Davos Atmosphere and Cryosphere Assembly 2013
Air, Ice & Process Interactions
July 8-12, 2013

Data assimilation and ensemble forecasting for weather, climate and air quality

Probabilistic state estimation of past, current and future states is an essential tool in Earth System simulation and prediction. Data assimilation integrates the wealth of data from both satellite and in-situ platforms to analyse the current and past state of the atmosphere and other elements of the Earth System, forming the basis of improved forecasts from the mesoscale to the global scale. Ensemble forecasting techniques provide an estimate of the inherent forecast uncertainty of future states of the atmosphere. A key issue is the characterization of the uncertainty in past, current and future state estimates in both coarse and high resolution models.

The session will provide a forum for presentation and discussion of the latest research in data assimilation and ensemble forecasting. The effectiveness of data assimilation and ensemble forecasting is inextricably linked to our ability to estimate the distribution of truth given limited information. Given imperfect and sparse observations together with an imperfect forecasting system, data assimilation focuses on estimating the distribution of current and past states while ensemble forecasting strives for the distribution of future states. Data assimilation schemes need an estimate of the distribution of truth given a short term forecast – a key objective of ensemble forecasting. The intertwining of the aims of data assimilation and ensemble forecasting ensures that data assimilation experts can help ensemble forecasting experts and vice-versa. We are therefore calling for presentations of outstanding research in data assimilation and ensemble forecasting.

Symposium lead convener

William Lahoz (william.a.lahoz@nilu.no)




Session B1.1: Advanced methods in data assimilation and ensemble forecasting

Session B1.2: Improved use of Earth observations

Session B1.3: Applications to weather and climate prediction

Session B1.4: Applications to air quality monitoring