The observation data from ground surface meteorological stations is an important basis on which climate change research is carried out, while the homogenization of the data is necessary for improving the quality and h...The observation data from ground surface meteorological stations is an important basis on which climate change research is carried out, while the homogenization of the data is necessary for improving the quality and homogeneity of the time series. This paper reviews recent advances in the techniques of identifying and adjusting inhomogeneity in climate series. We briefly introduce the results of applying two commonly accepted and well-developed methods (RHtest and MASH) to surface climate observations such as temperature and wind speed in China. We then summarize current progress and problems in this field, and propose ideas for future studies in China. Along with collecting more detailed metadata, more research on homogenization technology should be done in the future. On the basis of comparing and evaluating advantages and disadvantages of different homogenization methods, the homogenized climate data series of the last hundred years should be rebuilt.展开更多
The task of climate observation data processing is central to the quality of an assessment of future climate change impact. The current state-of-the-art is based on the long-running observation records of the meteorol...The task of climate observation data processing is central to the quality of an assessment of future climate change impact. The current state-of-the-art is based on the long-running observation records of the meteorological stations. However, it is common for the developing states to have only relatively short and/or intermittent record histories. The issue becomes even more aggravated under an effort to assess the climatic trends for specific territories with few meteorological stations. The paper offers a simple and effective technique to handle the climate observations; the technique makes the most complete use of an available data set by counting the data provided by all meteorological stations including those with short records and omissions. The method is based on numeric differentiation of source data samples.展开更多
基金supported by the National Program on Key Basic Research Project (No. 2010CB951602, 2009CB421401)National Science and Technology Ministry (No. 2008BAK50B07)+1 种基金China Special Fund for Meteorological Research in the Public Interest (No. 200906041-052)the Project of National Natural Science Foundation of China (No. 40805060)
文摘The observation data from ground surface meteorological stations is an important basis on which climate change research is carried out, while the homogenization of the data is necessary for improving the quality and homogeneity of the time series. This paper reviews recent advances in the techniques of identifying and adjusting inhomogeneity in climate series. We briefly introduce the results of applying two commonly accepted and well-developed methods (RHtest and MASH) to surface climate observations such as temperature and wind speed in China. We then summarize current progress and problems in this field, and propose ideas for future studies in China. Along with collecting more detailed metadata, more research on homogenization technology should be done in the future. On the basis of comparing and evaluating advantages and disadvantages of different homogenization methods, the homogenized climate data series of the last hundred years should be rebuilt.
文摘The task of climate observation data processing is central to the quality of an assessment of future climate change impact. The current state-of-the-art is based on the long-running observation records of the meteorological stations. However, it is common for the developing states to have only relatively short and/or intermittent record histories. The issue becomes even more aggravated under an effort to assess the climatic trends for specific territories with few meteorological stations. The paper offers a simple and effective technique to handle the climate observations; the technique makes the most complete use of an available data set by counting the data provided by all meteorological stations including those with short records and omissions. The method is based on numeric differentiation of source data samples.
