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Open AccessFeature PaperCommunication

On the Spatial and Temporal Sampling Errors of Remotely Sensed Precipitation Products

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 233-302E, Pasadena, CA 91109, USA
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(11), 1127;
Received: 25 September 2017 / Revised: 18 October 2017 / Accepted: 2 November 2017 / Published: 5 November 2017
Observation with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and duration of precipitation events. In this study, the errors resulting from temporal and spatial sampling of precipitation events were quantified and examined using the latest version (V4) of the Global Precipitation Measurement (GPM) mission integrated multi-satellite retrievals for GPM (IMERG), which is available since spring of 2014. Relative mean square error was calculated at 0.1° × 0.1° every 0.5 h between the degraded (temporally and spatially) and original IMERG products. The temporal and spatial degradation was performed by producing three-hour (T3), six-hour (T6), 0.5° × 0.5° (S5), and 1.0° × 1.0° (S10) maps. The results show generally larger errors over land than ocean, especially over mountainous regions. The relative error of T6 is almost 20% larger than T3 over tropical land, but is smaller in higher latitudes. Over land relative error of T6 is larger than S5 across all latitudes, while T6 has larger relative error than S10 poleward of 20°S–20°N. Similarly, the relative error of T3 exceeds S5 poleward of 20°S–20°N, but does not exceed S10, except in very high latitudes. Similar results are also seen over ocean, but the error ratios are generally less sensitive to seasonal changes. The results also show that the spatial and temporal relative errors are not highly correlated. Overall, lower correlations between the spatial and temporal relative errors are observed over ocean than over land. Quantification of such spatiotemporal effects provides additional insights into evaluation studies, especially when different products are cross-compared at a range of spatiotemporal scales. View Full-Text
Keywords: precipitation; remote sensing; spatial error; temporal error precipitation; remote sensing; spatial error; temporal error
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MDPI and ACS Style

Behrangi, A.; Wen, Y. On the Spatial and Temporal Sampling Errors of Remotely Sensed Precipitation Products. Remote Sens. 2017, 9, 1127.

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