Adjustment of Radar‐Gauge Rainfall Discrepancy Due to Raindrop Drift and Evaporation Using the Weather Research and Forecasting Model and Dual‐Polarization Radar

Abstract

Radar‐gauge rainfall discrepancies are considered to originate from radar rainfall measurements while ignoring the fact that radar observes rain aloft while a rain gauge measures rainfall on the ground. Observations of raindrops observed aloft by weather radars consider that raindrops fall vertically to the ground without changing in size. This premise obviously does not stand because raindrop location changes due to wind drift and raindrop size changes due to evaporation. However, both effects are usually ignored. This study proposes a fully formulated scheme to numerically simulate both raindrop drift and evaporation in the air and reduces the uncertainties of radar rainfall estimation. The Weather Research and Forecasting model is used to simulate high‐resolution three‐dimensional atmospheric fields. A dual‐polarization radar retrieves the raindrop size distribution for each radar pixel. Three schemes are designed and implemented using the Hameldon Hill radar in Lancashire, England. The first considers only raindrop drift, the second considers only evaporation, and the last considers both aspects. Results show that wind advection can cause a large drift for small raindrops. Considerable loss of rainfall is observed due to raindrop evaporation. Overall, the three schemes improve the radar‐gauge correlation by 3.2%, 2.9%, and 3.8% and reduce their discrepancy by 17.9%, 8.6%, and 21.7%, respectively, over eight selected events. This study contributes to the improvement of quantitative precipitation estimation from radar polarimetry and allows a better understanding of precipitation processes.

Assessment of Satellite and Reanalysis Cold Season Snowfall Estimates Over Arctic Sea Ice

“…e. Reanalysis products: The ERA-Interim (0.25°× 0.25°, 1979-2019) (Dee et al, 2011), and the newly released ERA5 (0.25°× 0.25°since 1950) (Hersbach, 2016) reanalysis products from ECMWF are widely used for Arctic precipitation studies (Lindsay et al, 2014;Wang et al, 2019). Moreover, ERA-Interim generally shows consistently good skill in estimating air temperature, radiative flux and precipitation over the Arctic (Lindsay et al, 2014), and meteorological applications (Dai et al, 2019). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2, 0.5°× 0.625°, 1980-2019) (Gelaro et al, 2017) replaces the original MERRA reanalysis using an upgraded data assimilation system.…”

Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual‐frequency precipitation radar

“…The Z – R relationship under different environments (e.g. storm type, temperature, horizontal wind and aerosol effects) is the foundation of radar remote sensing and fully depends on dynamic DSD (Dai et al, 2019; Jameson & Kostinski, 2001a; Ji et al, 2019).…”

“…Considering that DSDs measured by disdrometers are only point‐based, DSD must be obtained through large‐scale raindrop microphysics measurements or simulations. A ground dual‐polarimetric radar can also be used to derive DSD, which exhibits a circular domain with a radius of up to 200 km, by using radar signatures such as differential reflectivity and specific differential phase shift (Brandes et al, 2004; Bringi et al, 2003; Dai et al, 2019; Gorgucci et al, 2002).…”

A coupled atmospheric–hydrologic modeling system with variable grid sizes for rainfall–runoff simulation in semi-humid and semi-arid watersheds: how does the coupling scale affects the results?

“…The WRF rainfall simulation is sensitive to the selection and the combination of its physical parameterizations (Di et al, 2015). In this study, the well-performing and extensively used parameterizations in northern China were chosen (Miao et al, 2011;Di et al, 2015;Tian et al, 2017a), which include two microphysics parameterizations, i.e., Purdue-Lin (Lin) (Lin et al, 1983) and WRF Single-Moment 6 (WSM6) (Hong et al, 2006); two cumulus parameterizations, i.e., Kain-Fritsch (KF) (Kain, 2004) and Grell-Devenyi (GD) (Grell and Freitas, 2014); and two PBL (planetary boundary layer) parameterizations, i.e., Mellor-Yamada-Janjic (MYJ) (Hong et al, 2006) and Yonsei University (YSU) (Janjić, 1994). Besides, Rapid Radiative Transfer Model (RRTM) and Dudhia (Evans et al, 2012) usually cooperate well as the long-and shortwave radiation parameterizations, and Noah is chosen to be the land surface model (Chen et al, 2014).…”