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 entirely new and 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”).
The HIGHLANDER (HIGH-performance computing to support smart LAND sERvices) project is funded under the Connecting Europe Facility (CEF) – Telecommunication Sector Programme of the European Union. One of its main goals is 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 supercomputer facilities in Bologna 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. Pre-existing 6-h (currently running in CINECA) 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. 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 even greater improvements for users (relative to raw model and reanalysis output) than we have seen hitherto.
The final ecPoint sub-seasonal products will cover lead times of 16 to 30 days. They will provide more reliable climatological representations, for points on the land surface, than can 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 will be the first probabilistic reanalysis products to have incorporated bias-corrected uncertainty information at point scale. ecPoint products for both systems will comprise 24-h accumulated rainfall and daily minimum, maximum and mean 2m temperature, for both percentiles (1, 2,..99) and (derived from these) probabilities of exceeding certain thresholds. Global outputs will be created, but a particular focus will be Italy, and surrounding Mediterranean areas, for agricultural planning purposes, to deliver benefits in both economic and resource availability terms.
In this presentation, we will introduce the ECMWF activities in the project and the methodologies applied to create the final probabilistic forecast and reanalysis products.
How to cite: Gascón, E., Vintzileos, A., and Hewson, T.: New ecPoint products for sub-seasonal forecasts and the ERA5 reanalysis - a HIGHLANDER project initiative, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-85, https://doi.org/10.5194/ems2021-85, 2021.