Dan Marsh (University of Leeds) Pathways to improved prediction of the MLTI system

Dan Marsh (University of Leeds) wird auf unserem IAP-Kolloquium einen Vortrag zum Thema "Pathways to improved prediction of the MLTI system" halten.

Zusammenfassung:

Our ability to predict the future state of the mesosphere-lower-thermosphere-ionosphere (MLTI) system lags far behind that of tropospheric weather and climate prediction. This is due to the difficulty in observing the MLTI, the extended domain (surface to geospace), the complexity of incorporating limited observations into a predictive modelling system and deficiencies in our atmospheric models. Improvements in our modelling capabilities can progress along three axes: higher model resolution, increased model complexity and greater model ensemble size. Along which axis you allocate finite computational resources depends on the target atmospheric parameters and the timescale of their prediction. For example, to better predict short term gravity wave variability in the winds, it may be necessary to perform very high-resolution simulations. Accurate prediction of ionospheric variability would require adding detailed ion-neutral chemistry and ion transport. Seasonal and climate prediction would benefit from conducting enough simulations to cover the chaotic divergence inherent in atmospheric systems and the range of atmospheric forcing. In this talk, I will present recent efforts to improve our capability of simulating the MLTI by providing an example of model development along each of the three axes above. The work has been conducted within the same modelling framework - the Community Earth System Model, using the Whole Atmosphere Community Model. The talk will conclude with highlighting opportunities for collaborations and co-development that could create a step-change in the accuracy of MLTI prediction.