Identifying and analysing uncertainty structures in the TRMM microwave imager precipitation product over tropical ocean basins

ABSTRACT

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. ‘climate method’) demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45–88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1° × 1° spatial/1 month temporal resolution, and highest associated with a 3° × 3° spatial/3 month temporal resolution). The second framework (i.e. ‘weather method’) explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50% of the daily uncertainty variability, with only limited dependence on the regions of interest.

Acknowledgements

This work was supported by NOAA National Centers for Environmental Information through grant number NA14OAR4320125. The authors wish to acknowledge Wesley Berg from Colorado State University for insightful discussions and comments. Appreciation is also extended to David Henderson from Colorado State University for providing the precipitation cluster datasets used in this study.

Disclosure statement

No potential conflict of interest was reported by the authors

Additional information

Funding

This work was supported by the NOAA National Centers for Environmental Information [Grant Number: NA14OAR4320125].