A Bridge between Short-Range and Seasonal Forecasts: Data-Based First Passage Time Prediction in Temperatures

• Main Author: Wulffen, Anja von
• Other Authors: Kantz, Holger, Kurths, Jürgen, Technische Universität Dresden
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2013
• Subjects: info:eu-repo/classification/ddc/530; ddc:530; Datenanalyse; Lufttemperatur; Wettervorhersage; Kurzfristige Prognose; Langfristige Prognose; Prognoseverfahren; Nordatlantik-Oszillation; Temperatur; Vorhersage; Wiederkehrzeit; Nordatlantische Oszillation; statistische Datenanalyse; Zeitreihenanalyse; temperature; forecasting; first passage time; North Atlantic Oscillation; Statistical data analysis; time series analysis; North Atlantic; North Atlantic oscillation
• Online Access: https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa-104493; https://tud.qucosa.de/id/qucosa%3A26523; https://tud.qucosa.de/api/qucosa%3A26523/attachment/ATT-0/

Current conventional weather forecasts are based on high-dimensional numerical models. They are usually only skillful up to a maximum lead time of around 7 days due to the chaotic nature of the climate dynamics and the related exponential growth of model and data initialisation errors. Even the fully detailed medium-range predictions made for instance at the European Centre for Medium-Range Weather Forecasts do not exceed lead times of 14 days, while even longer-range predictions are limited to time-averaged forecast outputs only. Many sectors would profit significantly from accurate forecasts on seasonal time scales without needing the wealth of details a full dynamical model can deliver. In this thesis, we aim to study the potential of a much cheaper data-based statistical approach to provide predictions of comparable or even better skill up to seasonal lead times, using as an examplary forecast target the time until the next occurrence of frost. To this end, we first analyse the properties of the temperature anomaly time series obtained from measured data by subtracting a sinusoidal seasonal cycle, as well as the distribution properties of the first passage times to frost. The possibility of generating additional temperature anomaly data with the same properties by using very simple autoregressive model processes to potentially reduce the statistical fluctuations in our analysis is investigated and ultimately rejected. In a next step, we study the potential for predictability using only conditional first passage time distributions derived from the temperature anomaly time series and confirm a significant dependence of the distributions on the initial conditions. After this preliminary analysis, we issue data-based out-of-sample forecasts for three different prediction targets: The specific date of first frost, the probability of observing frost before summer for forecasts issued in spring, and the full probability distribution of the first passage times to frost. We then study the possibility of improving the ...