Abstract
Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.
Highlights
Radar rainfall estimates are essential for hydrometeorological forecasting systems
An explanation for these higher adjustment factors from December through March is that radar quantitative precipitation estimations (QPEs) often severely underestimates the rainfall amounts for stratiform systems, which regularly occur during the Dutch winter
Radar QPE adjustments are needed for operational use in hydrometeorological models
Summary
Radar rainfall estimates are essential for hydrometeorological forecasting systems. In these systems, the data are used to force hydrological models (e.g., Borga, 2002; Thorndahl et al, 2017), to initialize numerical weather prediction models (e.g., Haase et al, 2000; Rogers et al, 2000) or as input data for rainfall nowcasting techniques (e.g., Ebert et al, 2004; Wilson et al, 2010; Foresti et al, 2016; Heuvelink et al, 2020; Imhoff et al, 2020a). R. Imhoff et al.: A climatological benchmark for operational radar rainfall bias reduction spatiotemporal sampling errors (Austin, 1987; Joss and Lee, 1995; Creutin et al, 1997; Gabella et al, 2000; Sharif et al, 2002; Uijlenhoet and Berne, 2008; Ochoa-Rodriguez et al, 2019; Imhoff et al, 2020b). Imhoff et al.: A climatological benchmark for operational radar rainfall bias reduction spatiotemporal sampling errors (Austin, 1987; Joss and Lee, 1995; Creutin et al, 1997; Gabella et al, 2000; Sharif et al, 2002; Uijlenhoet and Berne, 2008; Ochoa-Rodriguez et al, 2019; Imhoff et al, 2020b) These biases can be amplified when used in hydrological models (Borga, 2002; Borga et al, 2006; Brauer et al, 2016). Radar QPE requires corrections before operational use in hydrometeorological (forecasting) models
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
