Stratiform and Convective Radar Reflectivity–Rain Rate Relationships and Their Potential to Improve Radar Rainfall Estimates

• Published in: Journal of applied meteorology and climatology 2019-10, Vol.58 (10), p.2259-2271
• Main Authors: Kirsch, Bastian, Clemens, Marco, Ament, Felix
• Format: Article
• Language: English
• Subjects: Atmospheric precipitations; Convective rainfall; Drop size; Estimates; Estimation errors; Flood predictions; Flooding; Heavy rainfall rates; Particle size distribution; Precipitation; Radar; Radar rainfall; Radar reflectivity; Rain; Raindrop size distribution; Raindrops; Rainfall; Rainfall intensity; Reflectance; Separation; Size distribution
• ISSN: 1558-8424; 1558-8432
• DOI: 10.1175/jamc-d-19-0077.1

The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.