Sanakielet ja lokaalisuus

In this master's thesis we study the generalization of word languages into multi-dimensional arrays of letters i.e picture languages. Our main interest is the class of recognizable picture languages which has many properties in common with the robust class of regular word languages. After surveying the basic properties of picture languages, we present a logical characterization of recognizable picture languages—a generalization of Büchi's theorem of word languages into pictures, namely that the class of recognizable picture languages is the one recognized by existential monadic second-order logic. The proof presented is a recent one that makes the relation between tilings and logic clear in the proof. By way of the proof we also study the locality of the model theory of picture structures through logical locality obtained by normalization of EMSO on those structures. A continuing theme in the work is also to compare automata and recognizability between word and picture languages. In the fourth section we briefly look at topics related to computativity and computational complexity of recognizable picture languages

On the retrieval of atmospheric profiles

Measurements of the Earth's atmosphere are crucial for understanding the behavior of the atmosphere and the underlying chemical and dynamical processes. Adequate monitoring of stratospheric ozone and greenhouse gases, for example, requires continuous global observations. Although expensive to build and complicated to operate, satellite instruments provide the best means for the global monitoring. Satellite data are often supplemented by ground-based measurements, which have limited coverage but typically provide more accurate data. Many atmospheric processes are altitude-dependent. Hence, the most useful atmospheric measurements provide information about the vertical distribution of the trace gases. Satellite instruments that observe Earth's limb are especially suitable for measuring atmospheric profiles. Satellite instruments looking down from the orbit, and remote sensing instruments looking up from the ground, generally provide considerably less information about the vertical distribution. Remote sensing measurements are indirect. The instruments observe electromagnetic radiation, but it is ozone, for example, that we are interested in. Interpreting the measured data requires a forward model that contains physical laws governing the measurement. Furthermore, to infer meaningful information from the data, we have to solve the corresponding inverse problem. Atmospheric inverse problems are typically nonlinear and ill-posed, requiring numerical treatment and prior assumptions. In this work, we developed inversion methods for the retrieval of atmospheric profiles. We used measurements by Optical Spectrograph and InfraRed Imager System (OSIRIS) on board the Odin satellite, Global Ozone Monitoring by Occultation of Stars (GOMOS) on board the Envisat satellite, and ground-based Fourier transform spectrometer (FTS) at Sodankylä, Finland. For OSIRIS and GOMOS, we developed an onion peeling inversion method and retrieved ozone, aerosol, and neutral air profiles. From the OSIRIS data, we also retrieved NO2 profiles. For the FTS data, we developed a dimension reduction inversion method and used Markov chain Monte Carlo (MCMC) statistical estimation to retrieve methane profiles. Main contributions of this work are the retrieved OSIRIS and GOMOS satellite data sets, and the novel retrieval method applied to the FTS data. Long satellite data records are useful for trends studies and for distinguishing between anthropogenic effects and natural variations. Before this work, GOMOS daytime ozone profiles were missing from scientific studies because the operational GOMOS daytime occultation product contains large biases. The GOMOS bright limb ozone product vastly improves the stratospheric part of the GOMOS daytime ozone. On the other hand, the dimension reduction method is a promising new technique for the retrieval of atmospheric profiles, especially when the measurement contains little information about the vertical distribution of gases

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)

OSIRIS inversiomoduulin kehitys ja validointi

OSIRIS on ruotsalaisessa Odin-satelliitissa oleva näkyvän ja UV-alueen spektrometri, joka mittaa keski-ilmakehästä sironnutta auringon säteilyä spektrialueella 280 - 800 nm. Instrumentin spektraalinen resoluutio on noin 1 nm. Mittauksista voidaan päätellä muutamien ilmakehän kaasujen pitoisuudet ratkaisemalla epäsuoriin mittauksiin liittyvä käänteisongelma. Kaasuja, joita OSIRIS mittauksista voidaan havaita, ovat neutraali ilma, 03, NO2 ja aerosolit. Tämän työn tarkoituksena on ollut kehittää edelleen Ilmatieteen laitoksella OSIRIS datan käänteismuunnoksen suorittavaa ohjelmaa. Erityisesti tässä työssä on kehitetty NO2-profiilien ratkaisemista. Tuloksia on verrattu kahden muun OSIRIS-dataa kääntävän algoritmin tuloksiin (DOAS ja Flittner), sekä toisen keski-ilmakehä mittauksia tekevän satelliitti-instrumentin (POAM III:n) mittauksiin. Vertailuissa saatiin erittäin hyvä vastaavuus eri OSIRIS-inversioalgoritmien välille. Keskiarvojen suhteellinen ero oli yleensä vähemmän kuin 5%. OSIRIS ja POAM III NO2-vertailuissa havaittiin kuitenkin suuria eroja alle 25 km:n korkeuksilla, POAM III:n mitatessa systemaattisesti suurempia NO2 pitoisuuksia kuin OSIRIS. Keskiarvojen suhteellinen ero oli suurimmillaan 22 km:n korkeudella (35%). Vertailujen lisäksi työssä on tehty herkkyysanalyysejä simuloidulla datalla. Ne osoittivat, että ylimpien kerroksien invertoidut otsoniarvot ovat suhteellisen herkkiä virheelliselle a priori tiedolle ilmakehän rakenteesta. NO2-profiilit kääntyivät kuitenkin lähes täydellisesti oikein virheellisestä a priori tiedosta huolimatta

CloudnetPy

<ul> <li>Extend mwrpy processing to all sites</li> </ul>If you wish to acknowledge CloudnetPy in your publication, please cite it as below

actris-cloudnet/cloudnetpy: CloudnetPy 1.3.1

<p>This release adds support for RPG Level 1 V4 files</p&gt

CloudnetPy: A Python package for processing cloud remote sensing data

<p>CloudnetPy is a Python software for producing vertical profiles of cloud properties from ground-based remote sensing measurements. CloudnetPy is a rewritten and revised version of the original Matlab code. For more information, see CloudnetPy's <a href="https://cloudnetpy.readthedocs.io/en/latest/">documentation</a>.</p&gt

actris-cloudnet/cloudnetpy: CloudnetPy 1.3.0

replace global attribute "source" with "source_file_uuids" for categorize file and level 2 products to enable provenance on the data portal add more references to global attribute "references" minor fixe

actris-cloudnet/cloudnetpy: Initial release

This is the first CloudnetPy release under actris-cloudnet organization account. The commit history has been truncated. The original repository, which is no longer updated, contains full (and messy) commit history and can be accessed on https://github.com/tukiains/cloudnetpy

actris-cloudnet/cloudnetpy: CloudnetPy 1.3.2

This release fixes bug in the RPG timestamp to date conversion