emtool Centre for the Analysis of Time Series London School of Economics Jochen Broecker & Liam Clarke ECMWF Users Meeting June 2006

Background Developed by Jochen Broecker at the Centre for the Analysis of Time Series at the London School of Economics. Developed for the DIME project to facilitate probabilistic forecast evaluation and forecast comparison. Offers an easy to use set of tools for turning ensembles in continuous probability forecasts evaluating those probability forecasts

What is the emtool? emtool is an ensemble manipulation tool - currently in development contains a number of ensemble interpretations - objects for turning ensemble forecasts into continuous probability forecasts - based on kernel dressing methods contains tools for computing probabilities and evaluating probabilistic forecasts - skill scores

Overview further developments kernel dressing - turning simulations into probabilities forecast comparison getting the emtool

Kernel Dressing Problem: we want to turn ensembles of point forecasts into a continuous forecast distribution. Do this by dressing the ensemble with a distribution. Many different dressing methods: fit a distribution to the whole ensemble fit a kernel to each ensemble and normalise fit different kernels to multiple models and combine fit emprical kernels based on historical errors

Kernel Dressing Methods fit a distribution to the ensemble: kernel a function of ensemble properties ensemble mean offset spread

fit distributions to each ensemble member and normalize Kernel Dressing Methods

dress multiple models with different kernels and combine the results

Fitting Kernels The emtool has training algorithms to fit the parameters of the kernels - uses matlabs optimization toolbox. Determine the ensemble interpretations from forecast verification pairs. Offers a limited downscaling capability. Can also set model parameters by hand.

Forecast Evaluation The emtool contains skill scores to enable an evaluation of the probability forecast. DIME project investigated forecast evaluation in a weather forecasting context. Forecast evaluation carried out comparatively - want to show that a forecast (DEMETER, ENSEMBLES) is better than some zero skill forecast.

Investigations Do the forecasts have skill over climatology, or persistence? Do the ensembles have skill over the ensemble mean? Is there skill in combining the different simulations? Is there skill beyond the second moment?

Further Developments Further developments required in the emtool : additional kernel shapes: Gamma distributions, etc additional skill scores: proper linear score, continuous ranked probability score

Summary the emtool is an easy to use toolbox for turning ensembles into continuous probability forecasts the emtool has skill scores for probabilistic forecasts to carry out evaluation

Resources Jochen Broeckers homepage: CATS discussion board: DIME project page: emtool technical manuals and user manuals available from CATS