Radar Rainfall System
Available for Non-Exclusive Licensing
At A Glance
Researchers at Colorado State University have developed an improved dual-polarization rainfall methodology which is driven by a region-based hydrometeor classification mechanism. The success of this system is that it incorporates the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and produce robust rainfall estimates for operational applications. Test results show that the improved rainfall method provides better performance than prior single- and dual-polarization algorithms.
Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation, because more information about the raindrop size distribution and hydrometeor type can be gleaned. Hence, a number of dual-polarization radar rainfall algorithms have been developed. Although there is still no standard algorithm for radar rainfall estimation, the hydrometeor-classification-based rainfall methodologies are widely used in the weather radar community. Such rainfall systems typically consist of three modules, namely, data quality control, classification of different hydrometeor types, and precipitation quantification with appropriate rainfall relations. However, the rainfall products derived based on traditional bin-by-bin-based fuzzy-logic classification methods are not sufficient for operational applications, especially if the radar data are noisy. That is because the hydrometeor classification result will be noisy and unstable when the input radar data quality is low since the classification quality and correlation with adjacent range gates are not taken into account. In addition, traditional hydrometeor-classification-based rainfall methods severely suffer from brightband contamination due to the challenges of mixed-phase precipitation classification in melting layer.
- Very robust system
- Exploits the spatial information content of dual-polarization radar observations
- Implemented, demonstrated, and evaluated wit NPOL radar data
- Excellent performance compared to traditional fuzzy-logic-based dual-polarization approach and single-polarization-based Z-R relation
- Can be applied in a broader domain with operational dual-polarization radars
- Robust rainfall estimates for operational applications
Chen, H., V. Chandrasekar, and R. Bechini, 2017: An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign. J. Hydrometeor., 18, 917–937, https://doi.org/10.1175/JHM-D-16-0124.1.
Last updated: October 2020