Using fractal downscaling of satellite precipitation products for hydrometeorological applications
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The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L»l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (~25-km grid spacing) to the same resolution as the NCEP stage IV products (~4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied. © 2010 American Meteorological Society.
Published Version (Please cite this version)10.1175/2009JTECHA1219.1
Publication InfoTao, K; & Barros, AP (2010). Using fractal downscaling of satellite precipitation products for hydrometeorological applications. Journal of Atmospheric and Oceanic Technology, 27(3). pp. 409-427. 10.1175/2009JTECHA1219.1. Retrieved from https://hdl.handle.net/10161/4271.
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Edmund T. Pratt, Jr. School Distinguished Professor of Civil and Environmental Engineering
Professor Ana Barros was born in Africa, grew up in Angola and Portugal, and has lived almost all of her adult life in the United States. She attended the Faculty of Engineering of the University of O’Porto where she obtained a summa cum laude Diploma in Civil Engineering with majors in Structures and Hydraulics in 1985, and a M.Sc. degree in Ocean Engineering in 1988 with a thesis focusing on numerical modeling of sediment transport in estuaries and coastal regions. In 1990, Dr. Barros