Gionata Ghiggi

ETH Zurich | ETH Zürich · Institute of Atmosphere and Climate Science

The proposed approach allows to efficiently combine radar and rain-gauge observations, taking into account the nonstationarity and the intermittency of precipitation as well as to correct for possible errors present in radar composites, such as biases and wind drifts. Since it gives accurate estimates with both high temporal and spatial resolutions...

Freshwater resources are of high societal relevance, and understanding their past variability is vital to water management in the context of ongoing climate change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In situ streamflow observations are used to train a machine learning algor...

River discharge is an Essential Climate Variable (ECV) and is one of the best monitored components of the terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around the world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm and historical weather data to upscale sp...

Snowfall information at the scale of individual particles is rare, difficult to gather, but fundamental for a better understanding of solid precipitation microphysics. In this article we present a dataset (with dedicated software) of in-situ measurements of snow particles in free fall. The dataset includes gray-scale (255 shades) images of snowflak...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular through two techniques: the use of multi-frequency radar measurements and the analysis of radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining both techniques, while relaxing some as...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two tec...

Freshwater resources are of high societal relevance and understanding their past variability is vital to water management in the context of current and future climatic change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In-situ streamflow observations are used to train a machine lea...

Slides of the key-notes J.P. Carbajal: Emulation@EmuMore: What is emulation? Gionata Gigghi: Machine learning in geospatial modelling Andreas Scheiddegger: Hierarchical statistical models & tools Antonio Moreno-Rodenas: Practical uncertainty propagation in dissolved oxygen dynamic simulators

The proposed approach allows to efficiently combine radar and rain-gauge observations, taking into account the nonstationarity and the intermittency of precipitation as well as to correct for possible errors present in radar composites, such as biases and wind drifts. Since it gives accurate estimates with both high temporal and spatial resolutions...

The proposed approach allows to efficiently combine radar and rain-gauge observations, taking into account the nonstationarity and the intermittency of precipitation as well as to correct for possible errors present in radar composites, such as biases and wind drifts. Since it gives accurate estimates with both high temporal and spatial resolutions...