Gappy POD-based reconstruction of the temperature field in Tibet
- 15 Downloads
Meteorological observations in Tibet are poor in quality with a severe amount of missing data; this is mostly caused by extreme climatological conditions and higher maintenance costs. This paper focuses on the imputation of missing data and the reconstruction of the regional temperature field. Due to insufficient observation stations and complicated topography, we employ the weather research and forecasting (WRF) model to produce the proper orthogonal decomposition (POD) basis for the study region. We then develop the gappy POD method for the imputation of missing data. Both methods are compared and tested for various missing data cases, and the results show that the gappy POD method dramatically outperforms the regularized EM algorithm when the amount of missing spatial data is not severe. Furthermore, between the two methods, only the gappy POD method is capable of reconstructing the temperature field at locations where the data are absent. The gappy POD method can also be generalized for data assimilation with the assumption that the data across all model grids have missing values.
KeywordsReconstruction Gappy POD Tibet Missing value
We are immensely grateful to Prof. Pingwen Zhang at School of Mathematical Sciences of PKU and Prof. Yun Chen at National Meteorological Center of CMA for their helpful discussion and valuable suggestions. We would also like to extent our gratitude to the anonymous reviewer for their comments that greatly improved the manuscript.
- Lee K, Mavris DN (2010) Unifying perspective for gappy proper orthogonal decomposition and probabilistic principal components analysis. AIAA J 48(6):1117–1129Google Scholar
- Murray NE, Ukeiley LS (2006) Flow field dynamics in open cavity flows. AIAA Paper 2428:1–16Google Scholar
- Murray N, Seiner J (2008) The effects of gappy POD on higher-order turbulence quantities. AIAA Paper, 241Google Scholar
- Maussion F, Scherer D, Finkelnburg R, Richters J, Yang W, Yao T (2010) WRF simulation of a precipitation event over the Tibetan Plateau, China – an assessment using remote sensing and ground observations, vol 7. https://doi.org/10.5194/hessd-7-3551-2010
- Roger Barry G. (1992) Mountain weather and climate. Cambridge University Press, Cambridge, pp 251–363Google Scholar
- Robinson TD, Eldred MS, Willcox KE et al (2006) Strategies for multifidelity optimization with variable dimensional hierarchical models. In: Proceedings of the 2nd AIAA Multidisciplinary Design Optimization Specialist Conference, Newport, RI 2006(1819)Google Scholar
- Skamarock WC, Klemp JB, Dudhia J et al (2005) A description of the advanced research WRF version 2. National Center For Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div.Google Scholar
- Tveito O, Wegehenkel M, Van der Wel F, Dobesch H (2006) The Use of Geographic Information Systems in Climatology and Meteorology - Final Report COST Action 719Google Scholar
- Tveito O et al (2007) The developments in spatialization of meteorological and climatological elements. In: Dobesch H (ed) Spatial interpolation for climate data: the use of GIS in climatology and meteorology. ISTE Ltd, London, pp 73–86Google Scholar
- Tan BT (2003) Proper orthogonal decomposition extensions and their applications in steady aerodynamics. Singapore-MIT Alliance. (Thesis)Google Scholar
- Yang J, Duan K, Wu J et al (2015) Effect of data assimilation using WRF-3DVAR for heavy rain prediction on the northeastern edge of the Tibetan Plateau. Advances in Meteorology 2015(1):1–14Google Scholar