The objective of this proposal is to quantify the uncertainties in NASA’s satellite-based precipitation sensor ESDRs, and to identify the propagation of their systematic errors into merged multi-sensor precipitation measurements. We will analyze both the random errors and the systematic errors in 9 long-term microwave precipitation sensor measurements for complete uncertainty quantification. The approach for this proposal is to employ, improve and expand the scientifically rigorous, peer-recognized methods from our previous studies, and adopt well-established methods from other fields, with the support of a high-throughput data processing infrastructure and an extensive precipitation ESDR archive. Specifically, we will:
Determine the systematic errors in 9 major precipitation sensor ESDRs over a time period of 8-10 years, to establish the error characteristics of these sensors over the longest time-span possible. Such long-term error quantification will lay the foundation for uncertainty analysis on both the weather and climate time scales;
Quantify the random errors in the 9 precipitation sensor ESDRs with well-established methodologies adopted from existing studies and from other fields which do not require the use of reference data and their uncertainties (e.g., Stoffelen 1998; Caires and Sterl 2003; Tian and Peters-Lidard 2010); and
Understand and quantify the propagation of the sensor errors to downstream merged precipitation products, to connect and attribute the error characteristics documented in existing studies (e.g. Tian et al. 2009, 2010a) to the upstream sensor measurement errors determined in 1 and 2. The proposed work will be critical in quantifying the uncertainties in NASA’s precipitation ESDRs by determining both the systematic and random errors, and in tracking down the error sources and their relative contributions. These results on uncertainty analysis will be as significant as the data records themselves, and will be indispensible for a wide range of applications such as Earth system model data assimilation, sensor calibration and validation, multi-sensor algorithm development, climate trend analysis and decision making.
Project PI: Yudong Tian/GEST/UMBC
UMBC/GEST, Hydrological Sciences Branch, NASA/GSFC Code 614.3, Greenbelt, MD 20771, United States
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