Satellite sub-pixel rainfall variability

Rain gauge networks are used to calibrate and validate quantitative precipitation estimation (QPE) methods based on remote sensing, which may be used as data sources for hydrologic models. The typical approach is to adjust (calibrate) or compare (validate) the rainfall in the QPE pixel with the rain gauge located within the pixel. The QPE result represents a mean rainfall over the pixel area, whereas the rainfall from the gauge represents a point, although it is normally assumed to represent some area. In most cases the QPE pixel area is millions of square meter in size. We hypothesize that some rain gauge networks in environments similar to this study (i.e., tropical coastal), which provide only one rain gauge per remote sensing pixel, may lead to error when used to calibrate/validate QPE methods, and that consequently these errors may be propagated throughout hydrologic models. The objective of this paper is to describe a ground-truth rain gauge network located in western Puerto Rico which will be available to test our hypothesis. In this paper we discuss observations from the rain gauge network, but do not present any QPE validation results. In addition to being valuable for validating satellite and radar QPE data, the rain gauge network is being used to test and calibrate atmospheric simulation models and to gain a better understanding of the sea breeze effect and its influence on rainfall. In this study, a large number of storms (> 60) were evaluated between August 2006 and August 2008. The area covered by the rain gauge network was limited to a single GOES-12 pixel (4 km × 4 km). Five-minute and total storm rainfall amounts were spatially variable at the sub-pixel scale. The average storm rainfall from 20% of the 120 possible rain gauge-pairs was found to be significantly different at the 5% of significance level, indicating significant rainfall variation at the sub-pixel scale. The average coefficient of determination (r2), describing the goodness of fit of a linear model relating rain gauge pairs, was 0.365, further suggesting a significant degree of variability at the satellite sub-pixel scale. Although there were several different storm types identified (localized, upper westerly trough, tropical easterly wave, tropical westerly trough, cold front and localized with cold front), there did not appear to be any relationship between storm type and the correlation patterns among the gauges.