We employ hierarchical statistical models to investigate spatiotemporal differences between sea surface temperature, marine air temperature, and their anomalies in the tropical Pacific.

Southeasterm Australia, including the Murray–Darling Basin, is a highly productive agricultural region largely dependent on adequate rainfall, providing irrigation water needed for crops.

The Australian Bureau of Meteorology uses linear methods and provides seasonal forecasts expressed as the probability of exceeding median rainfall. I use expanded methods, including more ocean data and deep learning neural networks that provide nonlinear estimates of the rainfall as measured by rain gauges.

Meteorological data recorded at high frequency by automatic sensors are often marred by multiple forms of error. Existing validation techniques, in isolation, are sub-optimal for such error-prone data. We propose a new data-standardization procedure for the validation of strongly correlated series which commonly arise in meteorology. We show the procedure to be more comprehensive in the simultaneous detection of solitary spikes, shifts in series means, and irregular diurnal patterns.

Weather generators efficiently create realistic weather data based on historical records. This study introduces a daily temperature generator for large regions, separating deterministic factors (trends, seasonality) from random variations modeled using space-time interactions. Validated on French weather station data, it replicates observed patterns, including heatwaves. It offers a practical solution for generating realistic weather data, for applications such as climate impact assessments.

We develop a spatial statistical calibration of wind gust observations for the region of Germany with an interpolation to unobserved locations. Furthermore, the model is spatially adaptive and includes the station altitude both as explanatory variable and as offset to increase the distance between stations. This offset allows us to include mountain stations into the training data. Compared to a spatially constant model, the adaptive model improves the representation of extreme wind gusts.

This study investigates soil moisture–temperature coupling during the extreme warm conditions in May–August 2018 in southern and central Sweden using the merged GLEAM-E-OBS dataset and four simulations from the Weather Research and Forecasting model coupled with the Community Terrestrial Systems Model (WRF-CTSM). Based on changes in surface soil moisture, evaporative fraction, and daily maximum 2 m temperature, on average across the region and five datasets, the coupling lasted for 22 d.