Predictability of frontal waves and cyclones

The statistical properties and skill in predictions of objectively identified and tracked cyclonic features (frontal waves and cyclones) are examined in MOGREPS-15, the global 15-day version of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The number density of cyclonic features is found to decline with increasing lead-time, with analysis fields containing weak features which are not sustained past the first day of the forecast. This loss of cyclonic features is associated with a decline in area averaged enstrophy with increasing lead time. Both feature number density and area averaged enstrophy saturate by around 7 days into the forecast. It is found that the feature number density and area averaged enstrophy of forecasts produced using model versions that include stochastic energy backscatter saturate at higher values than forecasts produced without stochastic physics. The ability of MOGREPS-15 to predict the locations of cyclonic features of different strengths is evaluated at different spatial scales by examining the Brier Skill (relative to the analysis climatology) of strike probability forecasts: the probability that a cyclonic feature center is located within a specified radius. The radius at which skill is maximised increases with lead time from 650km at 12h to 950km at 7 days. The skill is greatest for the most intense features. Forecast skill remains above zero at these scales out to 14 days for the most intense cyclonic features, but only out to 8 days when all features are included irrespective of intensity

The TIGGE project and its achievements

TIGGE was a major component of the THORPEX (The Observing System Research and Predictability Experiment) 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 Multi-model 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 multi-model ensemble research. Their extra-tropical transition also has a major impact on skill of mid-latitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extra-tropical 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

Extreme Rainfall and Flooding over Central Kenya Including Nairobi City during the Long-Rains Season 2018: Causes, Predictability, and Potential for Early Warning and Actions

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 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

A multicentre observational study evaluating image-guided radiotherapy for more accurate partial-breast intensity-modulated radiotherapy: comparison with standard imaging technique

Background: Whole-breast radiotherapy (WBRT) is the standard treatment for breast cancer following breast-conserving surgery. Evidence shows that tumour recurrences occur near the original cancer: the tumour bed. New treatment developments include increasing dose to the tumour bed during WBRT (synchronous integrated boost) and irradiating only the region around the tumour bed, for patients at high and low risk of tumour recurrence, respectively. Currently, standard imaging uses bony anatomy to ensure accurate delivery of WBRT. It is debatable whether or not more targeted treatments such as synchronous integrated boost and partial-breast radiotherapy require image-guided radiotherapy (IGRT) focusing on implanted tumour bed clips (clip-based IGRT). Objectives: Primary – to compare accuracy of patient set-up using standard imaging compared with clip-based IGRT. Secondary – comparison of imaging techniques using (1) tumour bed radiotherapy safety margins, (2) volume of breast tissue irradiated around tumour bed, (3) estimated breast toxicity following development of a normal tissue control probability model and (4) time taken. Design: Multicentre observational study embedded within a national randomised trial: IMPORT-HIGH (Intensity Modulated and Partial Organ Radiotherapy – HIGHer-risk patient group) testing synchronous integrated boost and using clip-based IGRT. Setting: Five radiotherapy departments, participating in IMPORT-HIGH. Participants: Two-hundred and eighteen patients receiving breast radiotherapy within IMPORT-HIGH. Interventions: There was no direct intervention in patients’ treatment. Experimental and control intervention were clip-based IGRT and standard imaging, respectively. IMPORT-HIGH patients received clip-based IGRT as routine; standard imaging data were obtained from clip-based IGRT images. Main outcome measures: Difference in (1) set-up errors, (2) safety margins, (3) volume of breast tissue irradiated, (4) breast toxicity and (5) time, between clip-based IGRT and standard imaging. Results: The primary outcome of overall mean difference in clip-based IGRT and standard imaging using daily set-up errors was 2–2.6 mm (p < 0.001). Heterogeneity testing between centres found a statistically significant difference in set-up errors at one centre. For four centres (179 patients), clip-based IGRT gave a mean decrease in the systematic set-up error of between 1 mm and 2 mm compared with standard imaging. Secondary outcomes were as follows: clip-based IGRT and standard imaging safety margins were less than 5 mm and 8 mm, respectively. Using clip-based IGRT, the median volume of tissue receiving 95% of prescribed boost dose decreased by 29 cm3 (range 11–193 cm3) compared with standard imaging. Difference in median time required to perform clip-based IGRT compared with standard imaging was X-ray imaging technique dependent (range 8–76 seconds). It was not possible to estimate differences in breast toxicity, the normal tissue control probability model indicated that for breast fibrosis maximum radiotherapy dose is more important than volume of tissue irradiated. Conclusions and implications for clinical practice: Margins of less than 8 mm cannot be used safely without clip-based IGRT for patients receiving concomitant tumour bed boost, as there is a risk of geographical miss of the tumour bed being treated. In principle, smaller but accurately placed margins may influence local control and toxicity rates, but this needs to be evaluated from mature clinical trial data in the future. Funding: This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and NIHR partnership