This paper assesses the skill of probabilistic forecasts of solar irradiance in the northern regions of Chile. Raw ensemble forecast are calibrated using a parametric and a novel non-parametric machine-learning-based method. As the reference approach, the ensemble model output statistics are considered. We verify the superiority of the proposed non-parametric neural-network-based ensemble correction, resulting in more than 50 % improvement in prediction performance compared to the raw forecasts.

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

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.

Extreme precipitation probability estimation is vital for hazard protection design but has high uncertainty. We tested six statistical models using 2000 years of climate data. Our Bayesian hierarchical duration-dependent Generalized Extreme Value model shows the highest accuracy and robustness for sample sizes between 30 and 100 years, making it highly promising for use with limited observational records.

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.

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.

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.

Using information derived from tree-rings, we reconstruct possible combinations of past temperatures and precipitation amounts.  This lets us put current changes in context and shows, for example, that the 1930s were likely the driest decade on record in central Kansas, while the late 20th century was likely the wettest period on record in the North American southwest.

This study advances nonstationary precipitation modeling by using single, flexible distributions to analyze trends across the full daily spectrum. We demonstrate that evolving shape parameters are critical for accurately capturing observed differential changes in low, medium, and extreme quantiles. This method ensures statistical consistency, providing reliable trend assessments over the two-component framework common in climate impact analysis. 

We studied how small, short-lived ocean features in the Mediterranean Sea affect microscopic plant communities that support ocean life. Using a new statistical approach, we found strong evidence that these features can host unique communities not found in surrounding waters. This discovery helps us better understand the role of ocean dynamics in shaping marine ecosystems, even when data are limited and conditions vary widely.

Standard algorithms for estimating the power spectral density of finite discrete data require interpolating missing samples, which usually produces biased estimates. An unbiased estimate can be obtained by taking the Fourier transform of the unbiased estimator of the circular autocorrelation, using only the available data. With missing samples, this estimator can produce negative power spectral densities, but converges to positive values when averaged over a sufficient number of realizations.

Precipitation generators are statistical models for generating long synthetic sequences of (multisite) precipitation for hydrological analyses. One widely-used class of precipitation generators is based on so-called 'generalised linear models'.  In this work, we extend this class to better capture key features of daily precipitation and introduce a new method to ensure realistic inter-site dependence, so neighbouring locations tend to be dry or wet at the same time.

We derived a new statistical test that can compare climate models and observations more broadly than before, while allowing for both natural fluctuations and human influences. Tests on global temperature data show that most climate models differ from observations in important ways.

The analysis of the effect of extreme meteoceanic conditions is usually based on physical simulators, which rely on simulated extreme inputs consistent with the observations. However, surge measurements often fail to meet the theoretical assumptions. To address this, we propose a new simulation method which makes it possible to adjust the desired level of extremes after retrieving standard hypotheses. The consistency of simulations with the observations is then validated by using several tools.

Recurrent flooding in the Marahoué region, especially at Bouaflé and Zuénoula, requires reliable rainfall forecasts. This study uses deep learning with satellite data to predict daily rainfall up to seven days ahead. The results show high accuracy for short- and medium-term forecasts, supporting early warning systems and helping local communities prepare for flood risks.