基于WRF和统计方法的气温空间尺度分析

doi:10.3724/SP.J.1047.2013.00546

Abstract

Abstract:

Due to intensified global warming, more and more global and regional issues become the focus of research. In the context of global scale change, the mechanism of how regional climate makes feedback to global environment change and how regional climate change influences large-scale environment in turn is a difficult point for climate research. However, the transformation between scales provides ideas to solve these difficulties. With the development of climate models, nesting regional climate model into global climate models to simulate is an effective downscaling way, however the resolution limitations of the regional climate model cause simulation accuracy of mountainous areas and other complex terrain much lower than expected. WRF model, a mesoscale climate model with resolution from several meters to thousands kilometers and with two-way nested features, provides opportunities for transforming between scales dynamically. In this paper, we firstly use the WRF model to simulate the temperature at different scales and compare with 15 climate-stations' in-site value. Through the comparison, we can conclude that the simulated values are more similar with the measured ones with the resolution becomes better. Then we used statistical downscaling method to downscale the temperature from 27km to 3km and compared with the WRF downscaling results and ANUSPLIN interpolation. The results showed that the downscaling from statistical method had the same tendency with the WRF downscaling and ANUSPLIN interpolation results, but compared with the latter, the former downscaling had obvious Mosaic effect. The statistical based downscaling method only considered the influence of elevation change on temperature not other factors, while the temperature is influenced by various factors such as slope, slope aspect and land cover types. In the further research we will consider more factors and analyze the relationship between temperature and related factors more comprehensively, in this way we can make the statistical downscaling more realistic.

Key words: temperature, statistical methods, WRF, spatial scale analysis, downscaling

XU Ling-Xue, ZHANG Shu-Wen, LIU Ting-Xiang, BO Kun, YANG Jiu-Chun. Spatial Scale Analysis of Temperature Based on WRF and Statistical Methods[J]., 2013, 15(4): 546-553.DOI:10.3724/SP.J.1047.2013.00546

References

[1] 赵文武,傅伯杰,陈利顶.尺度推绎研究中的几点基本问题[J].地球科学进展,2002,17(6):905-911.

[2] Babaei M., King P R. A modified nested-gridding for upscaling-downscaling in reservoir simulation[J]. Transport in Porous Media, 2012,93(3):753-775.

[3] Gutmann E D, Rasmussen R M, Liu C, et al. A Comparison of statistical and dynamical downscaling of winter precipitation over complex terrain[J]. Journal of Climate, 2012,25(1):262-281.

[4] Ahammer H, DeVaney T T J, Tritthart H A. How much resolution is enough? Influence of downscaling the pixel resolution of digital images on the generalised dimensions[J]. Physica D-Nonlinear Phenomena, 2003,181(3-4):147-156.

[5] 张娜.生态学中的尺度问题——尺度上推[J].生态学报,2007,27(10):4252-4266.

[6] Jeong D I, St-Hilaire A, Ouarda T B M J, et al. Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada[J]. Stochastic Environmental Research and Risk Assessment, 2012,26(5):633-653.

[7] Quintana Seguí P, Ribes A, Martin E, et al. Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins[J]. Journal of Hydrology, 2010,383(1-2):111-124.

[8] Gangopadhyay S. Statistical downscaling using K-nearest neighbors[J]. Water Resources Research, 2005,41(2):1-23

[9] Caldwell P, Chin H-N S, Bader D C, et al. Evaluation of a WRF dynamical downscaling simulation over California[J]. Climatic Change, 2009,95(3-4):499-521.

[10] Druyan L M, Fulakeza M, et al. Dynamic downscaling of seasonal climate predictions over Brazil[J]. Journal of Climate, 2002,15(23):3411-3426.

[11] Teutschbein C, Wetterhall F, Seiberb. Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale[J]. Climate Dynamics, 2011,37(9-10):2087-2105.

[12] Wilby R L, Hassan H, et al. Statistical downscaling of hydrometeorological variables using general circulation model output[J]. Journal of Hydrology, 1998,205(1-2):1-19.

[13] Widmann M, Bretherton C S, Salathe E. P. Statistical precipitation downscaling over the Northwestern United States using numerically simulated precipitation as a predictor[J]. Journal of Climate, 2003,16(5):799-816.

[14] 丁一汇,林而达,何建坤.中国气候变化——科学、影响、适应与对策研究[M].北京:中国环境科学出版社,2009.

[15] Almazroui M. Dynamical downscaling of rainfall and temperature over the Arabian Peninsula using RegCM4[J]. Climate Research, 2012,52(1):49-62.

[16] Oh J H., Kim T, Kim M K, et al. Regional climate simulation for Korea using dynamic downscaling and statistical adjustment[J]. Journal of the Meteorological Society of Japan, 2004,82(6):1629-1643.

[17] 张树文,张养贞,李颖,等.东北地区土地利用/覆被时空特征分析[M].北京:科学出版社,2006.

[18] 刘廷祥,张树文.东北地区农林交错带土地利用变化及其对区域气温影响模拟研究[D].长春:中国科学院东北地理与农业生态研究所,2012.

[19] 邓伟,陈海波,马振升,等.NCEP FNL全球分析资料的解码及其图形显示[J].气象与环境科学,2009,32(3):78-82.

[20] 蔡红艳.基于MODIS 物候特征的黑龙江流域植被动态编绘研究[D].长春:中国科学院东北地理与农业生态研究所,2010.

[21] Giorgi F, Francisco R, et al. Effects of a subgrid-scale topography and land use scheme on the simulation of surface climate and hydrology. Part I: Effects of temperature and water vapor disaggregation[J]. Journal of Hydrometeorology, 2003,4(2):317-333.

[22] Hewitson B C, Crane R G. Climate downscaling: Techniques and application[J]. Climate Research, 1996,7(2):85-95.

Spatial Scale Analysis of Temperature Based on WRF and Statistical Methods

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