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Original Articles

A physically based satellite rainfall estimation method using fluid dynamics modelling

Pages 5851-5862 | Received 28 Sep 2006, Accepted 13 Feb 2008, Published online: 20 Sep 2008
 

Abstract

A cloud motion winds (CMW) method is presented for improving quantitative rainfall estimation advection schemes that use both infrared (IR) and passive microwave (PMW) satellite data. Advection schemes are used to provide quantitative rainfall estimates by combining more direct PMW rainfall estimates with more frequent IR cloud top temperature measures using a two‐step technique: (1) PMW estimates are transported along CMW trajectories calculated with an advection scheme at subpixel resolution; and (2) PMW estimates are calibrated using the IR gradient along those trajectories. These schemes outperform traditional methods of satellite rainfall estimation but no clear physical basis for the procedure has yet been described. Here, the physical basis for the image processing techniques used in advection techniques is described. It is shown that geostationary satellite‐derived CMW from IR sensors can be modelled in terms of fluid dynamics using Navier–Stokes equations. This approach allows for modelling the problem as equivalent to the flow of a brightness temperature field, also providing subpixel resolution and unlimited rotation/deformation possibilities. The method is illustrated with rainfall estimates from a numerical weather prediction (NWP) model and with 3‐hourly PMW products as simulation data, obtaining consistent results.

Acknowledgements

Thanks are due to the referees who asked for additional calculations that improved the paper. Special thanks are also due to Chris Kidd at Birmingham University for his help in the early stages of this work. Meteosat data came from the EUMETSAT's Meteosat archive. Microwave data were provided by the Global Hydrology Resource Center (GHRC) at the Global Hydrology and Climate Center, Huntsville, Alabama. Raingauge data were kindly provided by the Spanish INM. This research was funded with research grants PAI06‐0102‐7466 (Regional Government of Castilla‐La Mancha), CGL2006‐03611, CGL2007‐28828‐E/BOS (Ministry of Science and Education) and the Ramon y Cajal programme.

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