Search results for "Forecasting"
showing 10 items of 329 documents
Current and emerging developments in subseasonal to decadal prediction
2020
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-…
MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe
2015
Abstract. This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more…
In vitro fertilization and andrology laboratory in 2030: expert visions.
2021
The aim of this article is to gather 9 thought leaders and their team members to present their ideas about the future of in vitro fertilization and the andrology laboratory. Although we have seen much progress and innovation in the laboratory over the years, there is still much to come, and this article looks at what these leaders think will be important in the future development of technology and processes in the laboratory.
A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe
2018
The paper suggests a methodology for predicting next-year seasonal pollen index (SPI, a sum of daily-mean pollen concentrations) over large regions and demonstrates its performance for birch in Northern and North-Eastern Europe. A statistical model is constructed using meteorological, geophysical and biological characteristics of the previous year). A cluster analysis of multi-annual data of European Aeroallergen Network (EAN) revealed several large regions in Europe, where the observed SPI exhibits similar patterns of the multi-annual variability. We built the model for the northern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia and…
Forecasting basketball players' performance using sparse functional data*
2019
Statistics and analytic methods are becoming increasingly important in basketball. In particular, predicting players’ performance using past observations is a considerable challenge. The purpose of this study is to forecast the future behavior of basketball players. The available data are sparse functional data, which are very common in sports. So far, however, no forecasting method designed for sparse functional data has been used in sports. A methodology based on two methods to handle sparse and irregular data, together with the analogous method and functional archetypoid analysis is proposed. Results in comparison with traditional methods show that our approach is competitive and additio…
Deep learning and process understanding for data-driven Earth system science
2017
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybri…
The rise of the middle author: Investigating collaboration and division of labor in biomedical research using partial alphabetical authorship
2017
Contemporary biomedical research is performed by increasingly large teams. Consequently, an increasingly large number of individuals are being listed as authors in the bylines, which complicates the proper attribution of credit and responsibility to individual authors. Typically, more importance is given to the first and last authors, while it is assumed that the others (the middle authors) have made smaller contributions. However, this may not properly reflect the actual division of labor because some authors other than the first and last may have made major contributions. In practice, research teams may differentiate the main contributors from the rest by using partial alphabetical author…
Noise-induced behavioral change driven by transient chaos
2022
We study behavioral change in the context of a stochastic, non-linear consumption model with preference adjusting, interdependent agents. Changes in long-run consumption behavior are modelled as noise induced transitions between coexisting attractors. A particular case of multistability is considered: two fixed points, whose immediate basins have smooth boundaries, coexist with a periodic attractor, with a fractal immediate basin boundary. If a trajectory leaves an immediate basin, it enters a set of complexly intertwined basins for which final state uncertainty prevails. The standard approach to predicting transition events rooted in the stochastic sensitivity function technique due to Mil…
Surface to boundary layer coupling in the urban area of Lisbon comparing different urban canopy models in WRF
2019
Abstract This work presents a sensitivity study to evaluate different Urban Canopy Models (UCM) existing within the Weather Research and Forecasting Model (WRF) in the urban area of Lisbon, Portugal. Several hind-cast simulations were carried out for a selected period in July 2010, in which synoptic conditions favoured urban heat island formation. We aim to gain knowledge on the feedback of modified urban canopy representation in WRF on local scale meteorology and the boundary-layer dynamics over the urban area, by comparing a single layer urban canopy model (SLUCM) and a more sophisticated multi-layer building effect parametrisation (BEP). We find significant differences in the characteris…
[Coronary microvascular dysfunction: past, present, and future of an evolving disease].
2017
Coronary atherosclerosis is the main cause of myocardial ischemia. Nevertheless 10-30% of patients with angina has angiographically normal coronary arteries. In the last 30 years, several studies showed that in these patients the symptoms can be caused by dysfunction of the coronary microcirculation. Coronary microvascular dysfunction (CMVD) occurring in patients affected by specific cardiac or systemic diseases may be due to mechanisms of the underlying disease. On the other hand, in several patients affected by angina with angiographically normal coronary arteries, there is no specific disease, and CMVD only is responsible for the clinical picture. This condition can be defined as leading…