New probabilistic point forecast products ("ecPoint") for sub-seasonal forecasts and the ERA5 reanalysis - the HIGHLANDER project

Ideally, weather forecasts should be provided for points and not for the large regions represented by global model grid boxes. This requirement can be addressed by post-processing global forecast model output, as in “ecPoint”, an innovative statistical technique developed by ECMWF that uses decision trees and non-local calibration. Products from ecPoint explicitly incorporate the expected sub-grid variability and gridscale bias correction (which both vary according to a diagnosed “grid-box weather type”). Pre-existing 6-h (currently running in CINECA supercomputer facilities in Bologna) and 12-h ecPoint-Rainfall forecasts products are currently being provided by ECMWF in real-time, using shorter range (day 1-15) twice daily predictions at 18km resolution. These probabilistic forecasts have exhibited clear improvements, in both reliability and resolution, relative to the raw model output.

The HIGHLANDER (HIGH-performance computing to support smart LAND sERvices) project started in 2018, funded under the Connecting Europe Facility (CEF) – Telecommunication Sector Programme of the European Union. One of its main goals is the data processing for more intelligent and sustainable management of natural resources and the territory. And one component of this, managed by ECMWF, is exploiting the CINECA HPC capacity to extract maximum benefit from the ecPoint technique. The specific aims here are to improve probabilistic 24-h rainfall and 2m temperature representation in sub-seasonal forecasts and in the ERA5 reanalysis. ecPoint benefits tend to be more significant when working at a lower resolution, so downscaling from 36km in the sub-seasonal forecast and 31 km in ERA5 can in principle deliver greater improvements for users (relative to raw model and reanalysis output) than we have seen in the Medium-Range forecast products.

The final ecPoint sub-seasonal products cover lead times of 16 to 30 days. They provide more reliable climatological representations, for points on the land surface than raw model output (e.g. numbers of dry or wet days, days of freezing temperatures, which are both relevant for agricultural applications). Meanwhile, ERA5 ecPoint products are the first global probabilistic reanalysis products to have incorporated bias-corrected uncertainty information at point scale. ecPoint products for both systems comprise 24-h accumulated rainfall and daily minimum, maximum and mean 2m temperature, for percentiles (1, 2,..99) and (derived from these) probabilities of exceeding certain thresholds. Global outputs are created, but a particular focus is Italy, and surrounding Mediterranean areas, to assist with agricultural planning, to deliver benefits in both economic and resource availability terms.

In this presentation, we will describe ECMWF activities in HIGHLANDER and the methods applied to create the final probabilistic forecast and reanalysis products.