Show threadsport of sacred spherical cowsJun 7, 2025

Just dropping this here for reference.

Essentially, today's fortnightly variant proportions data has been indefinitely postponed.

Not by a three-day weekend (as per two weeks ago) but by a "software issue".

In other news, the "razor-blade throat" XDV scion #Nimbus NB.1.8.1 was near 14% of GISAID data for the fortnight two weeks prior, per Raj's dashboard dropped today.

Nimbus had not been broken out by CDC as of the last data drop.

#CDC #covid-19 #XFG #XFC #PublicHealth

LaRampa.itNov 7, 2024

#BoxingImprota vince al mondiale #XFC 2024

https://www.larampa.news/2024/11/boxing-improta-vince-mondiale-xfc-2024/

Boxing Improta vince al mondiale X.F.C. 2024

La Boxing Improta di Teverola ha partecipato ai mondiali di kick boxing X.F.C. 2024, tenutisi presso il palazzetto “Roman Sport

larampa
MacApr 30, 2024

#StoreDot: #Polestar lädt Prototyp in 10 Minuten auf

https://www.golem.de/news/storedot-polestar-laedt-prototyp-in-10-minuten-auf-2404-184678.html

#Elektromobilität #eAuto #XFC

StoreDot: Polestar lädt Prototyp in 10 Minuten auf - Golem.de

Die Ladezeit werde sich zukünftig mit der Tankzeit eines Benziners decken, verspricht der CEO des Autoherstellers.

Golem.de
LaRampa.itJan 28, 2024

#Teverola. Benemerenza #XFC alla #BoxingImprota

https://www.larampa.it/2024/01/teverola-benemerenza-x-f-c-alla-boxing-improta/

Teverola. Benemerenza X.F.C. alla Boxing Improta

Nella mattinata odierna, è stata conferita la benemerenza sportiva alla Boxing Improta, presso la sala Auditorium del palazzo del C.O.N.I.

larampa
AOSbotJan 22, 2024

Title: Visualizing driving forces of spatially extended systems using the recurrence plot framework.

The increasing availability of highly resolved spatio-temporal data leads to
new opportunities as well as challenges in many scientific disciplines such as
climatology, ecology or epidemiology. This allows more detailed insights into
the investigated spatially extended systems. However, this de [...]

Authors: Maik Riedl, Norbert Marwan, Jürgen Kurths

Link: http://arxiv.org/abs/2401.10309

Visualizing driving forces of spatially extended systems using the recurrence plot framework

The increasing availability of highly resolved spatio-temporal data leads to new opportunities as well as challenges in many scientific disciplines such as climatology, ecology or epidemiology. This allows more detailed insights into the investigated spatially extended systems. However, this development needs advanced techniques of data analysis which go beyond standard linear tools since the more precise consideration often reveals nonlinear phenomena, for example threshold effects. One of these tools is the recurrence plot approach which has been successfully applied to the description of complex systems. Using this technique's power of visualization, we propose the analysis of the local minima of the underlying distance matrix in order to display driving forces of spatially extended systems. The potential of this novel idea is demonstrated by the analysis of the chlorophyll concentration and the sea surface temperature in the Southern California Bight. We are able not only to confirm the influence of El Niño events on the phytoplankton growth in this region but also to confirm two discussed regime shifts in the California current system. This new finding underlines the power of the proposed approach and promises new insights into other complex systems.

arXiv.org
LaRampa.itJan 9, 2024

#Casaluce. #GiovanniImprota riceve onorificenza alla carriera #XFC 2024

https://www.larampa.it/2024/01/casaluce-giovanni-improta-riceve-onorificenza-alla-carriera-xfc-2024/

Casaluce. Giovanni Improta riceve onorificenza alla carriera XFC 2024

Nella giornata odierna, la World Federation X.F.C. ha conferito a Giovanni Improta l’onorificenza alla carriera per i suoi successi agonistici

larampa
AOSbotNov 16, 2023

Title: Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models.

Modeling is crucial to understanding the effect of greenhouse gases, warming,
and ice sheet melting on the ocean. At the same time, ocean processes affect
phenomena such as hurricanes and droughts. Parameters in th [...]

Authors: Yixuan Sun, Elizabeth Cucuzzella, Steven Brus, Sri Hari Krishna Narayanan, Balu Nadiga, Luke Van Roekel, Jan Hückelheim, Sandeep Madireddy

Link: http://arxiv.org/abs/2311.08421

Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models

Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.

arXiv.org
LaRampa.itOct 22, 2023

#VilladiBriano. #XFC, trionfo mondiale per #GabriellaIovine

https://www.larampa.it/2023/10/villa-di-briano-xfc-trionfo-mondiale-gabriella-iovine/

Villa di Briano. XFC, trionfo mondiale per Gabriella Iovine

Come ogni autunno è data fissa per i fighter l’appuntamento con il campionato Mondiale XFC, sigla internazionale organizzatrice di eventi

larampa
AOSbotSep 11, 2023

Title: Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks.

Statistical postprocessing is used to translate ensembles of raw numerical
weather forecasts into reliable probabilistic forecast distributions. In this
study, we examine the use of permutation-invariant neural networks for this
task. In contrast to previous approache [...]

Authors: Kevin Höhlein, Benedikt Schulz, Rüdiger Westermann, Sebastian Lerch

Link: http://arxiv.org/abs/2309.04452

Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks

Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In contrast to previous approaches, which often operate on ensemble summary statistics and dismiss details of the ensemble distribution, we propose networks that treat forecast ensembles as a set of unordered member forecasts and learn link functions that are by design invariant to permutations of the member ordering. We evaluate the quality of the obtained forecast distributions in terms of calibration and sharpness and compare the models against classical and neural network-based benchmark methods. In case studies addressing the postprocessing of surface temperature and wind gust forecasts, we demonstrate state-of-the-art prediction quality. To deepen the understanding of the learned inference process, we further propose a permutation-based importance analysis for ensemble-valued predictors, which highlights specific aspects of the ensemble forecast that are considered important by the trained postprocessing models. Our results suggest that most of the relevant information is contained in a few ensemble-internal degrees of freedom, which may impact the design of future ensemble forecasting and postprocessing systems.

arXiv.org
AOSbotApr 21, 2023

Title: Towards replacing precipitation ensemble predictions systems using machine learning.

Precipitation forecasts are less accurate compared to other meteorological
fields because several key processes affecting precipitation distribution and
intensity occur below the resolved scale of global weather prediction models.
This requires to use higher resolution simulations. To generate an uncertainty
predictio [...]

Authors: Rüdiger Brecht, Alex Bihlo

Link: http://arxiv.org/abs/2304.10251

Towards replacing precipitation ensemble predictions systems using machine learning

Precipitation forecasts are less accurate compared to other meteorological fields because several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather prediction models. This requires to use higher resolution simulations. To generate an uncertainty prediction associated with the forecast, ensembles of simulations are run simultaneously. However, the computational cost is a limiting factor here. Thus, instead of generating an ensemble system from simulations there is a trend of using neural networks. Unfortunately the data for high resolution ensemble runs is not available. We propose a new approach to generating ensemble weather predictions for high-resolution precipitation without requiring high-resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble.

arXiv.org