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Effect of data coverage on the estimation of mean and variability of precipitation at global and regional scales
Author(s) -
Wan Hui,
Zhang Xuebin,
Zwiers Francis W.,
Shiogama Hideo
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/jgrd.50118
Subject(s) - precipitation , environmental science , climatology , sampling (signal processing) , undersampling , magnitude (astronomy) , estimation , spatial variability , meteorology , statistics , geography , computer science , mathematics , geology , management , economics , physics , filter (signal processing) , astronomy , artificial intelligence , computer vision
We use monthly precipitation simulated by a high‐resolution global climate model (the MIROC4h) to examine the effects of spatial and temporal coverage on the estimation of mean, trend, and variability of precipitation for large land regions and the global land area. We consider spatial and temporal coverage typical of publicly available precipitation data sets of in situ station observations. We find that the spatial coverage of these data sets is not sufficient for the estimation of total precipitation for the global and hemispheric land areas and for some large regions considered. Estimates of global and hemispheric total land precipitation tend to be biased to higher values due to undersampling in low precipitation regions. The existing station coverage may nevertheless provide reasonable estimates for the magnitude of trend and variability in global to regional land area mean precipitation. However, the incomplete spatial coverage of the observational records results in larger sampling errors in trend estimates, making it harder to detect statistically significant trends. Publicly available gridded precipitation data that are based on larger collection of stations (all of which are not publicly available) may provide a better alternative for the time being.