Metzger, Martin
(2024)
Neural network and data-driven correction methods for improving the simulation of alpine snowpack.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Fisica del sistema terra [LM-DM270], Documento ad accesso riservato.
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Abstract
This study investigates on the internal structure of snowpack, metamorphism, and their evolution during the winter season using numerical models.
A customized snowpack forecast model is used for this scope, enabling to compare simulation's results obtained with different initialization choices and to evaluate methods for improving the accuracy of the snowpack simulation.
Specifically, meteorological data from automatic weather stations (AWS), reanalysis and outputs from numerical weather models (NWMs) are respectively used to simulate snowpack characteristics as an input to the SNOWPACK model, a one-dimensional finite element model developed for avalanche forecasting and snow behavior studies.
The model is validated using a specially built statistical verification method to assess its performance.
In particular, it is evaluated how various meteorological inputs influence snowpack simulations and to identify the best dataset for accurate snowpack representation.
Special emphasis is placed on bias correction techniques and the integration of neural networks to enhance model performance.
Results indicate that neural network approaches, better than bias-corrected meteorological data, offer improved predictions of snow height and internal snow structure when compared to traditional NWM data.
The promising results of this study support new and encouraging possibilities for understanding the processes related to the snow and improving their predictability over the winter season. Although applied to a specific case study, both bias correction and neural networks methods are general enough to be applied to other location of interest for the snow science. Nonetheless, great improvements in the accuracy of both the snowpack internal structure and the snow quantity are produced by these methods.
A similar approach can be used over forecast data to improve the simulations of the snowpack conditions to come.
Abstract
This study investigates on the internal structure of snowpack, metamorphism, and their evolution during the winter season using numerical models.
A customized snowpack forecast model is used for this scope, enabling to compare simulation's results obtained with different initialization choices and to evaluate methods for improving the accuracy of the snowpack simulation.
Specifically, meteorological data from automatic weather stations (AWS), reanalysis and outputs from numerical weather models (NWMs) are respectively used to simulate snowpack characteristics as an input to the SNOWPACK model, a one-dimensional finite element model developed for avalanche forecasting and snow behavior studies.
The model is validated using a specially built statistical verification method to assess its performance.
In particular, it is evaluated how various meteorological inputs influence snowpack simulations and to identify the best dataset for accurate snowpack representation.
Special emphasis is placed on bias correction techniques and the integration of neural networks to enhance model performance.
Results indicate that neural network approaches, better than bias-corrected meteorological data, offer improved predictions of snow height and internal snow structure when compared to traditional NWM data.
The promising results of this study support new and encouraging possibilities for understanding the processes related to the snow and improving their predictability over the winter season. Although applied to a specific case study, both bias correction and neural networks methods are general enough to be applied to other location of interest for the snow science. Nonetheless, great improvements in the accuracy of both the snowpack internal structure and the snow quantity are produced by these methods.
A similar approach can be used over forecast data to improve the simulations of the snowpack conditions to come.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Metzger, Martin
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
snow,snowpack,neural network,data validation,NWMs
Data di discussione della Tesi
28 Ottobre 2024
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Metzger, Martin
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
snow,snowpack,neural network,data validation,NWMs
Data di discussione della Tesi
28 Ottobre 2024
URI
Gestione del documento: