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Rain radar3/31/2023 ![]() ![]() High resolution radar quantitative precipitation estimation in the San Francisco Bay Area: Rainfall monitoring for the urban environment. Cifelli, R., Chandrasekar, V., Chen, H., & Johnson, L.An improved dual-polarization radar rainfall algorithm (DROPS2.0): Application in NASA IFloodS field campaign. Chen, H., Chandrasekar, V., & Bechini, R.Proceedings of the 22nd International Conference on Machine Learning (ICML'05), 89-96. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G.Cambridge, UK: Cambridge University Press. Polarimetric Doppler weather radar: Principles and applications (p. Journal of Atmospheric and Oceanic Technology, 20( 5), 647– 659. Methodology for aligning and comparing spaceborne radar and ground-based radar observations. Proceedings of the 24th International Conference on Neural Information Processing Systems (pp. Algorithms for hyper-parameter optimization. With more and more gauges and radars being deployed and many of them become operational, this algorithm can be trained at different locations represented by different atmosphere properties to further improve the performance and generality. In areas such as ocean and remote regions where no gauge or radar available, the proposed rainfall algorithm is easy to implement, and it can still produce reasonable estimates. In the regions where substantial gauge and ground radar data are available, this approach can produce better rainfall estimates compared to the standard PR algorithm. This study develops a nonparametric machine learning technique for PR rainfall estimation. However, the ground-based sensors have different characteristics from PR in terms of resolution, viewing angle, and uncertainties in the sensing environments, which are not taken into account in the operational parametric rainfall relations applied to PR measurements. Ground validation is a critical component in the development of TRMM products. During its 17 years (1997–2014) in orbit and beyond, PR has been an important tool to characterize tropical precipitation microphysics and quantify rainfall rate over the globe. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) was the first spaceborne active sensor for observing precipitation over the tropics and subtropics. Validation using independent data in 2009, as well as 2-year comparison against the standard PR products, demonstrates the promising performance and generality of this innovative rainfall algorithm. Data from 1 year of observations in Florida during 2009 are utilized to illustrate the application of this hybrid rainfall system. The first model is trained for ground radar using rain gauge data as target labels, whereas the second model is for spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) using ground radar estimates as training labels. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. This study develops a deep learning mechanism to link between point-wise rain gauge measurements, ground-based, and spaceborne radar reflectivity observations. Remote sensing of precipitation is critical for regional, continental, and global water and climate research.
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