The ultimate test for global weather predictions is their value to society. Several developments in 2018 aimed to maximise the value of ECMWF’s forecasts to its Member and Co-operating States and other users.
First among them, ECMWF’s high-resolution forecasts followed in the footsteps of its ensemble forecasts by beginning to reflect dynamic interactions between the ocean, sea ice and the atmosphere. The extension of coupling to high-resolution forecasts was introduced as part of an upgrade of ECMWF’s Integrated Forecasting System (IFS Cycle 45r1). This and other changes in Cycle 45r1 meant that many aspects of ECMWF’s forecasts improved in 2018. Severe weather events, from extreme snow in the Alps to a heatwave affecting much of northern Europe, were predicted well but also highlighted scope for further improvements. New products for lightning and the vertical structure of the atmosphere at a point were made available to support the work of forecasters. Finally, more data were made available to all WMO Members as well as to all users holding a real-time licence.
All forecasts coupled
Including more Earth system components in numerical weather prediction models has the potential to improve weather forecasts because of the interactions of those components with the atmosphere and with each other.
The upgrade of ECMWF’s Integrated Forecasting System implemented in June 2018 (IFS Cycle 45r1) brought ocean and sea ice coupling to high-resolution forecasts (HRES) after it had previously been included in lower-resolution ensemble forecasts (ENS).
The change meant that the evolution of ocean and sea ice variables as well as related atmospheric variables in HRES became more realistic. This is for example apparent in the evolution of modelled and observed North Sea sea-surface temperatures during a cold spell in February and March 2018. A coupled high-resolution 8-day forecast was able to predict the observed drop in sea-surface temperatures much better than an uncoupled forecast.
The ocean coupling in HRES also improved predictions of tropical cyclones. For example, the mean absolute intensity error was reduced by about 20% from day 5 onwards. Verification results also showed that using a dynamic sea ice model improves medium-range sea ice predictions, which in turn has repercussions on local 2-metre temperature forecasts.
Forecast performance 2018
The skill of ECMWF’s ensemble forecasts (ENS) and high-resolution forecasts (HRES) increased in 2018. Part of the increase in skill can be attributed to the upgrade of the Integrated Forecasting System (IFS Cycle 45r1) on 5 June 2018.
In general terms, the upgrade brought improvements in the extratropics for some aspects of the forecast and improvements in the tropics for most parameters. There was an overall improvement in 2-metre temperature in the ENS and HRES, particularly for Europe. Precipitation forecast skill in the HRES increased when compared with a baseline of forecasts based on the ERA5 reanalysis.
Forecasts of tropical cyclones further improved in terms of position, intensity, and speed errors for both the ENS and HRES. The change from La Niña to El Niño conditions in the first half of 2018 was well captured several months in advance. For ocean waves, ECMWF maintained its lead compared to other global wave forecasting systems for forecasts of significant wave height, and its position among the leading systems for peak period.
The late spring and summer of 2018 were among the warmest on record for northern Europe. ECMWF extended-range forecasts predicted warm anomalies weeks in advance. The northern extent and intraseasonal variability of the heatwave were reflected in forecasts up to two weeks ahead. The strongest anomalies occurred in the Baltic Sea region, while the countries around the Mediterranean experienced close to normal temperatures on average.
Looking at composites of weekly anomalies from ECMWF’s extended-range forecasts covering the period 7 May to 12 August, the predicted anomalies in week-two forecasts resemble the spatial pattern of the anomalies in the analysis well. Warm anomalies are also present in week-three and week-four forecasts, but they are weaker and the forecasts did not reflect their northern extent.
Further verification shows that week-two forecasts captured intra-seasonal variations reasonably well, while week-three forecasts showed less variation in the predicted anomalies throughout the summer. For example, they failed to give an indication of the warm peak at the end of May or of the break in the warm weather at the end of June, although they gave some indication of the warm period in the second half of July.
Extreme snow in the Alps
January 2018 saw several episodes of extreme snowfall in the Alps. Up to 3 metres of fresh snow reportedly fell in the south- western part from 7 to 9 January. The ski report for Tignes and Val d’Isère said between 110 and 160 cm of fresh snow had fallen in two days. Road links to several villages were cut by avalanches and tourists were stranded in resorts.
ECMWF’s Extreme Forecast Index (EFI) for total precipitation showed a signal in the south-western Alps more than a week in advance. Ensemble forecasts (ENS) starting on 1 January showed a risk of up to 100 mm/48 hours in Val d’Isère for 7–8 January. Between 2 and 3 January, the ensemble forecast became more extreme.
For all forecasts issued from 3 January onwards, the high-resolution forecast (HRES, 9 km grid spacing compared to 18 km for ENS) gave higher two-day precipitation for 7–8 January in Val d’Isère than the ensemble median. Such differences can be expected in steep terrain. The limited-area COSMO-LEPS ensemble with 7 km grid spacing from ARPA-ER SIMC in Italy predicted even higher precipitation accumulations.
Lightning can trigger wildfires; disrupt air traffic; cause power supply outages or power surges; damage buildings; and even lead to fatalities. In June 2018, a new forecast product for lightning density developed at ECMWF was made available as part of the upgrade to IFS Cycle 45r1. Experiments had shown that ensemble forecasts for lightning can have useful skill to at least day 3. The discrete and random nature of lightning makes it particularly suitable for the probabilistic predictions provided by ensemble forecasts.
Also in June 2018, a new product to show the vertical structure of the atmosphere at a point in ensemble forecasts (ENS) was incorporated into ECMWF’s web-based chart-viewing applications.
Users can now examine vertical profiles of the atmosphere anywhere across the globe, at 6-hour intervals, up to a lead time of 120 hours. Such profiles can support the prediction of cloud layers, layers of instability, precipitation type, wind gust penetration to the surface, and more.
Wider forecast availability
In July 2018, ECMWF substantially increased the amount of weather prediction data it makes available free of charge to Members of the World Meteorological Organization (WMO). For example, all forecasts of weather variables are now provided at 6- or 12-hour time steps instead of 24-hour time steps. The provision of the data is part of the Centre’s obligations as a World Meteorological Centre (WMC). ECMWF became a WMC in June 2017.
In October 2018, ECMWF made hourly data and 06/18 UTC forecast runs from its Boundary Conditions Optional Programme available to all users holding a real-time licence, upon request. The change applied to both the single high-resolution forecast (HRES) and the ensemble forecast (ENS).
Until then, many users had interpolated the three- and six-hourly data they received from ECMWF to provide hourly weather forecasts. The introduction of hourly time steps and more frequent forecasts reduced the requirement for interpolation and made the hourly forecasts more accurate.
Including more Earth system components in numerical weather prediction models has the potential to improve weather forecasts.
There was an overall improvement in 2-metre temperature forecasts, particularly for Europe.
ECMWF’s Extreme Forecast Index for total precipitation showed a signal in the south-western Alps more than a week in advance.
In 2018, ECMWF substantially increased the amount of weather prediction data it makes available to WMO Members.