Laboratory for
Climatology and Remote Sensing

Abstract:

Fog is a weather phenomenon that affects all of us, both in the business and private sectors. On the one hand it is vital for entire ecosystems worldwide, on the other hand it can lead to immense economic damage (airport etc.) and even life-threatening situations, for example in road traffic. The positive as well as the negative implications demonstrate the urgency of a good understanding of fog events and their prediction. Fog forecasting has been a subject of research for many years. However, due to the complex interactions of a wide variety of variables relevant for fog formation, accurate prediction still remains very difficult. One reason for this is that the simulation of parameter interactions which are difficult to capture by modelling is affected by errors due to the complexity of the system. Another reason is the neglected but most likely important combination of temporal and spatial variables, which is currently not taken into account by the models. This is mainly due to the lack of suitable approaches for the combination of temporally high-resolution station measurements of fog-relevant variables and area-wide high-resolution satellite data.

Abstract:

Fog forecasting still shows large inaccuracies in accurately predicting fog formation, dissipation and duration. Since a few years, Machine learning (ML) algorithms are increasingly used in addition to numerical fog forecasts because of their computational speed and ability to learn non-linear interactions between the variables. Due to their black-box nature, precise and accurate training and evaluation is vital to prevent insufficient training or meaningless scores. Three main points important for fog prediction are explained in the following. 1. Fog forecasting datasets consist of autocorrelated variables. In most cases, there is an information leakage between the training and test data sets which are used to evaluate the model performance. This information leakage can have an impact on the performance scores because the stronger the information flow, the easier it is for the model to memorize. 2. Fog forecasting datasets have a temporal order. To be able to make statements about the performance of an operational model this temporal order should already be simulated during model training and evaluation. This is because for an operational model, the training data points are always older than the data points to be predicted. Commonly used training methods neglect this fact. 3. Time series used for fog forecasting usually have a large imbalance between the frequency of the fog class and non-fog class. This imbalance can have an unfavorable interaction with the confusion matrix based meteorological scores that are widely used for evaluation. All of the aforementioned points, if not considered, can lead to an insufficient forecast without even being noticed. Therefore, the negative influence on the model score of two commonly used training methods that neglect the points named above will be shown using an XGBoost model and a logistic regression model. In comparison, a training and evaluation method was evaluated that maintains the temporal order and thus simulates the performance of an operational model. It will also be shown that common meteorological scores, since they are computed based on a confusion matrix, share a weakness when the data set is unbalanced: Persistence behavior remains undetected. The study is funded by the DFG research project “FOG-ML FOrecasting radiation foG by combining station and satellite data using Machine Learning”.

Abstract:

Large inaccuracies still exist in accurately predicting fog formation, dissipation, and duration. To improve these deficiencies, machine learning (ML) algorithms are increasingly used in nowcasting in addition to numerical fog forecasts because of their computational speed and their ability to learn the nonlinear interactions between the variables. Although a powerful tool, ML models require precise training and thoroughly evaluation to prevent misinterpretation of the scores. In addition, a fog dataset’s temporal order and the autocorrelation of the variables must be considered. Therefore, classification-based ML related pitfalls in fog forecasting will be demonstrated in this study by using an XGBoost fog forecasting model. By also using two baseline models that simulate guessing and persistence behavior, we have established two independent evaluation thresholds allowing for a more assessable grading of the ML model’s performance. It will be shown that, despite high validation scores, the model could still fail in operational application. If persistence behavior is simulated, commonly used scores are insufficient to measure the performance. That will be demonstrated through a separate analysis of fog formation and dissipation, because these are crucial for a good fog forecast. We also show that commonly used blockwise and leave-many-out cross-validation methods might inflate the validation scores and are therefore less suitable than a temporally ordered expanding window split. The presented approach provides an evaluation score that closely mimics not only the performance on the training and test dataset but also the operational model’s fog forecasting abilities.

Abstract:

The scope of the EU Concerted Action on Science and Technology (COST) 722 “Short range forecasting methods of fog, visibility and low clouds” is presented. Results of the inventory phase are discussed and some examples of the current work within the single working groups are given.

Abstract:

The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ‘‘fog’’ in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.