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Institute
The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.
The described secondary data provide a comprehensive basis for modeling conditional mean nitrogen dioxide (NO2) concentration levels across Germany. Besides concentration levels, meta data on monitoring sites from the German air quality monitoring network, geocoordinates, altitudes, and data on land use and road lengths for different types of roads are provided. The data are based on a grid of resolution 1 × 1 km, which is also included. The underlying raw data are open access and were retrieved from different sources. The statistical software R was used for (pre-)processing the data and all codes are provided in an online repository. The data were employed for modeling mean annual NO2 concentration levels in the paper "Agglomeration and infrastructure effects in land use regression models for air pollution - Specification, estimation, and interpretations" by Fritsch and Behm (2021).