Results 51 to 60 of about 5,604 (198)
International Journal of Climatology, EarlyView.ERA5 near‐surface temperature over South America exhibits systematic cold biases in daily maximum temperature and warm biases in daily minimum temperature. To address the continent's strong climatic and topographic heterogeneity, South America was partitioned into five topoclimatic clusters, and four bias‐correction approaches were evaluated. The Multi‐
José Roberto Rozante, Gabriela Rozante
wiley +1 more source
Investigation on the potential of regression kriging for mapping oriental beech forest site productivity in research forest of Tarbiat Modares University [PDF]
مجله جنگل ایران, 2018 Forest resources mapping is a prerequisite for sustainable forest management. Site productivity is a key indicator of forest ecosystem services like wood production, carbon sequestration, etc.
Zahra Ahadi +2 more
doaj
Climatic Drivers of the Area Burned by Winter Wildfires in Northern Italy
International Journal of Climatology, EarlyView.This study investigates winter wildfires occurring between November and April in northern Italy over the period 2008–2022. By integrating high‐resolution burned‐area data with gridded climatic indices derived from 150 weather stations, it identifies spatial and temporal wildfire patterns, highlights the most fire‐prone mountain areas through pixel ...
Alice Baronetti +2 more
wiley +1 more source
Focal‐Feature Regression Kriging
Geographical AnalysisABSTRACT Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models‐such as Ordinary Kriging (OK)‐assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables.
Peng Luo, Yilong Wu, Yongze Song
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International Journal of Climatology, EarlyView.The study provides insights for post‐processing climate model output in complex terrain, useful for climate change impact models. We compared different bias adjustment and downscaling techniques from quantile delta mapping to simple lapse rates. We developed a new spatial downscaling method based on principal components analysis, which showed superior ...
Michael Matiu +4 more
wiley +1 more source
From Rare to Recurrent: Intensifying Hot–Dry Extremes Across Romania
International Journal of Climatology, EarlyView.Romania has undergone a marked shift toward more frequent and spatially extensive compound hot–dry months since the 1990s, with persistent hotspots in the eastern Moldavian Plateau and the lower Danube–Bărăgan–Black Sea lowlands and comparatively buffered conditions at higher elevations.
Viorica Nagavciuc, Monica Ionita
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Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping
Computers and Electronics in AgricultureMachine learning and geostatistics are two fundamentally different frameworks for predicting and spatially mapping soil properties. Geostatistics leverages the spatial structure of soil properties, while machine learning captures the relationship between available environmental features and soil properties.
Jonas Schmidinger +4 more
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Polymer Composites, EarlyView.An integrated experimental, numerical, and explainable machine learning framework for the material‐geometric co‐optimization and multi‐objective Pareto design of robust composite adhesive joints. ABSTRACT The reliability of composite adhesive joints is compromised by non‐linear dependencies between material properties and manufacturing uncertainties ...
Sajjad Karimi
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Frontiers in Plant Science, 2023 The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function.
Yudong Chen +20 more
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Epistemic and aleatoric uncertainty quantification in weather and climate models
Quarterly Journal of the Royal Meteorological Society, EarlyView.Aleatoric and epistemic uncertainties over time on weather and climate time‐scales, estimated through ensembles that sample aleatoric and epistemic uncertainty using Bayesian neural networks for parameterisations in the Lorenz 1996 model. The spread shows the 16th and 84th percentiles.
Laura A. Mansfield +1 more
wiley +1 more source