Kriging‐variance‐based multi‐member ensembles of radar–rain‐gauge precipitation estimates: Application in Switzerland

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (5)

Page 1 sur 1

What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

Alexis Berne, Anne-Claire Marie Billault--Roux

Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

American Meteorological Society2025

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Invariance of the Double-Moment Normalized Raindrop Size Distribution through 3D Spatial Displacement in Stratiform Rain

Alexis Berne, Timothy Hugh Raupach

Double-moment normalization of the drop size distribution (DSD) summarizes the DSD in a compact way, using two of its statistical moments and a "generic'' double-moment normalized DSD function. Results are presented of an investigation into the invariance ...

Amer Meteorological Soc2017

Threshold modeling of extreme spatial rainfall

Anthony Christopher Davison, Raphaël Mutzner, Emeric Rolland Georges Thibaud

We propose an approach to spatial modeling of extreme rainfall, based on max-stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models are coherent with classical extreme-value theory and allow ...

American Geophysical Union2013

Likelihood-Based Inference for Max-Stable Processes

Simone Padoan

The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes However, their application is complicated due to the unavailability of the multivariate density function and so likehhood-based methods r ...

American Statistical Association2010