Abstract:
The current goal of our research is to improve the machine learning (ML) based fog forecast for a forecasting period up to four hours for the station in Linden-Leihgestern. The prediction of radiation fog is still subject to large uncertainties. In particular, the precise prediction of fog start and dissipation, i.e. the transitions, is very difficult. The high-frequency fluctuations of the variables in the formation and dissipation phases pose a particular challenge to ML models. These strong fluctuations make it difficult to extract the necessary information about the past, namely increasing or decreasing trend. However, the temporal evolution in the past is determining for the development of radiation fog. Thus, these dynamics must be prepared in such a way that they can be learned during model training.
Therefore, different smoothing levels were tested using a Gaussian moving average filter. Furthermore, additional trend variables for model training were generated that carry information about the temporal evolution of previous data points. Training and evaluation have been carried out with the Expanding Window Approach (Vorndran et al. 2022) that has recently been accepted as a training and validation method for radiation fog prediction. Building on this scheme with the tree-based algorithm XGBoost, the newly preprocessed data led to an improvement in the prediction of radiation fog for the station in Linden-Leihgestern. The results from this research will be presented in the poster session.
The study is funded by the DFG research project “FOG-ML FOrecasting radiation foG by combining station and satellite data using Machine Learning”.
