25 Feb 2021

25 Feb 2021

Review status: this preprint is currently under review for the journal HESS.

A climatological benchmark for operational radar rainfall bias reduction

Ruben Imhoff1,2, Claudia Brauer1, Klaas-Jan van Heeringen2, Hidde Leijnse1,3, Aart Overeem1,3, Albrecht Weerts1,2, and Remko Uijlenhoet1,4 Ruben Imhoff et al.
  • 1Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, The Netherlands
  • 2Operational Water Management & Early Warning, Department of Inland Water Systems, Deltares, Delft, The Netherlands
  • 3R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
  • 4Department of Water Management, Delft University of Technology, Delft, The Netherlands

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPE) limits its use in hydro-meteorological 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 twelve 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 31 automatic and 325 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 far 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. Annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB adjusted rainfall estimates for catchments far from the radars. 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.

Ruben Imhoff et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-105', Anonymous Referee #1, 30 Mar 2021
  • RC2: 'Comment on hess-2021-105', Anonymous Referee #2, 07 Apr 2021
  • RC3: 'Comment on hess-2021-105', Søren Thorndahl, 08 Apr 2021
  • RC4: 'Comment on hess-2021-105', Marco Gabella, 09 Apr 2021

Ruben Imhoff et al.

Data sets

The archived gauge-adjusted (reference) QPE Royal Netherlands Meteorological Institute

Archived 5-min rainfall accumulations from a radar dataset for the Netherlands Aart Overeem & Ruben Imhoff

Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting: Adjustment factors for the Netherlands Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, H. (Hidde) Leijnse, Aart Overeem, Albrecht Weerts, and R. (Remko) Uijlenhoet

Ruben Imhoff et al.


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Short summary
Significant biases in real-time radar rainfall products limit the use for hydro-meteorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for twelve Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.