Quality control of daily meteorological data in China, 1951–2000: a new dataset

Abstract

Long-term observational data are essential for understanding local and regional climate and climate change. These data are also important for hydrological designs and agricultural decision making. This study examined the daily meteorological data from 726 stations in China from 1951 to 2000, and developed an unprecedented climatic dataset that contains 10 daily variables: maximum and minimum surface air temperatures, mean surface air temperature, skin surface temperature, surface air relative humidity, wind speed, wind gust, sunshine duration hours, precipitation, and pan evaporation. The characteristics of the original stations' data and quality-control methods designed and used in developing this dataset are detailed. The quality-control procedures identified less than 0.05% of the data records as being erroneous because of typos and incorrect units, reading, or data coding. When the spatial and temporal consistency of the variables' time series were inspected, nearly 37.9% of the stations were found to have one or more variables with inconsistent changes. The sources causing the temporal inconsistency/discontinuity were evaluated, and a method was developed and applied to adjust those data segments containing inconsistent values. The resulting data series, as an alternative to the original quality-controlled series, showed both spatially and temporally consistent trends in the occurrence frequency of extreme climate events compared with the unadjusted data series. Finally, the quality-controlled daily data were gridded to a 1.0°×1.0°grid system covering China after the erroneous and missing data were estimated. This new dataset opens up opportunities for analysing and understanding the climate variability and climate change in China.

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