doi:

DOI: 10.3724/SP.J.1047.2014.00819

Journal of Geo-information Science (地球信息科学学报) 2013/15:6 PP.819-828

A Statistical Downscaling Approach of NCEP/NCAR Reanalysis Temperature Data


Abstract:
Near-surface air temperature is an important controlling parameter for land surface processes, and is critical to ecological, environmental and hydrological modeling. Temperature records observed at meteorological stations have been widely used, but there has been an increasing need for temperature data in grid for modeling purposes. Although grid temperature can be estimated from in-situ temperature records using interpolation algorithm, low accuracy have been reported due to limited ground stations and their clustering distribution, especially when there were insufficient sites to represent all land cover types and terrain conditions in the area. NCEP/NCAR reanalysis project uses a frozen state-of-art global data assimilation system and a database as complete as possible. Although the NCEP/NCAR data has a coarse resolution (0.5 degree), it provides global, consistent, and long term estimation of climate variables. This paper presents a downscaling approach to derive monthly temperature at 1km resolution from the NCEP/NCAR by utilizing derived relationships between monthly aggregated NCEP/NCAR temperature and other ground elements, i.e., terrain, vegetation and geographic locations. Regression tree model was chosen to detect the possible relationships. Monthly temperature with 1km resolution for China land area from 2000 to 2010 has been produced using the approach. The final predicted temperatures were compared with observed records at 380 meteorological stations in China. The results indicate that the downscaled estimations can represent spatial distribution and trends and the magnitude of inter-month temperature with R2 ranging from 0.861 to 0.95, and RMSE from 1.88℃ to 2.681℃.

Key words:statistical downscaling,mean monthly temperature,NCEP/NCAR,NDVI,DEM

ReleaseDate:2015-04-17 13:34:26



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