Verification scientists and practitioners came together at the 5 th International Verification Methods Workshop in Melbourne, Australia, in December 2011 to discuss methods for evaluating forecasts within a wide variety of applications. Progress has been made in many areas including improved verification reporting, wider use of diagnostic verification, development of new scores and techniques for difficult problems, and evaluation of forecasts for applications using meteorological information. There are many interesting challenges, particularly the improvement of methods to verify high resolution ensemble forecasts, seamless predictions spanning multiple spatial and temporal scales, and multivariate forecasts. Greater efforts are needed to make best use of new observations, forge greater links between data assimilation and verification, and develop better and more intuitive forecast verification products for end-users.

Turbulence remains one of the leading causes of aviation incidents. Climate change is predicted to increase the occurrence of clear‐air turbulence and therefore forecasting turbulence will become more important in the future. Currently, the two World Area Forecast Centres (WAFCs) use deterministic numerical weather prediction models to predict clear‐air turbulence operationally; it has been shown that ensemble forecasts improve the forecast skill of traditional meteorological variables. This study applies multi‐model ensemble forecasting to aviation turbulence for the first time. It is shown in a 12‐month global trial from May 2016 to April 2017 that combining two different ensembles yields a similar forecast skill to a single model ensemble and yields an improvement in forecast value at low cost/loss ratios. This finding is consistent with previous work showing that the use of ensembles in turbulence forecasting is beneficial. Using a multi‐model approach is an effective way to improve the forecast skill and provide pilots and flight planners with more information about the forecast confidence, allowing them to make a more informed decision about what action needs to be taken, such as diverting around the turbulence or requiring passengers and flight attendants to fasten their seatbelts. The multi‐model ensemble approach is intended to be made operational by both WAFCs in the near future and this study lays the foundations to make this possible.

ABSTRACT:The two World Area Forecast Centres (WAFC) are responsible for providing aviation hazard forecasts above 800 hPa (6000 ft) including clear air turbulence (CAT) to aviation customers around the world. A new automated gridded forecast for CAT is now being produced by the two WAFCs along with the traditional forecaster-produced Significant Weather (SIGWX) charts. Until now little objective verification has been available for the WAFC products. However, the increasing availability of high-resolution in situ aircraft observations now makes routine objective verification a possibility. The Global Aircraft Data Set (GADS) formed from the fleet of British Airways Boeing 747-400 aircraft is a particularly useful resource. This paper proposes an objective verification scheme using Relative Operating Characteristic analysis to investigate the skill in both the operational SIGWX and new gridded CAT forecasts from both WAFC London and WAFC Washington. Global verification results using GADS data are presented for 4 months during winter

Turbulence is a major source of weather-related aviation incidents. There are many different indicators used to try and predict where turbulence is likely to occur. The indicators are derived from deterministic models although they are often quoted as probabilities. This paper proposes the use of ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) to produce a probabilistic indicator of wind shear and convectively induced moderate or greater turbulence. An objective verification scheme using high-resolution automated aircraft observations from the Global Aircraft Data Set is used to compare the skill with the routinely available World Area Forecast Centre gridded turbulence forecasts. The forecasts are assessed globally over a 12 month period from November 2010 to October 2011 looking at the skill, reliability and economic value of the forecasts.

Abstract-Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviationaffecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed-ideally in collaboration with the aviation industry-to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future.

ABSTRACT:The World Area Forecast Centres provide aviation users around the world with meteorological hazard forecasts. At present, hazard forecasts are issued for turbulence associated with wind shear or mountain waves. However convection is also a major source of turbulence. This paper proposes a method for combining different predictors of turbulence derived from the area of biostatistics, and this is used to produce a single global turbulence forecast that includes convection as well as wind shear and mountain-wave components. The use of the Richardson number and a climatology of observed turbulence are also investigated as a way of improving turbulence forecasts. The performance of the forecast is tested using an objective verification scheme using automated aircraft observations, and it is shown that the inclusion of convective indicators, as well as Richardson number and climatology, can greatly improve the skill in forecasting turbulence.

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Marine forecasts are essential to operational planning, with decisions able to be guided by a host of different weather products spanning a period of days, weeks and even months ahead. The correct selection and subsequent application of these different types of weather products has the potential to save many thousands of dollars per day in operational downtime, however this is only possible when the science underpinning these marine forecasts is properly understood by the user. In the current economic context, this is especially relevant to the offshore industry – whose use of forecasting technology is traditionally very conservative, and therefore whose planning is often more reactive – allowing large savings (e.g. mobilization / demobilisation costs) if robust decisions are made as early as possible. Two established methods for the interpretation of probabilistic data based on cost-loss and weather regime analysis are described and applied to ocean wave forecasting. It is suggested the selection of methods will be dependant on the timescales of interest, with the cost-loss analysis optimised for supporting decisions at timescales on days to weeks ahead and the weather regime analysis optimized for supporting decisions at timescales of weeks to months ahead. The application of these methods are illustrated from the point of view of a North Sea asset manager planning the mobilization of equipment / personnel under conditions of calm weather, and the protection of equipment / personnel under conditions of severe weather. For such a user, efficient operational planning will be best supported by the use of marine forecasts across all such timescales, from days to months ahead. It is intended that this will enable more informed decision-making, and help reduce operational costs, by promoting increased confidence in longer-range forecasts than are typically used by the offshore oil & gas and marine renewable energy sector.