The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.

Synoptic-scale cyclonic features provide an inescapable focal point for operational forecasting, whilst the merits of tracking such features are increasingly being recognized in the climate change field. Close association with adverse and extreme weather is the main motivator. Here a new and highly sophisticated set of techniques to detect, classify and track the full range is developed. A revised conceptual model of cyclone development provided the initial framework, ensuring a solid bond with forecasting practice, whilst also connecting closely to baroclinic life-cycle concepts. Building on this, cyclones are detected using a hybrid of geopotential minimum/vorticity maximum techniques, whilst incorporating important extensions to ensure that vorticity can be used at high resolution (∼50 km) and that features on fronts take priority. To track the features across time, at intervals of 12 h or less, feature attributes in the association process are used. Additionally, an upper-tropospheric steering wind is employed to estimate future and past positions. This facilitates 'half-time tracking', a new approach that has clear-cut advantages over 'full-time tracking' employed elsewhere.In detection tests, comparing with subjectively-drawn charts, the feature hit rate was 84%, and the false alarm ratio 17%, whilst in a simple tracking test the association failure rate was just 2%. These values compare very favourably with previous studies.One key application is discussed. This involves processing ensemble output to provide wide-ranging real-time products tailor-made to forecasters' needs. Products include track-following plume diagrams, for various cyclone attributes, and storm-track strike probability plots for different thresholds of severity.

The Long-Rains wet season of March-May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden-Julian Oscillation (MJO) events in MJO phases 2-4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October-December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed Atmosphere 2018, 9, 472 2 of 30 and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM.

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