Browsing by study line "Meteorologia"

Tämä työ tarkastelee kylmää jaksoa Pohjois-Euroopassa ja erityisesti Lapissa 1.1.2017 – 6.1.2017. Tarkastelujaksolla Sodankylässä mitattiin yli neljäkymmentä astetta pakkasta, jonka Euroopan keskipitkien ennusteiden keskuksen säämalli IFS ennusti pintalämpötilan yli kymmenen astetta liian korkeaksi. Kylmä jakso ylettyi aina Bulgariaan ja Kreikaan asti antaen viitteitä laajemmasta säähäiriöstä. Näistä lähtökohdista lähdin tutkimaan, mikäli lämpötilan yliennustuksen syy olisi laajemman synoptisen skaalan häiriön epätarkka ennustaminen. Työssä visualisoin IFS:n paine ja lämpötilakenttiä Euroopan keskuksen metview alustalla ja vertaan niitä synoptiseen analyysiin sekä pinta- ja luotaushavaintoihin Sodankylästä. Käytän pohjana Euroopan keskuksen omaa raporttia poikkeuksellisesta sääilmiöistä, joka kuitenkin keskittyy enemmän Kaakkois-Euroopan poikkeukselliseen kylmyyteen ja voimakkaisiin lumisateisiin. Työssä havaitaan, että IFS ennusti synoptisen skaalan matalapainejärjestelmien ja muiden säähäiriöiden synnyn ja liikkeet tarkastelujaksolla varsin hyvin. Syy pintalämpötilan yliennustamiseen ei arvioni mukaan johdu virtaustilanteen väärästä ennustamisesta, vaan mallin tavasta käsitellä pintalämpötilaa. Erittäin stabiileissa olosuhteissa oletukset, joiden perusteella mallin pintalämpötila lasketaan, eivät tuota järkevää tulosta. Luotauksista havaitaan, että Sodankylässä vallitsi voimakas pintainversio, jota malli ei kykene täysin mallintamaan johtuen tavasta, jolla se käsittelee pinnan ja alimman mallitason välistä kerrosta. Ennustettu lämpötila poikkeaa toteutuneesta kuitenkin niin voimakkaasti, että inversion mallintamiseen liittyvät ongelmat eivät välttämättä ole ainoa virhelähde. Lopuksi tarkastelen lyhyesti raportteja mallin ongelmista ennustaa pintalämpötilaa Suomen talviolosuhteisssa, sekä miten Euroopan keskipitkien säähavaintojen keskus on itse käsitellyt ongelmaa. Globaalimallina IFS on kalibroitu tuottamaan keskimäärin osuvin ennuste koko planeetalla, ja on tärkeä tietää ne rajatapaukset, joissa sen oletukset eivät ole päteviä.

Suomen lentosäähavainnot käyvät läpi murrosta kohti automaatiota. Automaattisiin havaintoihin liittyy laatuongelmia, joten syntyi idea tehdä aiheesta laajempi tutkimus. Tutkimusaineistona käytettiin Rovaniemen lentoaseman havainnontekijöiden vuodesta 2011 lähtien täyttämää verifiointitaulukkoa, jossa ideana on kirjata manuaalisen havainnon tekohetkellä ylös automaattijärjestelmän tarjoamat arvot eri sääsuureille. Vertailtavat parametrit ovat näkyvyys, pilven alaraja ja vallitseva sää. Parametrien automaatin ja ihmisen määrittämät arvot ristiintaulukoitiin jokaiselle kolmelle parametrille erikseen. Tulokset eivät antaneet kovin hyvää kuvaa automaattihavaintojen nykyisestä laadusta, sillä kaikkien kolmen parametrin osalta havainnoista löytyi merkittäviä puutteita arvojen tarkkuudessa ja ajantasaisuudessa. Erot tarkaksi oletettuihin ihmishavaintoihin olivat niin suuria, että esiin nousi kysymyksiä lentoturvallisuuteen ja automaattihavaintojen käytön järkevyyteen liittyen. Tulosten pohjalta esitetään ratkaisuksi merkittäviä parannuksia havaintojärjestelmään sekä havaintojen tilapäistä manualisointia parannusprosessin ajaksi. Tutkielmassa käydään varsinaisen tutkimusosion lisäksi läpi Suomen lentosäähavaintojen teoriaa. Tekstissä pureudutaan syvemmin manuaalisen ja automaattisen havaintomenetelmän perusperiaatteisiin sekä esitellään Suomen lentosäähavaintojen historiaa pääpiirteittäin.

Ilmatieteen laitoksella on otettu käyttöön eri säämallien ennusteita yhdistelevä, niin sanotun konsensusennusteperiaatteen mukainen jälkikäsittelymenetelmä, joka tunnetaan nimellä Blend. Tämä tutkielman tarkoituksena on selvittää Blend-menetelmällä tuotetun tuuliennusteen toimivuutta Suomen merialueilla käyttämällä muutamaa yleiseen käyttöön vakiintunutta sääennusteiden verifiointimenetelmää. Verifiointi on toteutettu vertaamalla Blend-ennusteen tuulennopeusarvoja niin ikään jälkikäsittelyllä tuotettuihin potentiaalituuliarvoihin 25:llä Suomen merialueilla sijaitsevalla havaintoasemalla. Potentiaalituulta on päätetty käyttää alkuperäisten tuulihavaintojen sijasta, koska se parantaa eri sääasemilta tulevien mittaustulosten keskinäistä vertailukelpoisuutta ja näin ollen tekee verifiointituloksista paremmin koko alueelle yleistettäviä. Tulokset osoittavat odotetusti, että merkittävimmät Blend-tuuliennusteen toimivuuteen vaikuttavat tekijät ovat tuulennopeus ja ennustepituus – ennustevirhe kasvaa yleisesti suuremmilla tuulennopeuksilla ja pidemmillä ennustepituuksilla. Myös muilla muuttujilla, kuten vuorokauden- ja vuodenajalla sekä tuulen suunnalla, havaittiin olevan jonkin verran vaikutusta ennustevirheeseen. Useimmissa säätilanteissa Blend-ennusteen voidaan todeta olevan toimivuudeltaan varsin hyvä ja tasalaatuinen. Blend-ennusteen merkittävin ongelma on etenkin suurilla tuulennopeuksilla huomattavan suuri negatiivinen harha (bias), eli ennustetut tuulennopeudet ovat havaintoihin nähden selvästi liian heikkoja. Tästä johtuen Blend ei useimmissa tapauksessa kykene ennustamaan kovimpia tuulia, jotka ovat harvinaisuudestaan huolimatta operatiivisen sääennustamisen kannalta kaikista tärkeimpiä mm. merialueille annettavien tuulivaroitusten vuoksi. Menetelmä on kuitenkin kehityskelpoinen, ja jos ennusteharha pystytään jatkossa minimoimaan laskennassa paremmin, se saattaa kyetä tuottamaan jopa varsinaisia säämalleja parempia tuuliennusteita.

In this thesis I have examined wind gust cases in Finland that have occurred during the summer season between 2010 and 2021. The main goal of the thesis was to find convective wind gust cases of non-tornadic origin, also known as damaging straight-line winds, and find out whether the gust on the surface could have been, in theory, solely caused by the slow advection of strong upper-level winds to the surface or whether another factor, such as a strong downdraft, must have played a role in the creation of the gust. Convective wind gusts occur in Finland every summer, but despite this, the amount of research on them and the damage they can cause has been relatively small in the past compared to gusts caused by extratropical cyclones, for example. To find suitable wind gust cases, weather data from the Finnish Meteorological Institute (FMI) was downloaded. After scanning through the data to find cases, which were suspected of being convective origin, ERA5 reanalysis data developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) was downloaded from the locations and times of the gusts’ occurrence. Also chosen for further examination, for comparison purposes, were wind gust cases suspected of being caused by extratropical cyclones. The FMI wind gust speed and wind speed data was visualized in line charts, while the ERA5 data values of wind speed, equivalent potential temperature and relative humidity were tabulated and visualized in vertical cross sections. The visualization was done with the help of Python’s matplotlib.pyplot library and the MetPy toolbox. The results indicated that the differences between gust cases caused by convection and gust cases caused by extratropical cyclones can be clearly seen from the reanalysis data. As for the convective cases themselves, the data indicated that in several of them the gust could have been caused by the slow advection of strong upper-level winds to the surface on its own, in theory at least. However, in the majority of the cases the data indicated that the gust was likely the result of a strong downdraft or possibly by a combination of a downdraft and advection. Besides this, the values of the examined parameters and their visualization revealed that damaging straight-line winds can occur under various conditions in Finland.

In order to reliably quantify the impact of cities to the climate change, carbon dioxide (CO2) fluxes from urban areas need to be accurately estimated. Currently, eddy covariance (EC) measurements of net ecosystem exchange (NEE) are among the most used methods for CO2 balance assessment at ecosystem scale. They cannot, however, directly separate gross primary production (GPP) from different sources of CO2 , but instead different tracers and methods need to be used for the separation. In this thesis, urban carbonyl sulfide (COS) fluxes are reported and used as a tracer for biogenic CO2 uptake. For estimating GPP, a leaf-relative uptake (LRU) of CO2 and COS during photo- synthesis has to be calculated. Three different methods for ecosystem scale LRU estimations are compared in order to find which performs the best at assessing urban GPP from EC measurements. LRUNEE method is based on averaging LRU obtained directly from an equation using EC measurements of CO2 and COS. LRUP AR uses parameterisation of photosynthetically available radiation (PAR) to estimate LRU, and LRUCAP uses additional environmental parameters and information of soil water content (SWC), water vapour pressure deficit (VPD) and PAR. EC measurements were conducted at SMEAR III station (ICOS ecosystem associate site) in Helsinki during summer 2022 using a quantum cascade laser gas analyser. Mole fractions of COS, CO2, CO, and water vapour were collected with 10 Hz frequency, together with three-dimensional wind speed measurements. 30-min fluxes were calculated and used for estimating LRU and GPP with the three methods above. Source area of the station was divided into urban, street, and vegetation sectors. To compare different LRU methods, EC measured GPP from vegetation sector was used because it had the smallest anthropogenic emissions. CO and COS fluxes were compared to estimate co-emissions from fuel combustion. Median COS flux of −25.5 pmol m−2 s−1 was measured, indicating a biogenic CO2 uptake in the study area. For simplicity, soil related COS fluxes were neglected. LRUPAR performed the best at estimating GPP, giving an average CO2 uptake of 13.9 μmol m−2 s−1 for the vegetation sector. Furthermore, peak median anthropogenic emissions of 9.1 μmol m−2 s−1 were deduced with LRUPAR, which agrees with earlier research. However, due to problems with instrumentation the data collection period was so short that distinction between LRUPAR and LRUCAP was not unambiguous. Clear is that setting a constant value for LRU leads to significant underestimation of GPP, and a misunderstanding of its behaviour in heterogeneous urban landscape. Further development of COS based GPP estimation should include a more careful instrumentation and revision of the parameterisation of LRUCAP method.

In atmospheric sciences, measurements provided by remote-sensing instruments are crucial in observing the state of atmosphere. The associated uncertainties are important in nearly all data analyses. Random uncertainties reported by satellite instruments are typically estimated by inversion algorithms (ex-ante). They can be incomplete due to simplified or incomplete modelling of atmospheric processes used in the retrievals, and thus validating random uncertainties is important. However, such validation of uncertainties (or their estimates from statistical analysis afterwards, i.e. ex-post) is not a trivial task, because atmospheric measurements are obtained from the ever-changing atmosphere. This Thesis aims to explore the structure function method – an important approach in spatial statistics – and apply it to total ozone column measurements provided by the nadir-viewing satellite instrument TROPOMI. This method allows us to simultaneously perform validation of reported ex-ante random uncertainties and to explore of local-scale natural variability of atmospheric parameters. Two-dimensional structure functions of total ozone column have been evaluated based on spatial separations in latitudinal and longitudinal directions over selected months and latitude bands. Our results have indicated that the ex-post random uncertainties estimated agree considerably well with the reported ex-ante random uncertainties, which are within 1-2 DU. Discrepancies between them are very small in general. The morphology of ozone natural variability has also been illustrated: ozone variability is minimal in the tropics throughout the year, whereas in middle latitudes and polar regions they attain maxima in local spring and winter. In every scenario, the ozone structure functions are anisotropic with a stronger variability in the latitudinal direction, except at specific seasons in polar regions where isotropic behaviour is observed. Our analysis has demonstrated that the structure function method is a remarkable and promising tool for validating random uncertainties and exploring natural variability. It has a high potential for applications in other remote sensing measurements and atmospheric model data.

Numerical weather prediction models are the backbone of modern weather forecasting. They discretise and approximate the continuous multi-scale atmosphere into computable chunks. Thus, small-scale and complex processes must be parametrised rather than explicitly calculated. This introduces parameters estimated by empirical methods best fit the observed nature. However, the changes to the parameters are changing the properties of the model itself. This work quantifies the impact parameter optimisation has on ensemble forecasts. OpenEPS allows running automated ensemble forecasts in a scientific setting. Here, it uses the OpenIFS model at T255L91 resolution with a 20 min timestep to create 10-day forecasts, which are initialised every week in the period from 1.12.2016 to 30.11.2017. Four different experiments are devised to study the impact on the forecast. The experiments only differ in the parameter values supplied to OpenIFS, all other boundary conditions are held constant. The parameters for the experiments are obtained using the EPPES optimisation tool with different goals. The first experiment minimises the cost function by supplying knowledge regarding the ensemble initial perturbation. The second experiment takes a set of parameters with a worse cost function value. Experiments three and four replicate experiments one and two with the difference that the ensemble initial perturbations are not provided to EPPES. The quality of an ensemble forecast is quantified with a series of metrics. Root mean squared error, spread, and continuous ranked probability score are used with ERA5 reanalysis data as the reference, while the filter likelihood score is providing a direct comparison with observations. The results are summarised in comprehensive scorecards. This work shows that optimising parameters decreases the root mean square error and continuous ranked probability score of the ensemble forecast. However, if the initial perturbations are included in the optimisation the spread of the ensemble is strongly limited. It also could be shown that this effect is reversed if the parameters are tuned with a worse cost function. Nonetheless, when excluding the initial perturbations from the optimisation process, then a better model can be achieved without sacrificing the ensemble spread.

Accuracy and general performance of weather radar measurements are of great importance to society due to their use in quantitative precipitation estimation and its role on flood hazard risks prevention, agriculture or urban planning, among others. However, radars normally suffer from systematic errors such as attenuation, misscalibration in Z field or bias in Zdr field, or random errors such as clutter, beam blockage, noise, non-meteorological echoes or non-uniform beam filling, which affect directly the rain rate estimates or any other relevant product to meteorologists. Impact of random errors is reduced by exploiding the polarimetric properties of polarimetric radars by identifying and classifying measurements according to their signature and a classification scheme based on the available polarimetric variables, but systematic errors are more difficult to address as they require a ’’true’’ or reference value in order to be corrected. The reference value can either be absolute or obtained from another radar variable. In reality, an absolute reference value is not feasible because we normally do not know what we are observing with the radar. Therefore, a way of assesing this issue is by elaborating theoretical relations between radar variables based on their consistency when measuring a volume with hydrometeors of known characteristics such as size and concentration. This procedure is known as self-consistency theory and it is a powerful tool for checking radar measurements quality and correcting offsets causing bias, misscalibration or attenuation. The theoretical radar variables themselves can be simulated using available T-Matrix scattering algorithms, that estimate the scattered phase and amplitude for a given distribution of drops of a given size. Information of distribution of drops of a given size, commonly referred as drop size distributions, can be obtained, for instance, from gauge or disdrometer measurements. Once the theoretical relations among radar variables are established, it is possible to check the consistency of, for instance, measured differential reflectivity with respect to differential reflectivity calculated as function of measured reflectivity, assuming the latter has been filtered properly, and any discrepancy between the observed and theoretical differential reflectivity can be thus attributed to offsets in the radar. This work thus presents a methodology for the revision of radar measurements filtering and quality for their improvement by correcting bias and calibration, using theoretical relations between radar variables through self-consistency theory. Furthermore, as the aforementioned issues are easier to track and resolve in the liquid rain regime of precipitation, this work presents a detailed description of methodologies to exclude ice-phased hydrometeors such as the melting layer detection algorithm and its operational implementation along with other complementary filters suggested in the literature. Examples of the melting layer detection and filtering as well as self-consistency curves for radar measurement performance evaluation are also provided.

Tutkielmassa on selvitetty lumensyvyyden muutoksia ERA5-Land-uudelleenanalyysin antamille tuloksille ajanjaksolla 1950-2021. Datan analyysi ja käsittely on toteutettu Pythonilla. ERA5-Land:n etuihin lukeutuu muun muassa parempi erotuskyky kuin ERA5-uudelleenanalyysiin, mikä parantaa aineiston tarkkuutta huomattavasti. ERA5-Land ei suoraan käytä havaintoarvoja vaan lumensyvyys lasketaan muitten sääsuureitten, kuten lämpötilan ja sademäärän, aikasarjoja hyödyntäen. Näin ollen tutkielmassa käsiteltyihin suureisiin on sisällytetty muitakin suureita kuin vain lumensyvyys jotta syitä lumensyvyyden muutoksiin olisi helpompaa hahmottaa. Tutkielmaan valikoitiin kolme tutkimusaluetta; Suomi kokonaisuudessaan, Fennoskandian alueelta Norjan, Ruotsin sekä Suomen yhteinen pinta-ala, sekä Japanista Hokkaidon ja Honshun saarten muodostama maa-alue. Aineiston pohjalta voidaan todeta varsin yksiselitteisesti lumensyvyyden kuukausikeskiarvojen olevan, varsinkin lumensyvyyden huippuarvokuukausina, vertailukaudelle 1991-2020 pienempiä kuin vertailukaudelle 1951-1980. Sama trendi näkyy myös vuotuisten keskiarvojen kehityksessä. Muutosten suuruus on jonkin verran sidoksissa alueellisiin erityispiirteisiin mutta päätrendi on kaikille alueille jokseenkin samansuuntainen; nykyisentyyppinen ilmastollinen kehitystrendi laskee lumensyvyyttä pitkällä aikatähtäimellä. Toisin sanoen ilmastonmuutos vähentää lumimäärää pohjoisella pallonpuoliskolla.

The mean temperature of Earth has been rising due to human-influenced climate change. Climate change has been mostly caused by the rise of greenhouse gases from anthropogenic sources. After carbon dioxide (CO2), the second most important anthropogenic greenhouse gas to climate change is methane (CH4). Approximately half of the methane emissions come from natural sources, including wetlands. The northern high latitude wetlands store large amounts of carbon in permafrost, and the thawing of permafrost could release more methane into the atmosphere. However, there is still much uncertainty related to the methane emissions from the northern high latitude wetlands. The emissions on these wetlands have an annual cycle related to the freezing and thawing of the soil with the highest emissions during summer and the lowest during winter. Climate change can affect the duration and timing of the freezing and thawing periods leaving the winter period shorter. In this thesis, the melting season for the northern high latitude wetlands was defined for four regions: non-permafrost, sporadic, discontinuous and continuous permafrost as well as two smaller regions: Hudson Bay lowlands and Western Siberian lowlands for the years 2011-2020. The melting period was defined with a new method of using the SMOS F/T soil thawing data, which has not been done before this study. The data includes daily information on the freezing state of the soil in the northern latitudes. The melting period methane emissions were defined from the inversion model Carbon Tracker Europe -CH4. The relationship between the emissions, melting period length and mean temperature was studied. Emissions during the spring melting season were detected in all the permafrost regions defined in this study. The fluxes grew stronger as spring progressed and the soil and snow melted. The melting period methane emissions were relatively small compared to the annual emissions (a few per cent of the annual budget). However, the emissions were a little larger than autumn emissions. To understand the melting season emissions better, different drivers in addition to air temperature, like the melting of the permafrost, should be studied in relation to the CH4 emissions.