Suomalais-ugrilaiset kielet ja internet -projekti 2013-2019

Jauhiainen, Tommi; Jauhiainen, Heidi; Lindén, Krister

doi:10.31885/9789515150257.21

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…where the altitude-dependent correlated uncertainties of the Vaisala product u corr,EDT (∆T rad ) are less than 0.2 K (k = 1) at 35 km (Vaisala, 2013;Jauhiainen, 2021). Due to the large number of data points N , u uncorr (T ) vanishes, so that the resulting total uncertainty reduces to u corr (∆T rad ).…”

Section: Comparison With Manufacturer Correctionmentioning

confidence: 99%

Laboratory characterisation of the radiation temperature error of radiosondes and its application to the GRUAN data processing for the Vaisala RS41

Rohden

Sommer

Naebert

et al. 2021

Preprint

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Abstract. The paper presents the Simulator for Investigation of Solar Temperature Error of Radiosondes (SISTER), a setup that was developed to quantify the solar heating of the temperature sensor of radiosondes under laboratory conditions by recreating as closely as possible the atmospheric and illumination conditions that are encountered during a daytime radiosounding ascent. SISTER controls the pressure (3 hPa to 1020 hPa) and ventilation speed of the air inside the windtunnel-like setup to simulate the conditions between the surface and 35 km altitude, to determine the dependence of the radiation temperature error on the irradiance and the convective cooling. The radiosonde is mounted inside a quartz tube, while the complete sensor boom is illuminated by an external light source to include the conductive heat transfer between sensor and boom. A special feature of SISTER is that the radiosonde is rotated around its axis to imitate the spinning of the radiosonde in flight. The characterisation of the radiation temperature error is performed for various pressures, ventilation speeds and illumination angles, yielding a 2D-parameterisation of the radiation error for each illumination angle, with an uncertainty smaller than 0.2 K (k = 2) for typical ascend speeds. This parameterisation is applied in the GRUAN processing for radiosonde data, which relies on the extensive characterisation of the sensor properties to produce a traceable reference data product which is free of manufacturer dependent effects. The GRUAN radiation correction model combines the laboratory characterisation with model calculations of the actual radiation field during the sounding to estimate the correction profile. In the second part of this paper it is described how this procedure was applied in the development of the GRUAN data product for the Vaisala RS41 radiosonde (version 1, RS41-GDP.1). The magnitude of the averaged correction profile increases gradually from 0.1 K at the surface to approximately 0.8 K at 35 km altitude. Comparison between sounding data (N = 154) that were GRUAN-processed and Vaisala-processed reveal that the daytime differences are smaller than +0.1 K (GRUAN – Vaisala) in the troposphere and increase above the tropopause steadily with altitude to +0.35 K (GRUAN – Vaisala) at 35 km. These differences are just within the limits of the combined uncertainties (with coverage factor k = 2) of both data products, meaning that the GRUAN processing and the Vaisala processing are in agreement.

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Section: Comparison With Manufacturer Correctionmentioning

confidence: 99%

Laboratory characterisation of the radiation temperature error of radiosondes and its application to the GRUAN data processing for the Vaisala RS41

Rohden

Sommer

Naebert

et al. 2021

Preprint

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“…There have been several approaches to language detection including detection of Finnish from web corpora (see Jauhiainen et al, 2021). Similarly, native Finnish has been automatically identified from learner's Finnish (Malmasi and Dras, 2014).…”

Section: Parsingmentioning

confidence: 99%

The Current State of Finnish NLP

Hämäläinen¹,

Alnajjar²

2021

Preprint

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There are a lot of tools and resources available for processing Finnish. In this paper, we survey recent papers focusing on Finnish NLP related to many different subcategories of NLP such as parsing, generation, semantics and speech. NLP research is conducted in many different research groups in Finland, and it is frequently the case that NLP tools and models resulting from academic research are made available for others to use on platforms such as Github. TiivistelmäSuomen kielen koneelliseen käsittelyyn on tarjolla paljon valmiita työkaluja ja resursseja.Tässä artikkelissa tarkastelemme viimeaikoina julkaistuja tieteellisiä artikkeleita, joissa keskitytään suomen kielen kieliteknologiaan.Tarkastelemme kieliteknologian eri alaluokkia, kuten jäsentämistä, tuottamista, semantiikkaa ja puheetta. kieliteknologista tutkimusta tehdään Suomessa monissa eri tutkimusryhmissä, ja usein akateemisen tutkimuksen tuloksena tuotetut kieliteknologian työkalut ja mallit julkaistaan muiden käytettäväksi esimerkiksi Githubissa.

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