Satellite-based precipitation estimates (SPE) are becoming valuable sources of rainfall data for hydrologic and climatic studies, especially where ground-based gauge measurements are sparse. They are, however, subject to considerable uncertainty due to their indirect measurement techniques. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in terms of both resolution and accuracy. In a recent case study in Chile, a framework for merging satellite and gauge data was developed to produce high-quality precipitation estimates.
The model that was applied and tested was the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Developed in collaboration with G-WADI, PERSIANN-CCS provides real-time precipitation estimates, updated hourly and with only about an hour of time lag, at a grid scale of about 4 km (0.04° × 0.04°). Real-time precipitation information is especially useful for flood forecasting, especially for smaller basins and watersheds where this information is critical input for rapid response in order to save lives and property.
An inverse-root-mean-square-error weighting approach was used to combine the satellite and gauge estimates over a study area in Chile, for the 6 year period of 2009–2014. Daily observations from about 90% of collected gauges over the study area were used for model calibration; the rest of the gauged data were regarded as ground “truth” for validation. Evaluation results indicated high effectiveness of the model in producing high-resolution precipitation data; the original PERSIANN-CCS estimates were consistently improved through this method. Significantly, this framework can provide high-quality rainfall estimates even over ungauged areas. Research on this methodology is continuing in Chile and elsewhere.
Citation: Yang, Z., K. Hsu, S. Sorooshian, X. Xu, D. Braithwaite, Y. Zhang, and K. M. J. Verbist (2017), Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements—A case study in Chile, J. Geophys. Res. Atmos., 122, 5267–5284, doi:10.1002/2016JD026177.