Project description
More precise and efficient weather forecasts
Computer weather models are capable of providing reasonably accurate predictions. While seasonal forecasts offer guidance on large-scale weather patterns one or several months in advance, subseasonal forecasts are made two weeks to two months out. These predictions provide valuable insights for decision-making in all social and economic activities. In this context, the ERC-funded ASPIRE project will focus on the intrinsic predictability of modes of tropical convective variability. It will take a cross-disciplinary approach to make better use of the intrinsic predictability of tropical convective modes and to quantify the added value of locally confined kilometre-scale resolution in the tropics. Using models based on machine learning that mimic the effect of the kilometre-scale resolution, ASPIRE aims to lower computational costs in the long-term.
Objective
The new frontier for weather prediction is the so-called subseasonal time scale of two weeks to two months ahead. To take preventive measures at an early stage, reliable forecasts on this time scale are becoming increasingly important for multiple socio-economic sectors. Subseasonal predictability can be gained from recurring patterns in the Earth system. ASPIRE will focus on one of these, namely modes of tropical convective variability. Long-standing systematic errors due to the parametrization of processes in numerical weather prediction models prevent the predictability of these modes from being exploited. Simply running models at a resolution high enough to resolve tropical convection is not feasible due to high computational costs. Taking advantage of three recent developments, ASPIRE will explore new ways to better exploit the intrinsic predictability of tropical convective modes without exhausting the currently available computing resources.
The uniqueness of ASPIRE is its cross-disciplinary approach that builds on my experience in atmospheric dynamics and predictability, numerical modeling, and machine learning (ML). First, ASPIRE will identify the source regions and pathways of tropical forecast errors that prevent the intrinsic predictability from being exploited using a new set of subseasonal ensemble hindcasts. Second, ASPIRE will quantify for the first time the added value of locally confined kilometer-scale resolution in the source regions identified before, and generate probabilistic predictions from deterministic forecasts through ML-based post-processing. Third, to enable simulations at kilometer-scale resolution in operations, ASPIRE will develop ML approaches that emulate the integrated effect of the resolved convection in the tropics at substantially reduced costs. If successful, this approach would be a breakthrough towards improved operational weather forecasts at substantially lower computational costs, for a global socio-economic benefit.
Fields of science
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
ERC - Support for frontier research (ERC)Host institution
76131 Karlsruhe
Germany