Sparse optimization for inverse problems in atmospheric modelling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00457037" target="_blank" >RIV/67985556:_____/16:00457037 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.envsoft.2016.02.002" target="_blank" >http://dx.doi.org/10.1016/j.envsoft.2016.02.002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.envsoft.2016.02.002" target="_blank" >10.1016/j.envsoft.2016.02.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparse optimization for inverse problems in atmospheric modelling

Popis výsledku v původním jazyce

We consider inverse problems in atmospheric modelling represented by a linear system which is based on a source-receptor sensitivity matrix and measurements. Instead of using the ordinary least squares, we add a weighting matrix based on the topology of measurement points and show the connection with Bayesian modelling. Since the source-receptor sensitivity matrix is usually ill-conditioned, the problem is often regularized, either by perturbing the objective function or by modifying the sensitivity matrix. However, both these approaches may be heavily dependent on specified parameters. To ease this burden, we propose to use techniques looking for a sparse solution with a small number of positive elements. Finally, we compare all these methods on the European Tracer Experiment (ETEX) data where there is no apriori information apart from the release position and some measurements.

Název v anglickém jazyce

Sparse optimization for inverse problems in atmospheric modelling

Popis výsledku anglicky

We consider inverse problems in atmospheric modelling represented by a linear system which is based on a source-receptor sensitivity matrix and measurements. Instead of using the ordinary least squares, we add a weighting matrix based on the topology of measurement points and show the connection with Bayesian modelling. Since the source-receptor sensitivity matrix is usually ill-conditioned, the problem is often regularized, either by perturbing the objective function or by modifying the sensitivity matrix. However, both these approaches may be heavily dependent on specified parameters. To ease this burden, we propose to use techniques looking for a sparse solution with a small number of positive elements. Finally, we compare all these methods on the European Tracer Experiment (ETEX) data where there is no apriori information apart from the release position and some measurements.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

<a href="/cs/project/7F14287" target="_blank" >7F14287: Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI)</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Environmental Modelling & Software

ISSN

1364-8152

e-ISSN

—

Svazek periodika

79

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

256-266

Kód UT WoS článku

000374602000018

EID výsledku v databázi Scopus

2-s2.0-84959423146