecology Division, Department of Agricultural Environment, National Institute of Agricultural Science, Rural Development)
Shim, Kyo-Moon (Climate Change and Agroecology Division, Department of Agricultural Environment, National Institute of Agricultural Science, Rural Development)
Publication Information
Korean Journal of Remote Sensing / v.33, no.5_2, 2017 , pp. 661-676 More about this Journal
Abstract
This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.
Keywords
Remote sensing; Statistical downscaling; Seasonal prediction; CGCM; Genetic Algorithm; Temperature;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Park, C.K., W.S. Lee, and W.T. Yun, 2008. Statistical downscaling for multi-model ensemble prediction of summer monsoon rainfall in the Asia-Pacific region using geopotential height field, Advances in Atmospheric Sciences, 25: 867-884.   DOI
2 Saha, S., and and Coauthors, 2014. The NCEP climate forecast system version 2, Journal of Climate, 27(6): 2185-2208.   DOI
3 Skourkeas, A., F. Kolyva-Machera, and P. Maheras, 2010. Estimation of mean maximum summer and mean minimum winter temperatures over Greece in 2070-2100 using statistical downscaling methods, Euro-Asian Journal of Sustainable Energy Development Policy, 2: 33-44.
4 Lee. J.L, J.B. Ahn, and H.G. Jeong, 2016. A Study on the Method for Estimating the 30 m-Resolution Daily Temperature Extreme Value Using PRISM and GEV Method, Atmosphere, 26(4): 697-709 (in Korean with English abstract).   DOI
5 Lee, S.H., I.H. Heo, K.M. Lee, S.Y. Kim, Y.S. Lee, and W.T. Kwon, 2008. Impacts of Climate Change on Phenology and Growth of Crops: In the Case of Naju, Journal of the Korean Geographical Society, 43(1): 20-35 (in Korean with English abstract).
6 Lee, Y.H., S.K. Park, and D.E. Chang, 2006. Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast. Annales Geophysicae, 24(12): 3185-3189.   DOI
7 Jeong, D.I., A. St-Hilaire, T.B.M.J. Ouarda, and P. Gachon, 2012. Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada, Stochastic Environmental Research and Risk Assessment, 26: 633-653.   DOI
8 Jo, S., and J.B. Ahn, 2014. Improvement of CGCM prediction for wet season precipitation over Maritime Continent using a bias correction method, International Journal of Climatology, 35(13): 3721-3732.   DOI
9 Jolliffe, I. T., and D.B. Stephenson, 2003. Forecast verification: a practitioner's guide in atmospheric science. p. 240, John Wiley, Chichester, U. K.
10 Min, Y.M., V.N. Kryjov, and S.M. Oh, 2014. Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983-2003) and real-time forecasts (2008-2013), Journal of Geophysical Research, 119(21): 132-143
11 Wang, G., R. Kleeman, N. Smith, and F. Tseitkin, 2001. The BMRC coupled general circulation model ENSO forecast system, Monthly Weather Review, 130: 975?991.
12 Sun, J.Q., and J.B. Ahn, 2011. A GCM-based forecasting model for the landfall of tropical cyclones in China, Advances in Atmospheric Sciences, 28: 1049-1055.   DOI
13 Sun, J.Q., and J.B. Ahn, 2015. Dynamical seasonal predictability of the arctic oscillation using a CGCM, International Journal of Climatology, 35(7): 1342-1353.   DOI
14 Szolgay, J., J. Parajka, S. Kohnova, and K. Hlavcova, 2009. Comparison of mapping approaches of design annual maximum daily precipitation, Atmospheric Research, 92(3): 289-307.   DOI
15 Wilks, D.S., 1995. Statistical Methods in the Atmospheric Sciences. Academic Press, p. 467.
16 Yoon, S.T., 2005. Effect of Global Warming and Croping with Vulnerability of Agricultural Production, The Korean Society of International Agriculture, 17(3): 199-207 (in Korean with English abstract).
17 Hagedorn, R., J. Francisco, Doblas-reyes, and T. N. Palmer, 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting-I. Basic concept. Tellus, 57A, 3, 219-233.
18 Molteni, F., T. Stockdale, M.A. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer, and F. Vitart, 2011. The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memorandum 656, Reading, U.K.
19 Pacanowski, R.C. and S.M. Griffies, 1998. MOM 3.0 Manual. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, USA.
20 Frey, H., and F. Paul, 2012. On the suitability of the SRTM DEM and ASTER GDEM for the compilation of topographic parameters in glacier inventories, International Journal of Applied Earth Observation and Geoinformation, 18: 480-490.   DOI
21 Holland, J.H., 1975. Adaption in Natural and Artificial Systems. University of Michigan Press, p. 288.
22 Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (Eds.), IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, U.K.
23 Kharin, V.V., F.W. Zwiers, and N. Gagnon, 2001. Skill of seasonal hindcasts as a function of the ensemble size, Climate Dynamics, 17: 835-843.   DOI
24 Kiehl, J.T., J.J. Hack, G.B. Bonan, B.A. Boville, B.P. Briegleb, D.L. Williamson, and P.J. Rasch, 1996. Description of the NCAR Community Climate Model (CCM3), NCAR Technical Note, p. 152.
25 Kug, J.S., J.Y. Lee, I.S. Kang, B. Wang, and C.K. Park, 2008. Optimal Multi-model Ensemble Method in Seasonal Climate Prediction, Journal of the Korean Meteorological Society, 44: 259-267.
26 Kim, M.K., M.S. Han, D.H. Jang, S.G. Baek, W.S. Lee, Y.H. Kim, and S. Kim, 2012. Production Technique of Observation Grid Data of 1 km Resolution, Journal of climate research, 7(1): 55-68 (in Korean with English abstract).
27 Kim, H.J., and J.B. Ahn, 2015. Improvement in Prediction of the Arctic Oscillation with a Realistic Ocean Initial Condition in a CGCM, Journal of Climate, 28(22): 8951-8967.   DOI
28 Bonan, G.B., 1998. The land surface climatology of the NCAR Land Surface Model (LSM 1.0) coupled to the NCAR Community Climate Model (CCM3), Journal of Climate, 11: 1307-1326.   DOI
29 Ahrens, C.D., 2012. Meteorology today: An introduction to weather, climate, and the environment, Cengage Learning, California, USA.
30 Barnes, S.L., 1964. A technique for maximizing details in numerical weather map analysis, Journal of Applied Meteorology and Climatology, 3(4): 396-409.   DOI
31 Brankovic, C., T.N. Palmer, F. Molteni, S. Tibaldi, and U. Cubasch, 1990. Extended-range predictions with ECMWF models: Time-lagged ensemble forecasting, Quarterly Journal of the Royal Meteorological Society, 116(494): 867-912.   DOI
32 Cressman, G.P., 1959. An operational objective analysis system, Monthly Weather Review, 87: 367?374   DOI
33 Brunetti, M., M. Maugeri, T. Nanni, C. Simolo, and J. Spinoni, 2014. High-resolution temperature climatology for Italy: Interpolation method intercomparison, International Journal of Climatology, 34: 1278-1296.   DOI
34 Charbonneau P., 2002. An introduction to genetic algorithms for numerical optimization, NCAR Technical Note TN-450 IA, National Center for Atmospheric Research, Boulder, Colo, USA.
35 Choi, J.M., 2013. Spatial Accuracy of Medium Resolution ASTER GDEM Data, The Korean Association of Professional Geographers, 47(1): 61-69 (in Korean with English abstract).
36 Daly, C., R.P. Neilson, and D.L. Phillips, 1994. A statistical-topograhic model for mapping climatological precipitation over mountainous terrain, Journal of Applied Meteorology and Climatology, 33: 140-158.   DOI
37 Ahn, J.B. and J.L. Lee, 2015. Comparative Study on the Seasonal Predictability Dependency of Boreal Winter 2m Temperature and Sea Surface Temperature on CGCM Initial Conditions, Atmosphere, 25(2): 353-366 (in Korean with English abstract).   DOI
38 Ahn, J.B., and J.A. Lee, 2001. Numerical Study on the Role of Sea-ice Using Ocean General Circulation Model, Atmosphere, 6(4): 225-233 (in Korean with English abstract).
39 Ahn, J.B., J.L. Lee, and E.S. Im, 2012. The reproducibility of surface air temperature over South Korea using dynamical downscaling and statistical correction, Journal of Meteorological Society Japan, 90(4): 493-507.   DOI
40 Ahn, J.B., J. Hur, and A.Y. Lim, 2014. Estimation of fine-scale daily temperature with 30 m-resolution using PRISM, Atmosphere, 24(1): 101-110 (in Korean with English abstract).   DOI
41 Ahn, J.B. and J.L. Lee, 2016. A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction, Journal of Geophysical Research, 121(16): 9263-9277.   DOI