Improving quantitative precipitation estimates in mountainous regions by modelling low-level seeder-feeder interactions constrained by Global Precipitation Measurement Dual-frequency Precipitation Radar measurements

Abstract

Abstract A physically-based framework to address the underestimation and missed detection errors in Quantitative Precipitation Estimates (QPE) from Global Precipitation Measurement (GPM) Precipitation Radar (PR) in regions of complex terrain is presented. The framework is demonstrated using GPM Ku-PR because of its wider swath. GPM Ku-PR precipitation estimates are evaluated against ground validation (GV) observations from the long-term ground-based rain-gauge network in the Southern Appalachian Mountains. The detection and estimation errors exhibit a diurnal cycle consistent with the diurnal cycle of low-level clouds and fog (LLCF), thus suggesting the importance of low-level orographic microphysical processes. Contamination of near-surface reflectivity profiles due to ground-clutter is the major source of error in the Ku-PR QPE with spatial features that mirror landform. In particular, GPM Ku-PR drop size distribution (DSD) retrieval algorithms systematically overestimate Dm (mass-weighted mean diameter), and underestimate Nw (normalized DSD intercept) and the precipitation-rate when low-level multilayer clouds and fog are present. Second, column simulations of rainfall dynamics constrained by reflectivity measurements show an emergent relationship in Dm-Nw phase-space that explains an increase in the frequency of Dm