Workshop on Predictability, dynamics and applications research using the TIGGE and S2S ensembles

Email: s.h.lee@pgr.reading.ac.uk

From April 2nd-5th I attended the workshop on Predictability, dynamics and applications research using the TIGGE and S2S ensembles at ECMWF in Reading. TIGGE (The International Grand Global Ensemble, formerly THORPEX International Grand Global Ensemble) and S2S (Sub-seasonal-to-Seasonal) are datasets hosted at primarily at ECMWF as part of initiatives by the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). TIGGE has been running since 2006 and stores operational medium-range forecasts (up to 16 days) from 10 global weather centres, whilst S2S has been operational since 2015 and houses extended-range (up to 60 days) forecasts from 11 different global weather centres (e.g. ECMWF, NCEP, UKMO, Meteo-France, CMA…etc.). The benefit of these centralised datasets is their common format, which enables straightforward data requests and multi-model analysis with minimal data manipulation allowing scientists to focus on doing science!

Attendees of the workshop came from around the world (not just Europe) although there was a particularly sizeable cohort from Reading Meteorology and NCAS.

Figure 1: Workshop group photo featuring the infamous ECMWF ducks!

In my PhD so far, I have been making extensive use of the S2S database – looking at both operational and re-forecast datasets to assess stratospheric predictability and biases – and it was rewarding to attend the workshop and see what a diverse range of applications the datasets have across the world. From the oceans to the stratosphere, tropics to poles, predictability mathematics to farmers and energy markets, it was immediately very clear that TIGGE and S2S are wonderfully useful tools for both the research and applications communities. A particular aim of the workshop was to discuss “user-oriented variables” – derived variables from model output which represent the meteorological conditions to which a user is sensitive (such as wind speed at a specific height for wind power applications).

The workshop mainly consisted of 15-minute conference-style talks in the main lecture theatre and poster sessions, but the final two days also featured parallel working group sessions of about 15 members each. The topics discussed in the working groups can be found here. I was part of working group 4, and we discussed dynamical processes and ensemble diagnostics. We reflected on some of the points raised by speakers over the preceding days – particular attention was given to diagnostics needed to understand dynamical effects of model biases (such as their influence on Rossby wave propagation and weather-regime transition) alongside what other variables researchers needed to make full use of the potentials S2S and TIGGE offer (I don’t think I could say “more levels in the stratosphere!” loudly enough – TIGGE does not go above 50 hPa, which is not useful when studying stratospheric warming events defined at 10 hPa).

Data analysis tools are also becoming increasingly important in atmospheric science. Several useful and perhaps less well-known tools were presented at the workshop – Mio Matsueda’s TIGGE and S2S museum websites provide a wide variety of pre-prepared plots of variables like the NAO and MJO which are excellent for exploratory data analysis without needing many gigabytes of data downloads. Figure 2 shows an example of NAO forecasts from S2S data – the systematic negative NAO bias at longer lead-times was frequently discussed during the workshop, whilst the inability to capture the transition to a positive NAO regime beginning around February 10th is worth further analysis. In addition to these, IRI’s Data Library has powerful abilities to manipulate, analyse, plot, and download data from various sources including S2S with server-side computation.


Figure 2: Courtesy of the S2S Museum, this figure shows S2S model forecasts of the NAO launched on January 31st 2019. The verifying scenario is shown in black, with ensemble means in grey. All models exhibited a negative ensemble-mean bias and did not capture the development of a positive NAO after February 10th.

It’s inspiring and motivating to be part of the sub-seasonal forecast research community and I’m excited to present some of my work in the near future!

TIGGE and S2S can be accessed via ECMWF’s Public Datasets web interface.