A5070521058- Meteorological Phenomena and Simulations
- Climate variability and models
- Wind and Air Flow Studies
- Hydrology and Drought Analysis
- Cryospheric studies and observations
- Tropical and Extratropical Cyclones Research
- Energy Load and Power Forecasting
- Time Series Analysis and Forecasting
- SAS software applications and methods
- Geophysics and Gravity Measurements
- Greenhouse Technology and Climate Control
- Data Quality and Management
- Distributed and Parallel Computing Systems
- Metaheuristic Optimization Algorithms Research
- Wind Energy Research and Development
- Air Quality Monitoring and Forecasting
- Neural Networks and Applications
- Atmospheric and Environmental Gas Dynamics
- Vehicle emissions and performance
- Precipitation Measurement and Analysis
CMCC Foundation - Euro-Mediterranean Center on Climate Change
2024
Royal Netherlands Meteorological Institute
2017-2023
National Institute of Meteorology
2020
Wageningen University & Research
2018
Abstract Observations are the foundation for understanding climate system. Yet, currently available land meteorological data highly fractured into various global, regional, and national holdings different variables time scales, from a variety of sources, in mixture formats. Added to this, many still inaccessible analysis usage. To meet modern scientific societal demands as well emerging needs such provision services, it is essential that we improve management curation land-based holdings. We...
The daily maximum and minimum temperature series of the European Climate Assessment & Dataset are homogenized using quantile matching approach. As dataset is large detail metadata generally missing, an automated method locates breaks in based on a comparison with surrounding applies adjustments which estimated homogeneous segments as reference. A total 6,500 have been processed after removing duplicates short series, about 2,100 adjusted. Finally, effect homogenization trend estimation...
Climate change is driving an alarming rise in extreme weather events, including heatwaves, droughts, and floods. Among these, heatwaves stand out as the deadliest, with profound widespread impacts across multiple sectors. Europe emerging a global heatwave hotspot, frequency increasing almost four times faster than other northern midlatitudes. Agriculture most vulnerable sector to temperature extremes, making adaptation measures essential support food security worldwide.  In...
The early-warning of heatwaves using seasonal forecasting systems has the potential to mitigate economic losses and loss life. Because limited reliability computational expense dynamical forecast systems, efforts in recent years have turned exploiting power Machine Learning. Recent seen data-driven methods deliver added-value for short-term forecasting, yet work on scale is not as mature. Within framework European Horizon project “CLINT - Climate Intelligence”, a purely...
Heatwaves heavily affect European public health, society and economy. A full understanding of the drivers behind occurrence intensity heatwaves (HWs) is one priorities H2020 CLimate INTelligence (CLINT) project. Particular attention given to detection attribution HW on their future evolution thanks Storylines method. For implementation this technique, it important assess capability climate models in thoroughly identifying relationships between HW. The relevant extreme event are selected...
Abstract We describe a global dataset of quality‐controlled in situ daily air temperature observations covering the period 1850–2015, developed framework EUSTACE (EU Surface Temperature for All Corners Earth) project ( www.eustaceproject.org ). The includes total 35,364 series maximum and minimum obtained from seven different collections. About 97% are publicly available common format, while remaining 3% can be original data providers. Unlike other similar products, duplicates have been...
Abstract Day-to-day variations in surface air temperature affect society many ways, but daily measurements are not available everywhere. Therefore, a global picture cannot be achieved with made situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org ) produce European multidecadal ensembles of analyses complementary those dynamical reanalyses, integrating...
Abstract Homogenization of daily temperature series is a fundamental step for climatological analyses. In the last decades, several methods have been developed, presenting different statistical and procedural approaches. this study, four homogenization (together with two variants) tested compared. This has performed constructing benchmark dataset, where segments homogeneous are replaced simultaneous measurements from neighboring series. generates inhomogeneous (the test set) whose version...
Growing season length (GSL) indices derived from surface air temperature are frequently used in climate monitoring applications. The widely Expert Team on Climate Change Detection and Indices (ETCCDI) definition aims to give a broadly applicable measure of the GSL that is indicative duration mild part year. In this paper long‐term trends index compared with an alternative calculated using time series decomposition technique (empirical ensemble mode [EEMD]). It demonstrated ETCCDI departs...
<p>The comparison of simulated climate with observed daily values allows to assess their reliability and the soundness projections on future. Frequency amplitude extreme events are fundamental aspects that simulations need reproduce. In this work six models developed within High Resolution Model Intercomparison Project compared over Europe homogenized version observational E-OBS gridded dataset. This is done by comparing averages, extremes trends summer maximum temperature...
ABSTRACT Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as the city center rural area or nearby airport, is causes discontinuities in these long that may affect trend estimates. In this paper, an updated procedure composition series, by combining data stations, introduced. It couples evolution blending already implemented within European Climate Assessment Dataset (ECA&D, which...
Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these remains challenging, as their complex interactions with large-scale climatic variables difficult to capture traditional statistical dynamical models. This work presents a general method for driver identification in climate events. A novel framework (STCO-FS) is proposed identify key immediate (short-term) HW drivers by combining clustering algorithms an ensemble...
Heatwaves heavily affect European public health, society and economy. A full understanding of the drivers behind occurrence intensity heatwaves (HW) is one priorities H2020 CLimate INTelligence (CLINT) project. Particular attention given to detection attribution HWs in future climate projections. However, it important assess capability models thoroughly describe relationships between HWs. For this reason, a feature selection framework, based on Coral Reef Optimization (Salcedo-Sanz et al.,...
As a consequence of limited reliability dynamical forecast systems, particularly over Europe, efforts in recent years have turned to exploiting the power Machine Learning methods extract information on drivers extreme temperature from observations and reanalysis. Meanwhile, diverse impacts heat driven development new indicators which take into account nightime temperatures humidity. In H2020 CLimate INTelligence (CLINT) project, feature selection framework is being developed find combination...
Effective forecasting of weather events, especially extremes, is critical for minimizing potential damage and ensuring public safety. Yet, ensemble forecasts that allow to represent uncertainty in predictions like ECMWF-ENS suffer from biases dispersion issues. These shortcomings decrease the skill introduce need statistical post-processing methods Ensemble Model Output Statistics (EMOS) Quantile Regression Forest (QRF) enhance forecast quality. While these increase overall performance...
Machine Learning (ML) encompasses various techniques and algorithms that have proven highly effective in addressing complex climate science tasks. In particular, using ML to detect forecast extreme events has gained much attention recently. Considering the vast volume of spatial temporal data available, employment data-driven methodologies becomes indispensable for effectively uncovering potential drivers these events. This study arises with ambition proposing a comprehensive general...
In the framework of KNMI’s Early Warning Center (EWC), ECMWF ensemble (ENS) predictions are used to issue medium-range forecasts severe weather. Timely wind gusts extremes important prevent potential damage. However, affected by biases and under- or over-dispersion. These errors lead a reduction in skill forecasts, especially for long lead-times extreme cases, such as windstorms deep convective episodes. Hence, statistical post-processing is fundamental step establishment skillful...
Ensemble forecasts are important due to their ability characterize forecast uncertainty, which is fundamental when forecasting extreme weather. however often biased and underdispersed thus need be post-processed.A common approach for this the use of ensemble model output statistics (EMOS), where a parametric distribution fitted with limited number predictors. With recent advances in computer science increased amounts data available, machine learning techniques, like random forests, becoming...