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Abstract. A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points.Instead of maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies.Some theoretical arguments for this are given.

[1] This paper describes a new method for estimating snow albedo for satellite retrieval of surface UV irradiance and daily UV doses over snow-covered terrains. The method is based on combining satellite and meteorological analysis data. The satellite data exploited in this work are the measured reflectivities of the Total Ozone Mapping Spectrometer/ Nimbus 7 instrument that coincides with the European Centre for Medium-Range Weather Forecasts ERA-15 reanalyzed meteorological data. We compared satellite-retrieved UV daily doses to the ground-based measurements of two Finnish and five Canadian sites. The comparison clearly showed that the new snow albedo approach improves the accuracy of the satellite-retrieved UV doses.

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