Preprints
https://doi.org/10.5194/egusphere-2022-419
https://doi.org/10.5194/egusphere-2022-419
 
09 Jun 2022
09 Jun 2022

Long-term evaluation of the Sub-seasonal to Seasonal (S2S) dataset and derived hydrological forecasts at the catchment scale

Marianne Brum and Dirk Schwanenberg Marianne Brum and Dirk Schwanenberg
  • KISTERS AG, BU Water, 52076 Aachen, Germany

Abstract. Recently, projects such as the S2S (Sub-seasonal to Seasonal) have surfaced with the goal of investigating the potential benefits of operational applications of medium- to long-term weather forecasts from two weeks to three months. Key challenges are to quantify forecast uncertainty and verify these predictions considering the downstream users. This work evaluates the meteorological lead-time performance and 5-years skill evolution of nine models of the S2S project alongside discharge predictions from a coupled hydrological model. Moreover, an analysis of the predictors of Numerical Weather Prediction (NWP) quality and an evaluation of the correlation between meteorological and hydrological quality improvement over time is carried out. Results show that the S2S models have skill at the catchment-scale, particularly for lower threshold levels, and that ensemble size is the main predictor of NWP performance. Discharge simulations forced with S2S predictions remain skilful up to one month. The quality of the S2S has increased over time, and there is a strong correlation between meteorological and hydrological improvements. We conclude that S2S products may provide added value to end-users of water resources applications.

Marianne Brum and Dirk Schwanenberg

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-419', Anonymous Referee #1, 13 Jul 2022
    • AC1: 'Reply on RC1', Marianne Brum, 02 Sep 2022
  • RC2: 'Comment on egusphere-2022-419', Anonymous Referee #2, 14 Jul 2022
    • AC2: 'Reply on RC2', Marianne Brum, 02 Sep 2022

Marianne Brum and Dirk Schwanenberg

Model code and software

Forecast Evaluation Code Marianne Brum https://gitlab.com/mbrum/forecast-evaluation

Marianne Brum and Dirk Schwanenberg

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Short summary
Systems such as navigation and water supply rely on river flow forecasts to manage their assets, which themselves use predictions from weather models (NWPs) as inputs. We evaluate the quality of sub-seasonal to seasonal (S2S) NWPs considering these applications. We conclude that the quality of S2S forecasts has increased over time, that resulting flow simulations are skillful up to one month, and that there is a strong correlation between hydro- and meteorological quality improvements.