Speaker Biography

Sarah L Dance is Professor of Data Assimilation at the University of Reading. She holds an EPSRC Senior Fellowship in Digital Technology for Living with Environmental Change. Her research ranges from mathematical theory to practical applications for data assimilation in hazardous weather and flooding.

Talk Abstract

Weather forecasts play a vital part in our lives, with major impacts on society and the economy. Forecasts are obtained by combining observations of the weather with computational predictions using a data assimilation process. Forecast accuracy relies on accurate estimates of the uncertainty in these weather observations. Recently, new, inexpensive sources of observations obtained from crowdsourcing are being investigated for use in numerical weather prediction. However, the uncertainty comparisons between crowdsourced observations and numerical model predictions is not well understood. For example, the measurements will be affected by their local environment (e.g. a temperature measurement in a sheltered street will give a different reading to one made on the top of a skyscraper). In order to utilise such observations, data assimilation algorithms must take account of the discrepancy in space and time-scales represented by the model and those observed in the actual process.  In this talk we will give an introduction to observations used in numerical weather prediction and how their uncertainty is dealt with in the data assimilation process. Examples will be given from recent research including temperature and windobservations from air traffic control reports, temperature observations from private cars, new observation uncertainty estimation techniques that have been adopted by operational weather centres and new filtering techniques based on the Schmidt-Kalman filter.

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