Dr Nankervis, Weather Logistics Ltd., 2016 – Research scope for the University of Warwick
Supervisor: Colm Connaughton (Complexity and Mathematics)
Co-supervisors: Chris Nankervis (Weather Logistics Ltd) and Lucasz Bonenberg (Nottingham
Geospatial Institute)
External Stakeholders: Weather Logistics Ltd
Weather, the day-to-day variability experienced at the Earth’s surface, is well represented by numerical weather prediction models. These models are based on an advanced understanding of the dynamical and chemical processes in the atmosphere, oceans, ice cover and the interaction
(coupling) between these components. A deterministic output is the most likely outcome of a weather forecast, providing customers with a definite course of action. International weather centres have become much better at predicting weather up to 1-2 weeks in the future. Weather is differentiated from climate, which can be forecast on monthly, seasonal, and annual timescales [1].
Seasonal climate forecasts are not deterministic, but have a large potential value in risk management; such as in agriculture, finance and energy.
A range of possible outcomes, known as an ensemble, is an essential feature of longer range predictions. This is because small perturbations to the initial conditions, and uncertainties in the parameterisation of physical processes, leads to different outcomes. As a consequence, more general descriptions of the surface conditions are made at longer timescales. This is possible, since the time-scale of predictability can be much longer when exploring less detailed descriptions of large and slowly evolving processes such as sea temperature variability e.g. the El Nino Southern
Oscillation. While seasonal climate forecasts perform well at modelling large-scale global processes, their representation of connections between large and finer-scale processes are poor. This provides an opportunity to explore techniques that link global variables to UK weather observations.
Establishing a better understanding of the connections between large-scale global weather variables, known as teleconnections, is pivotal in filling the gap in our scientific understanding of atmospheric processes. The aim of this project is to explore how these variables correlate with variability in weather conditions on seasonal timescales. Weather Logistics has accessed a large volume of climate data and has demonstrated an accurate hybrid model. The system combines seasonal climate forecasts with representations of teleconnections. The problem that needs to be addressed is how to scale this system to other regions where different sets global explanatory variables account for the observed variability. An automated machine learning process is required.
The current approach is to select large-scale global variables from long-term climate records that best explain the variance in observed weather at UK observation stations. A long-term time-series is then prepared for each explanatory variable by removing long-term climate trends, to produce a difference between the actual and expected conditions. Correlations are then established between these time-series to provide an indication to the magnitude and strength of global variability and UK

Dr Nankervis, Weather Logistics Ltd., 2016 – Research scope for the University of Warwick weather. Global variables linked to UK seasonal weather patterns are then extracted from seasonal climate forecast outputs, calibrated, and combined to form a forecast.
The objective of this project is to develop a consistent method for comparing the predictive power of different models on a like for like basis and quantifying the extent to which including additional data sources improve predictive power. Further development will then focus on understanding which correlations are most informative for characterising the extremes of the observed data. To what extent can the probabilities of extreme events be reliably estimated?
[1] Henry F. Diaz and Richard J. Murnane. (2008). The significance of weather and climate extremes to society: an introduction. In: Henry F. Diaz and Richard J. Murnane (eds.) Climate Extremes and
Society. pp. 1-8. [Online]. Cambridge: Cambridge University Press. Available from: Cambridge Books
Online <http://dx.doi.org/10.1017/CBO9780511535840.003> [Accessed 24 February 2016].