ASCMO - recent articles

Asymptotically-unbiased nonparametric estimation of the power spectral density from uniformly-spaced data with missing samples

2026-02-20T08:04:15+01:00

Asymptotically-unbiased nonparametric estimation of the power spectral density from uniformly-spaced data with missing samples
Cédric Chavanne
Adv. Stat. Clim. Meteorol. Oceanogr., 12, 59–72, https://doi.org/10.5194/ascmo-12-59-2026, 2026
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.

Joint probabilistic estimates of temperature and precipitation from tree ring-based reconstructions of the last millennium

2026-02-16T08:04:15+01:00

Joint probabilistic estimates of temperature and precipitation from tree ring-based reconstructions of the last millennium
Kate Marvel, Benjamin Cook, Ensheng Weng, Ram Singh, and Edward Cook
Adv. Stat. Clim. Meteorol. Oceanogr., 12, 43–57, https://doi.org/10.5194/ascmo-12-43-2026, 2026
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.

A statistical approach to unveil phytoplankton adaptation to ocean fronts

2026-01-30T08:04:15+01:00

A statistical approach to unveil phytoplankton adaptation to ocean fronts
Théo Garcia, Laurina Oms, Xavier Milhaud, Andrea M. Doglioli, Monique Messié, Pierre Vandekerkhove, Claire Lacour, Gérald Grégori, and Denys Pommeret
Adv. Stat. Clim. Meteorol. Oceanogr., 12, 21–41, https://doi.org/10.5194/ascmo-12-21-2026, 2026
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.

Bayesian hierarchical modelling of intensity-duration-frequency curves using a climate model large ensemble

2026-01-05T08:04:15+01:00

Bayesian hierarchical modelling of intensity-duration-frequency curves using a climate model large ensemble
Alexander Lee Rischmuller, Benjamin Poschlod, and Jana Sillmann
Adv. Stat. Clim. Meteorol. Oceanogr., 12, 1–19, https://doi.org/10.5194/ascmo-12-1-2026, 2026
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.

Soil moisture–temperature coupling during extreme warm conditions in 2018 in Sweden: a case study with WRF-CTSM

2025-12-02T08:04:15+01:00

Soil moisture–temperature coupling during extreme warm conditions in 2018 in Sweden: a case study with WRF-CTSM
Iris Mužić, Øivind Hodnebrog, Yeliz A. Yilmaz, Terje K. Berntsen, Jana Sillmann, David M. Lawrence, and Paul A. Dirmeyer
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 273–292, https://doi.org/10.5194/ascmo-11-273-2025, 2025
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.

A bi-level spatiotemporal clustering approach and its application to drought extraction

2025-11-28T08:04:15+01:00

A bi-level spatiotemporal clustering approach and its application to drought extraction
T. Elana Christian, Amit N. Subrahmanya, Brandi Gamelin, Vishwas Rao, Noelle I. Samia, and Julie Bessac
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 257–272, https://doi.org/10.5194/ascmo-11-257-2025, 2025
We present a novel spatiotemporal clustering algorithm to extract spatiotemporal events based on their intensity. Our algorithm proceeds in two steps: (1) extracting intensity structures that are spatiotemporally consistent and, (2) separating individual events. We apply the algorithm to a novel drought index over the continental United States from 1980–2021 and show that it captures historical drought events over the continental United States and their spatiotemporal extents.

Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model

2025-11-27T08:04:15+01:00

Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
Philipp Ertz and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 229–256, https://doi.org/10.5194/ascmo-11-229-2025, 2025
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.

A spatio-temporal weather generator for the temperature over France

2025-10-09T08:04:15+02:00

A spatio-temporal weather generator for the temperature over France
Caroline Cognot, Liliane Bel, David Métivier, and Sylvie Parey
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 203–228, https://doi.org/10.5194/ascmo-11-203-2025, 2025
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.

Interpretable seasonal multisite hidden Markov model for stochastic rain generation in France

2025-09-08T08:04:15+02:00

Interpretable seasonal multisite hidden Markov model for stochastic rain generation in France
Emmanuel Gobet, David Métivier, and Sylvie Parey
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 159–201, https://doi.org/10.5194/ascmo-11-159-2025, 2025
Stochastic weather generators (SWGs) are statistical models used to study climate variability. We design an interpretable multisite SWG for precipitation, capable of learning large-scale weather regimes solely from French observational data. The model reproduces extreme events like droughts and heavy rain and is applied to climate models under historical and Representative Concentration Pathway (RCP) scenarios. This type of model aims to assess large-scale weather risks, such as those impacting energy systems and agriculture.

A new data-standardization procedure for comprehensive outlier detection in correlated meteorological sensor data

2025-09-05T08:04:15+02:00

A new data-standardization procedure for comprehensive outlier detection in correlated meteorological sensor data
Natalie D. Benschop, Temesgen Zewotir, Rajen N. Naidoo, and Delia North
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 133–158, https://doi.org/10.5194/ascmo-11-133-2025, 2025
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.

Forecasting springtime rainfall in southeastern Australia using empirical orthogonal functions and neural networks

2025-08-26T08:04:15+02:00

Forecasting springtime rainfall in southeastern Australia using empirical orthogonal functions and neural networks
Stjepan Marčelja
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 123–132, https://doi.org/10.5194/ascmo-11-123-2025, 2025
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.

Comparison of sea surface temperatures and marine air temperatures in the tropical Pacific

2025-07-18T08:04:15+02:00

Comparison of sea surface temperatures and marine air temperatures in the tropical Pacific
Peter F. Craigmile and Peter Guttorp
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 107–121, https://doi.org/10.5194/ascmo-11-107-2025, 2025
We employ hierarchical statistical models to investigate spatiotemporal differences between sea surface temperature, marine air temperature, and their anomalies in the tropical Pacific.

Machine-learning-based probabilistic forecasting of solar irradiance in Chile

2025-06-11T08:04:15+02:00

Machine-learning-based probabilistic forecasting of solar irradiance in Chile
Sándor Baran, Julio C. Marín, Omar Cuevas, Mailiu Díaz, Marianna Szabó, Orietta Nicolis, and Mária Lakatos
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 89–105, https://doi.org/10.5194/ascmo-11-89-2025, 2025
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.

On inference of boxplot symbolic data: applications in climatology

2025-03-17T08:04:15+01:00

On inference of boxplot symbolic data: applications in climatology
Abdolnasser Sadeghkhani and Ali Sadeghkhani
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 73–87, https://doi.org/10.5194/ascmo-11-73-2025, 2025
This paper presents a novel study on boxplot-valued data in climatological applications. Our methodologies are applied to the Berkeley Earth Surface Temperature Study. We validate our approaches through comprehensive simulations, comparing Bayesian and frequentist estimators for efficiency and accuracy. The results provide robust insights into climatic trends, particularly summer average temperatures across European countries.

Analysis of meteorological drought using satellite-based rainfall products over southern Ethiopia

2025-03-14T08:04:15+01:00

Analysis of meteorological drought using satellite-based rainfall products over southern Ethiopia
Tesfay Mekonnen Weldegerima and Tewelde Berihu Gebresilassie
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 59–71, https://doi.org/10.5194/ascmo-11-59-2025, 2025
Drought frequently affects southern Ethiopia, and inadequate rainfall data complicate monitoring. Satellite data from three sources were evaluated for accuracy, showing the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) to be the most reliable. We assessed drought severity using the standardized precipitation index, aligning results with historical drought patterns. The study emphasizes that the CHIRPS enhances drought monitoring and early-warning systems in the region.

Proper scoring rules for multivariate probabilistic forecasts based on aggregation and transformation

2025-03-13T08:04:16+01:00

Proper scoring rules for multivariate probabilistic forecasts based on aggregation and transformation
Romain Pic, Clément Dombry, Philippe Naveau, and Maxime Taillardat
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 23–58, https://doi.org/10.5194/ascmo-11-23-2025, 2025
Correctly forecasting weather is crucial for decision-making in various fields. Standard multivariate verification tools have limitations, and a single tool cannot fully characterize predictive performance. We formalize a framework based on aggregation and transformation to build interpretable verification tools. These tools target specific features of forecasts, improving predictive performance characterization and bridging the gap between theoretical and physics-based tools.

Reducing reliability bias in assessments of extreme weather risk using calibrating priors

2025-02-20T08:04:16+01:00

Reducing reliability bias in assessments of extreme weather risk using calibrating priors
Stephen Jewson, Trevor Sweeting, and Lynne Jewson
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 1–22, https://doi.org/10.5194/ascmo-11-1-2025, 2025
We investigate how to make statistical predictions of extreme weather such that events predicted to occur with a probability of 1 % will occur 1 % of the time. We apply the methods we describe to a standard extreme weather attribution example from the recent climate literature. We find that the methods we describe imply that extremes are roughly twice as likely as when estimated using maximum likelihood. We have developed a software package to make it easy to apply these methods.

A non-stationary climate-informed weather generator for assessing future flood risks

2024-11-26T08:04:16+01:00

A non-stationary climate-informed weather generator for assessing future flood risks
Viet Dung Nguyen, Sergiy Vorogushyn, Katrin Nissen, Lukas Brunner, and Bruno Merz
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 195–216, https://doi.org/10.5194/ascmo-10-195-2024, 2024
We present a novel stochastic weather generator conditioned on circulation patterns and regional temperature, accounting for dynamic and thermodynamic atmospheric changes. We extensively evaluate the model for the central European region. It statistically downscales precipitation for future periods, generating long, spatially and temporally consistent series. Results suggest an increase in extreme precipitation over the region, offering key benefits for hydrological impact studies.

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

2024-11-15T08:04:16+01:00

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales
Chalachew Muluken Liyew, Elvira Di Nardo, Rosa Meo, and Stefano Ferraris
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 173–194, https://doi.org/10.5194/ascmo-10-173-2024, 2024
Global warming is a big issue: it is necessary to know more details to make a forecast model and plan adaptation measures. Warming varies in space and time and models often average it over large areas. However, it shows great variations between months of the year. It also varies between regions of the world and between lowland and highland regions. This paper uses statistical and machine learning techniques to quantify such differences between Italy and the UK at different altitudes.

Formally combining different lines of evidence in extreme-event attribution

2024-10-30T08:04:16+01:00

Formally combining different lines of evidence in extreme-event attribution
Friederike E. L. Otto, Clair Barnes, Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, and Robert Vautard
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 159–171, https://doi.org/10.5194/ascmo-10-159-2024, 2024
To assess the role of climate change in individual weather events, different lines of evidence need to be combined in order to draw robust conclusions about whether observed changes can be attributed to anthropogenic climate change. Here we present a transparent method, developed over 8 years, to combine such lines of evidence in a single framework and draw conclusions about the overarching role of human-induced climate change in individual weather events.