Quantifying uncertainty from the Extremes Digital Twin of Destination Earth

Within the Destination Earth initiative, the Extremes Digital Twin (DT) has been produced continuously at ECMWF since summer 2023. The Extremes DT is running at 4.4 km horizontal resolution every day starting at 00 UTC out to 5 days. Due to computational constraints it is currently comprised of a single deterministic forecast, which limits an assessment of uncertainty unless an ensemble or other methodologies are introduced. Currently, three approaches are under investigation to quantify uncertainty in the Extremes DT: (i) use of a physical ensemble at 4.4 km resolution with 10 members, (ii) use of machine learning to downscale the uncertainty information from the operational 9 km ensemble system to 4.4 km and (iii) application of statistical/neighbourhood (NB) methods for uncertainty quantification.  

 

In this presentation the implementation and first results of the NB method are described. The aim of the NB method is to characterize uncertainty from a single high-resolution deterministic forecast run by taking into account the inherent uncertainty that comes from location errors or timing errors of predicted localised weather phenomena. Uncertainty related to location errors is represented by constructing a pseudo-ensemble from neighbouring grid points in horizontal space. Uncertainty related to timing errors is constructed by investigating a time window incorporating forecast lead times before and after the actual lead time. Following this construction, the two main free parameters of the method that require investigation are the size of the search radius in horizontal space and the width of the time window. NB methods are widely used in kilometre-scale limited area models that typically operate on regular grids. The challenge here has been the implementation on an irregular spherical grid for global fields. It is now possible to compute NB probabilities on the native TCo2559 grid of the IFS model, so that the reduction of extreme values by interpolation is avoided.

 

The presentation will describe the results that aim to optimise space- and timescale choices of the neighbourhood method for applications.