Dimension reduction approaches for atmospheric remote sensing of greenhouse gases

Hiilidioksidi (CO2) ja metaani (CH4) ovat merkittävimmät ihmisperäiset kasvihuonekaasut, joilla molemmilla on huomattava vaikutus ilmastonmuutokseen ja ilmakehän lämpenemiseen. Näiden kaasujen pitoisuuksien epäsuorat kaukokartoitusmittaukset ovat oleellinen osa ihmisperäisten päästöjen kehityksen seurannassa. Näitä mittauksia tarvitaan myös arvioitaessa kasvihuonekaasujen vaikutusta ilmakehän prosesseihin. Edellämainitun tutkimuksen luotettavuus perustuu suurilta osin mittausten epävarmuuden arvioinnin paikkansapitävyyteen, minkä takaamiseksi käytetään korkeatasoista epävarmuusanalyysiä. Tämän väitöskirjatyön tavoitteena on kehittää ja ottaa käyttöön luotettavia ja laskennallisesti tehokkaita epävarmuusanalyysimenetelmiä sovellettuna kasvihuonekaasujen kaukokartoitukseen. Kehitetyt menetelmät perustuvat matemaattisesti käänteisongelmien teoriaan ja todennäköisyysteorian sovelluksiin. Käytämme erityisesti informaatioteoreettisia työkaluja pienentääksemme käänteisongelman ulottuvuutta. Tämä johtaa laskennalliseen ongelmaan, joka on huomattavasti nopeampi ratkaista. Työn sovelluskohteita ovat Nasan Orbiting Carbon Observatory 2 -satelliitin hiilidioksidipitoisuusmittaukset sekä Sodankylän Arktisessa Avaruuskeskuksessa sijaitsevan spektrometrin mittaamat metaanipitoisuudet. Jälkimmäisessä keskitymme sekä yksittäisiin mittauksiin että koko aikasarjan tutkimiseen ajalta 2009–2018. Kehitetyt menetelmät toimivat erittäin hyvin käsitellyissä sovelluksissa luoden pohjaa uusille operatiivisille epävarmuusanalyysialgoritmeille.Carbon dioxide (CO2) and methane (CH4) are two most significant anthropogenic greenhouse gases contributing to climate change and global warming. Indirect remote sensing measurements of atmospheric concentrations of these gases are crucial for monitoring manmade emissions and understanding their effects and related atmospheric processes. The reliability of these studies depends largely on robust uncertainty quantification of the measurements, which provides rigorous error estimates and confidence intervals for all results. The main goal of this work is to develop and implement rigorous, robust and computationally efficient means of uncertainty quantification for atmospheric remote sensing of greenhouse gases. We consider both CO2 measurements by NASA’s Orbiting Carbon Observatory 2 (OCO-2) and CH4 measurements by Sodankylä Arctic Space Center’s Fourier Transform Spectrometer (FTS), the latter being studied from the perspectives of both individual measurements, and the entire time series from 2009-2018. Our approach leverages recent mathematical results on dimension reduction to produce novel algorithms that are a step towards thorough and efficient operational uncertainty quantification in the field of atmospheric remote sensing. Mathematically, the process of inferring gas concentrations from indirect measurements is an ill-posed inverse problem, meaning that a well-defined solution doesn't exist without proper regularization. Bayesian approach utilizes probability theory to provide a regularized solution to the inverse problem as a posterior probability distribution. The posterior distribution is conventionally approximated using a Gaussian distribution, and results are reported as the mean of the distribution as a point estimate, and the corresponding variance as a measure of uncertainty. In reality, due to non-linear physics models used in the computations, the posterior is not well approximated by a Gaussian distribution, and ignoring its actual shape can lead to unpredictable errors and inaccuracies in the retrieval. Markov chain Monte Carlo (MCMC) methods offer a robust way to explore the actual properties of posterior distributions, but they tend to be computationally infeasible as the dimension of the state vector increases. In this work, the low intrinsic information content of remote sensing measurements is exploited to implement the Likelihood-Informed Subspace (LIS) dimension reduction method, which increases the computational efficiency of MCMC. Novel algorithms using LIS are implemented to abovementioned atmospheric CH4 profile and column-averaged CO2 concentration inverse problems

Dimension reduction approaches for atmospheric remote sensing of greenhouse gases

Carbon dioxide (CO2) and methane (CH4) are two most significant anthropogenic greenhouse gases contributing to climate change and global warming. Indirect remote sensing measurements of atmospheric concentrations of these gases are crucial for monitoring man-made emissions and understanding their effects and related atmospheric processes. The reliability of these studies depends largely on robust uncertainty quantification of the measurements, which provides rigorous error estimates and confidence intervals for all results. The main goal of this work is to develop and implement rigorous, robust and computationally efficient means of uncertainty quantification for atmospheric remote sensing of greenhouse gases. We consider both CO2 measurements by NASA’s Orbiting Carbon Observatory 2 (OCO-2) and CH4 measurements by Sodankylä Arctic Space Center’s Fourier Transform Spectrometer (FTS), the latter being studied from the perspectives of both individual measurements, and the entire time series from 2009-2018. Our approach leverages recent mathematical results on dimension reduction to produce novel algorithms that are a step towards thorough and efficient operational uncertainty quantification in the field of atmospheric remote sensing. Mathematically, the process of inferring gas concentrations from indirect measurements is an ill-posed inverse problem, meaning that a well-defined solution doesn't exist without proper regularization. Bayesian approach utilizes probability theory to provide a regularized solution to the inverse problem as a posterior probability distribution. The posterior distribution is conventionally approximated using a Gaussian distribution, and results are reported as the mean of the distribution as a point estimate, and the corresponding variance as a measure of uncertainty. In reality, due to non-linear physics models used in the computations, the posterior is not well approximated by a Gaussian distribution, and ignoring its actual shape can lead to unpredictable errors and inaccuracies in the retrieval. Markov chain Monte Carlo (MCMC) methods offer a robust way to explore the actual properties of posterior distributions, but they tend to be computationally infeasible as the dimension of the state vector increases. In this work, the low intrinsic information content of remote sensing measurements is exploited to implement the Likelihood-Informed Subspace (LIS) dimension reduction method, which increases the computational efficiency of MCMC. Novel algorithms using LIS are implemented to abovementioned atmospheric CH4 profile and column-averaged CO2 concentration inverse problems.Hiilidioksidi (CO2) ja metaani (CH4) ovat merkittävimmät ihmisperäiset kasvihuonekaasut, joilla molemmilla on huomattava vaikutus ilmastonmuutokseen ja ilmakehän lämpenemiseen. Näiden kaasujen pitoisuuksien epäsuorat kaukokartoitusmittaukset ovat oleellinen osa ihmisperäisten päästöjen kehityksen seurannassa. Näitä mittauksia tarvitaan myös arvioitaessa kasvihuonekaasujen vaikutusta ilmakehän prosesseihin. Edellämainitun tutkimuksen luotettavuus perustuu suurilta osin mittausten epävarmuuden arvioinnin paikkansapitävyyteen, minkä takaamiseksi käytetään korkeatasoista epävarmuusanalyysiä. Tämän väitöskirjatyön tavoitteena on kehittää ja ottaa käyttöön luotettavia ja laskennallisesti tehokkaita epävarmuusanalyysimenetelmiä sovellettuna kasvihuonekaasujen kaukokartoitukseen. Kehitetyt menetelmät perustuvat matemaattisesti käänteisongelmien teoriaan ja todennäköisyysteorian sovelluksiin. Käytämme erityisesti informaatioteoreettisia työkaluja pienentääksemme käänteisongelman ulottuvuutta. Tämä johtaa laskennalliseen ongelmaan, joka on huomattavasti nopeampi ratkaista. Työn sovelluskohteita ovat Nasan Orbiting Carbon Observatory 2 -satelliitin hiilidioksidipitoisuusmittaukset sekä Sodankylän Arktisessa Avaruuskeskuksessa sijaitsevan spektrometrin mittaamat metaanipitoisuudet. Jälkimmäisessä keskitymme sekä yksittäisiin mittauksiin että koko aikasarjan tutkimiseen ajalta 2009–2018. Kehitetyt menetelmät toimivat erittäin hyvin käsitellyissä sovelluksissa luoden pohjaa uusille operatiivisille epävarmuusanalyysialgoritmeille

Tilastollisen inversio-ongelman dimensioreduktio käyttäen informatiivisia suuntia

Tutkielmassa esitellään laskennallisten inversio-ongelmien teoriaa erityisesti tilastollista läahestymistapaa käyttäen. Kirjallisuuden ja tieteellisten artikkelien perusteella tutustutaan mittauksen informaatiosisältöön ja esitellään menetelmä inversio-ongelman dimension alentamiseksi. Tätä menetelmää sovelletaan ilmakehän kaukokartoitukseen metaanin tiheysprofiilin ratkaisemisen nopeuttamiseksi. Laskennallisessa inversio-ongelmassa ratkaistaan yhtälö y = F(x) + e missä F: A -> B on suora malli tila-avaruudesta A data-avaruuteen B, x on tuntematon, y on kohinallinen mittaus ja e on esimerkiksi mittausvirheestä johtuvaa kohinaa. Tilastollisessa inversiossa mallinnetaan kaikkia ongelman parametreja satunnaismuuttujilla. Käyttämällä Bayesin kaavaa saadaan inversio-ongelman ratkaisuksi posterioritodennäköisyysjakauma yhdistelemällä tuntemattoman x prioritodennäköisyysjakaumaa ja mittauksesta saatavaa uskottavuusjakaumaa. Tutkielmassa rajoitutaan tarkastelemaan reaaliavaruuksien välisiä kuvauksia, mutta tulokset voidaan yleistää koskemaan yleisempiä Hilbertin avaruuksia. Matemaattisena esitietona esitellään lieaarialgebrasta ominais-ja singulaariarvohajotelmat ja näytetään esimerkkinä niiden käyttöä pääkomponenttianalyysissä. Inversio-ongelmien klassista teoriaa käydään läpi vain pintapuolisesti, ja käsitellään syvemmin tilastollista lähestymistapaa normaalijakautuneita satunnaismuuttujia käyttäen. Annetaan myös joitain esimerkkejä priorien rakentamisesta ja käytöstä. Numeerisista ratkaisumenetelmistä esitellään lyhyesti tutkielmassa käytettävät Levenbergin-Marquardtin algoritmi (LevMar) sekä Markovin ketju Monte Carlo -menetelmä (MCMC). Mittaus sisältää usein vain rajallisen märän informaatiota. Lähdekirjallisuuden tulosten perusteella esitellään tapa tutkia mittauksen informaatiosisältöä sekä löytää inversio-ongelman informatiivinen aliavaruus (LIS). Näytetään myös tässä aliavaruudessa löydetyn ratkaisun approksimoivan ongelman oikeaa ratkaisua mahdollisimman hyvin. Lopuksi sovelletaan kehitettyä menetelmää Ilmatieteen laitoksen Sodankylän tutkimusasemalla suoritettavan FTIR-mittauksen yksinkertaistettuun versioon sekä vertaillaan täyden ja redusoidun avaruuden ratkaisuja keskenään. Tutkielma on tehty kokonaisuudessaan Ilmatieteen laitoksella Uudet havaintomenetelmät -yksikön Ilmakehän kaukokartoitus -ryhmässä vuosina 2016-2017

Digitalisaatio suomalaisten yritysten taloustoiminnossa

The financial function of corporations around the world is transforming. This is due to the accelerating clock speed of business, the increasing pressure to reduce costs and improve operational efficiency but also the growing expectations and demand for better services with more advanced deliverables. Large corporations view disruptive technologies as the most profitable solution to fulfill the demanding requirements. Without a digital transformation, they risk becoming an impediment, a bottleneck for the business. The paradigm shift is also visible in the transforming role of the chief financial officer (CFO). Often perceived as traditionalists and corporate auditors, CFOs are now assuming the roles of financial strategists, business partners and, increasingly, digital leaders. This changes the CFO function’s modus operandi significantly, even more so in the future as the transformation accelerates. However, the status of digitalization in Finnish corporations is unclear with little research available. The objective of this research is to provide insights to the current status of digitalization in the CFO functions of large Finnish corporations and how the transformations could be accelerated. To approach the problem holistically, it is divided into three parts: the CFO function’s digital maturity, technology enablers in the CFO function and the effects of organizational and cultural factors. The research was conducted in two parts: a literature review and an empirical study. The literature review explored the theoretical framework with a goal to gain knowledge on the topics. In the empirical study, semi-structured interviews were used to validate the contents of the findings from the theory. Then, a questionnaire was used to survey the CFO functions of the largest Finnish corporations on the topics. The sample size of the survey was 42. The results of this research indicate that the digital maturity of Finnish corporations is, on average, lower than their European competitors. The technological maturity is low-er as well, as Finnish CFO functions are slightly slower to adopt disruptive technologies with less complex business applications. On the other hand, the adoption rate is accelerating rapidly. Furthermore, CFOs are especially focused in business process improvements and process automation as those are perceived to deliver the most tangible benefits. The most significant threats that digital transformations face are often organizational and cultural challenges, for example, poor vertical communication of digital strategy and poor organizational agility. The identified key success factors in digital transformations are change management and driving the transformations with digital leadership. The results provide novel information to the business and academic communities on the status of digitalization in the CFO function, on the effects of digitalization on the financial function and on the ways to support digital transformations

Vertical Distribution of Arctic Methane in 2009–2018 Using Ground-Based Remote Sensing

We analyzed the vertical distribution of atmospheric methane (CH4) retrieved from measurements by ground-based Fourier Transform Spectrometer (FTS) instrument in Sodankyla, Northern Finland. The retrieved dataset covers 2009-2018. We used a dimension reduction retrieval method to extract the profile information, since each measurement contains around three pieces of information about the profile shape between 0 and 40 km. We compared the retrieved profiles against Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) satellite measurements and AirCore balloon-borne profile measurements. Additional comparison at the lowest tropospheric layer was done against in-situ measurements from a 50-m-high mast. In general, the ground-based FTS and ACE-FTS profiles agreed within 10% below 20 km and within 30% in the stratosphere between 20 and 40 km. Our method was able to accurately capture reduced methane concentrations inside the polar vortex in the Arctic stratosphere. The method produced similar trend characteristics as the reference instruments even when a static prior profile was used. Finally, we analyzed the time series of the CH4 profile datasets and estimated the trend using the dynamic linear model (DLM)

Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2

Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem

Verkkosivut Torstec – Tornion Asennus Oy:lle : HTML5 ja CSS3 standardeilla käyttäen Joomla-julkaisujärjestelmää

Opinnäytetyön tavoitteena oli suunnitella ja toteuttaa Torstec - Tornion asennus Oy:lle helposti ylläpidettävät verkkosivut nykyaikaisilla ohjelmointitekniikoilla, ja toteuttaa Torstec Oy:n tarvitsema sähköinen tilausjärjestelmä. Lisäksi opinnäytetyön tavoitteena oli tuottaa Torstec Oy:n työntekijöille oppimateriaali verkkosivujen ylläpitämiseksi. Torstec – Tornion asennus Oy:llä oli olemassa aikaisemmat verkkosivustot, kuitenkin yrityksellä oli tarve uudelle verkkosivustolle kahdesta syystä. Olemassa olevan verkkosivuston sisältö oli vanhentunutta eikä yrityksen työntekijöillä ollut mahdollisuutta päivittää sitä itse. Toisaalta yrityksellä oli tarvetta ottaa vastaan tilauksia verkkosivuston kautta, sellaista toimintoa ei vanhalla verkkosivustolla ollut. Koska rakensin verkkosivut aivan uudestaan, oli mahdollista kokeilla uusia verkkostandardeja verkkosivun rakennuksessa. Toteutin verkkosivuston nykyaikaisilla verkko-ohjelmointikielillä, kuten HTML5-kuvauskielellä ja CSS3-tyyliohjeilla. HTML5-kuvauskielen käyttö mahdollisti sivuston rakenteen suunnittelun semanttisesti, niin että esimerkiksi hakukoneet osaavat tulkita verkkosivuston sisältöä paremmin. Otin uudet verkkostandardit osaksi tutkimuksen teoreettista viitekehystä pyrkien rakentamaan verkkosivuston niiden standardien mukaisesti. Opinnäytetyön tuloksena syntyivät Torstec Oy:lle nykyaikaiset verkkosivut verkkokaupalla. Verkkosivujen toteutuksessa käytin Joomla 1.6 julkaisujärjestelmää, ja verkkosivun eri osiot, kuten verkkokaupan, toteutin Joomla-julkaisujärjestelmän komponenteilla. Tuotin myös Torstec Oy:n työntekijöille oppaita verkkokaupan ja verkkosivun sisällön ylläpidossa. Uudella verkkosivustolla vierailijat pystyvät selaamaan yrityksen tuotteita ja tekemään räätälöityjä tilauksia Torstec Oy:n tuotteista.The main purpose of this thesis was to design and a create web pages for Torstec - Tornion asennus Oy steel manufacturing company. Priorities in the project were to design web pages that are easy to update for staff of Torstec Oy and to implement a web shop in their web pages. There were already existing web pages of Torstec Oy, but there was a need for a new one due to two reasons. First reason was that existing web page was out of date; the content of the web page was no longer accurate. Second reason was a need the ability to receive orders via internet, which was not possible through the old web page. Because I built the web pages from the scratch, it was possible to implement newest web standards to new web pages. I created the web pages with modern internet programming languages including HTML5 markup language and CSS3 style sheet language. With HTML5 markup language it was possible to design a semantic structure to the web pages in a way that machines are able to read web page in more intelligent way. I took the newest web standards as part of theoretical framework and designed the web pages using technology standards that modern web browser manufacturers claim to support. As a result of this thesis a modern web pages for Torstec Oy were created. For the base of web pages I used Joomla 1.6 content management system and different sections of the web pages, such as the web shop, and the contact information, I created using Joomla's components. As part of the thesis I also created series of manuals for the staff of Torstec Oy so they are able to maintain and update the web page and products in the web shop. Once web pages are finished, customers are able to explore and order products from Torstec Oy

Digitalisaatio suomalaisten yritysten taloustoiminnossa

The financial function of corporations around the world is transforming. This is due to the accelerating clock speed of business, the increasing pressure to reduce costs and improve operational efficiency but also the growing expectations and demand for better services with more advanced deliverables. Large corporations view disruptive technologies as the most profitable solution to fulfill the demanding requirements. Without a digital transformation, they risk becoming an impediment, a bottleneck for the business. The paradigm shift is also visible in the transforming role of the chief financial officer (CFO). Often perceived as traditionalists and corporate auditors, CFOs are now assuming the roles of financial strategists, business partners and, increasingly, digital leaders. This changes the CFO function’s modus operandi significantly, even more so in the future as the transformation accelerates. However, the status of digitalization in Finnish corporations is unclear with little research available. The objective of this research is to provide insights to the current status of digitalization in the CFO functions of large Finnish corporations and how the transformations could be accelerated. To approach the problem holistically, it is divided into three parts: the CFO function’s digital maturity, technology enablers in the CFO function and the effects of organizational and cultural factors. The research was conducted in two parts: a literature review and an empirical study. The literature review explored the theoretical framework with a goal to gain knowledge on the topics. In the empirical study, semi-structured interviews were used to validate the contents of the findings from the theory. Then, a questionnaire was used to survey the CFO functions of the largest Finnish corporations on the topics. The sample size of the survey was 42. The results of this research indicate that the digital maturity of Finnish corporations is, on average, lower than their European competitors. The technological maturity is low-er as well, as Finnish CFO functions are slightly slower to adopt disruptive technologies with less complex business applications. On the other hand, the adoption rate is accelerating rapidly. Furthermore, CFOs are especially focused in business process improvements and process automation as those are perceived to deliver the most tangible benefits. The most significant threats that digital transformations face are often organizational and cultural challenges, for example, poor vertical communication of digital strategy and poor organizational agility. The identified key success factors in digital transformations are change management and driving the transformations with digital leadership. The results provide novel information to the business and academic communities on the status of digitalization in the CFO function, on the effects of digitalization on the financial function and on the ways to support digital transformations

A dormant microbial component in the development of pre-eclampsia

Preeclampsia (PE) is a complex, multisystem disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is, in fact, microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, and urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of “preeclampsia” that we assessed has, in fact, also been shown to be raised in response to infection. An infectious component to PE fulfills the Bradford Hill criteria for ascribing a disease to an environmental cause and suggests a number of treatments, some of which have, in fact, been shown to be successful. PE was classically referred to as endotoxemia or toxemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one