Preprints
https://doi.org/10.5194/hess-2021-56
https://doi.org/10.5194/hess-2021-56

  02 Feb 2021

02 Feb 2021

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

Simulation of rainfall fields conditioned on rain gauge observations and radar estimates using random mixing

Jieru Yan1,, Fei Li1,, András Bárdossy2, and Tao Tao1 Jieru Yan et al.
  • 1College of Environmental Science and Engineering, Tongji University, Shanghai, China
  • 2Institute of Modeling Hydraulic and Environmental Systems, Department of Hydrology and Geohydrology, University of Stuttgart, Stuttgart, Germany
  • These authors contributed equally to this work.

Abstract. The accuracy of spatial precipitation estimates with the relatively high temporospatial resolution is of vital importance in various fields of research and practice. Yet the intricate variability and the intermittent nature of precipitation make it very difficult to obtain accurate spatial precipitation estimates. Radar and rain gauge are two complementary sources of precipitation information: the former is inaccurate in general but is a valid indicator for the spatial pattern of the rainfall field; the latter is relatively accurate but lack spatial coverage. Considering the pros and cons of the two sources of precipitation information, a number of radar-gauge merging techniques have been developed to obtain spatial precipitation estimates over the past years. Conditional simulation has great potential to be used in spatial precipitation estimation. Unlike the commonly used interpolation methods, the results from the conditional simulation is a range of possible estimates due to its Monte Carlo framework. Yet an obstacle that hampers the application of conditional simulation in spatial precipitation estimation is how to obtain the marginal distribution function of the rainfall field with accuracy. In this work, we propose a method to obtain the marginal distribution function of the rainfall field based on rain gauge observations and radar estimates. The conditional simulation method, random mixing (RM), is used to simulate rainfall fields. The properties of the results from the proposed method are elaborated through the comparison with the results from other methods: ordinary kriging, kriging with external drift, and conditional merging. Finally, the sensitivity of the proposed method towards the two factors – density of rain gauges and random error in radar estimates – is analyzed.

Jieru Yan 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-2021-56', Anonymous Referee #1, 03 Mar 2021
    • AC1: 'Reply on RC1', Jieru Yan, 05 Mar 2021
  • RC2: 'Comment on hess-2021-56', Remko Uijlenhoet, 20 Mar 2021
    • AC2: 'Reply on RC2 concerning the general remarks', Jieru Yan, 22 Mar 2021
      • RC3: 'Reply on AC2', Remko Uijlenhoet, 23 Mar 2021
        • AC4: 'Reply on RC3', Jieru Yan, 29 Mar 2021
    • AC3: 'Reply on RC2 concerning the specific remarks', Jieru Yan, 25 Mar 2021
  • RC4: 'Comment on hess-2021-56', Scott Sinclair, 20 Apr 2021

Jieru Yan et al.

Jieru Yan et al.

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Short summary
Accurate spatial precipitation estimates are of vital importance in various fields. A method to simulate rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the interpolation methods where the best estimate is given, the Monte Carlo-based simulation method provides a range of possible outcomes. Further, the method is immune to the commonly seen systematic error in radar estimates and is relatively accurate in estimating the mean and the extreme of the rainfall field.