Hydrometeorology As an Inversion Problem: Can River Discharge Observations Improve the Atmosphere by Ensemble Data Assimilation?

We examine the potential of assimilating river discharge observations into the atmosphere by strongly coupled river-atmosphere ensemble data assimilation. The Japan Meteorological Agency’s Non-Hydrostatic atmospheric Model (JMA-NHM) is first coupled with a simple rainfall-runoff model. Next, the Local Ensemble Transform Kalman Filter (LETKF) is used for this coupled model to assimilate the observations of the rainfall-runoff model variables into the JMA-NHM model variables. This system makes it possible to do hydrometeorology backward, i.e., to inversely estimate atmospheric conditions from the information of river flows or a flood on land surfaces. We perform a proof-of-concept Observing System Simulation Experiment (OSSE) which reveals that the assimilation of river discharge observations into the atmospheric model variables can improve the skill of the short-term severe rainfall forecast.