Publications associées à 2DVD Data Revisited: Multifractal Insights into Cuts of the Spatiotemporal Rainfall Process

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

2023

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Michael Christoph Gastpar, Aditya Pradeep

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

2023

On the Use of Benford’s Law to Assess the Quality of the Data Provided by Lightning Locating Systems

Farhad Rachidi-Haeri, Marcos Rubinstein, Amirhossein Mostajabi, Wolfgang Schulz

Lightning causes significant damage and casualties globally by directly striking humans and livestock, by igniting forest fires, and by inducing electrical surges in electronic infrastructure, airplanes, rockets, etc. Monitoring the evolution of thundersto ...

2022

Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning

Michael Lehning, Jérôme François Sylvain Dujardin

Predicting wind flow in highly complex terrain like the Alps is a challenge for all models. When physical processes need to be resolved in a spatially explicit manner, grids with high horizontal resolution of a few hundred meters are often required and dra ...

WILEY2022

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Sparsely observed functional time series: estimation and prediction

Victor Panaretos, Tomas Rubin

Functional time series analysis, whether based on time or frequency domain methodology, has traditionally been carried out under the assumption of complete observation of the constituent series of curves, assumed stationary. Nevertheless, as is often the c ...

INST MATHEMATICAL STATISTICS2020

Time- and Space-Efficient Spatial Data Analytics

Mirjana Pavlovic

Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...

EPFL2019

Efficient User Guidance for Validating Participatory Sensing Data

Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell pho ...

ASSOC COMPUTING MACHINERY2019

Multifractal Analysis of Snowfall Recorded Using a 2D Video Disdrometer

Alexis Berne, Timothy Hugh Raupach

Universalmultifractal (UM) analysiswas used to investigate the scaling properties of snowfall at high temporal and spatial resolutions. Snowfall datawere recorded using a 2Dvideo disdrometer (2DVD) in the SwissAlps. Six 1-h-long periods of snowfall, half i ...

American Meteorological Society2017

Variability of the raindrop size distribution across scales in Mediterranean rainfall : characterisation and stochastic simulation

Timothy Hugh Raupach

Measurement of rain is made difficult by the high variability of the precipitation process, down to raindrop scale. Point measurements are generally accurate, but their lack of spatial representativeness is a significant limitation. Weather radars indirect ...

EPFL2016

Small-Scale Variability of the Raindrop Size Distribution and Its Effect on Areal Rainfall Retrieval

Alexis Berne, Timothy Hugh Raupach

The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the ...

American Meteorological Society2016

Space-time modelling of extreme events

Anthony Christopher Davison, Raphaël Huser

Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replicati ...

Wiley-Blackwell2014

Managing Evolving Uncertainty in Trajectory Databases

Saket Sathe, Ho Young Jeung

Modern positioning technologies enable collecting trajectories from moving objects across different locations over time, typically containing time-varying measurement errors of positioning systems. Unfortunately, current models on uncertain trajectories ar ...

Ieee Computer Soc2014

Towards Enabling Probabilistic Databases for Participatory Sensing

Karl Aberer, Saket Sathe, Quoc Viet Hung Nguyen, Chi Thang Duong

Participatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for world-wide applicati ...

2014

Stochastic simulation of intermittent DSD fields in time

Marc Schleiss, Alexis Berne, Joël Jaffrain

A method for the stochastic simulation of (rain)drop size distributions (DSDs) in space and time using geostatistics is presented. At each pixel, the raindrop size distribution is described by a Gamma distribution with two or three stochastic parameters. T ...

American Meteorological Society2012

Experimental Quantification of the Sampling Uncertainty Associated with Measurements from PARSIVEL Disdrometers

Alexis Berne, Joël Jaffrain

The variability of the (rain)drop size distribution (DSD) in time and space is an intrinsic property of rainfall, of primary importance for various environmental fields such as remote sensing of precipitation for example. DSD observations are usually colle ...

American Meteorological Society2011

Catchment travel time distributions and water flow in soils

Andrea Rinaldo, Enrico Bertuzzo, Ludovico Nicotina, Gianluca Botter

Many details about the flow of water in soils in a hillslope are unknowable given current technologies. One way of learning about the bulk effects of water velocity distributions on hillslopes is through the use of tracers. However, this paper will demonst ...

2011

HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

Bettina Schaefli

Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places ...

2011

Stochastic simulation of intermittent DSD fields in time

Marc Schleiss, Alexis Berne

A method for the stochastic simulation of (rain)drop size distributions (DSDs) in space and time using geostatistics is presented. At each pixel, the raindrop size distribution is described by a Gamma distribution with two or three stochastic parameters. T ...

2010