This report describes the methodology that will be used to produce information about;wind predictability in the European wind atlas. A review of the state-of-the-art in;predictability assessment is reported at different scales, from day ahead (short-term) to;subseasonal (medium-term predictability), seasonal and decadal (long-term;predictability). The report provides an overview of the potential applications of wind;predictability, the sources of predictability, the available prediction systems and the;methods to evaluate them.Short-term predictability was first studied in the frame of the European project;SAFEWIND. A summary of that work is included here in order to show the continuity of;this activity in the NEWA project and how it will be integrated with other scales of;prediction. Predictability mapping was carried out using forecasts from numerical;weather prediction and comparing them with reanalysis data. Downscaling to site level;to produce wind power predictability information, based on information from the;planning phase, was also done in order to illustrate how a wind farm developer could;anticipate costs of lack of predictability during the operational phase.Medium to long term predictability is studied with global climate models. Preliminary;assessment of predictability is also done using reanalysis data as a reference of the real;state of the atmosphere. This allows to map predictability skill over Europe. Preliminary results detected at least two windows of opportunity (high forecast skill) over Europe,;one at sub-seasonal time scale, for a lead time of 12-18 days, and another at seasonal;scale, for a lead time of one month. This skill is found to exist mainly over Central and Northern Europe and to a lesser degree in the Iberian Peninsula during the winter;months of December-February.Having demonstrated that there is statistically significant wind speed skill during winter;over some European areas, the end users could gain potential economic value using the;forecasts instead of the climatology to base their decisions. Over these areas, novel techniques that employ this probabilistic information effectively to enhance the value of;operational business decisions and quantitative risk management in the wind energy;sector can be developed.Further work will try to demonstrate the potential use of predictability with site;observations. This will help potential users answer questions like: what can they expect;on the use of wind forecast at different time horizons? How predictable the wind resource is at the site of interest? This information will be synthetized in the form of;comprehensive maps of predictability skills over Europe so it can be used for spatial;planning of wind energy development.