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.

Cumulonimbus (Cb) clouds form a serious threat to aviation as they can produce severe weather hazards. Therefore, it is important to detect Cb clouds as well as possible. Finnish Meteorological Institute (FMI) provides aeronautical meteorological services in Finland, including METeorological Aerodrome Report (METAR). METAR describes weather at the aerodrome and its vicinity. Significant weather is reported in METARs, and therefore Cb clouds must be included in it. At Helsinki-Vantaa METARs are done manually by human observer. Sometimes Cb detection can be more difficult, for example, when it is dark, and it is also expensive to have human observers working around the clock all year round. Therefore, automation of Cb detection is a topical matter. FMI is applying an algorithm that uses weather radar observations to detect Cb clouds. This thesis studies how well the algorithm can detect Cb clouds compared to manual observations. The dataset used in this thesis contains summer months (June, July and August) from 2016 to 2020. Various verification scores can be calculated to analyse the results. In addition, daytime and night-time differences are calculated as well as different years and months are compared together. The results show that the algorithm is not adequate to replace human observers at Helsinki-Vantaa. However, the algorithm could be improved, for instance, by adding satellite observations to improve detection accuracy.

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.

Earth’s energy budget describes the balance between the net incoming and outgoing energy fluxes, and the energy balance approach can be used to better understand the basic physical mechanisms of climate change. Anthropogenic changes in the atmospheric composition, such as increases in greenhouse gases, drive changes in climate system which in turn can cause rising of the global temperatures. Various feedbacks, associated with increase in atmospheric water vapor content, changes in clouds and reduced snow/ice cover, affect the pattern of surface warming by altering the fluxes of energy. By studying the energy balance at the top of the atmosphere and at the surface, we gain useful information about the climate system’s response to changes in the atmospheric composition. In this thesis, data for 23 climate models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) was used. The present-day distributions and future projections of the simulated changes (under RCP8.5 emission scenario, Representative Concentration Pathway) for 14 radiative and non-radiative energy budget components, along with the changes in surface temperature and cloud cover were studied, with baseline period of 1981-2010 and a comparison scenario period of 2071-2100. The geographical distributions of the multimodel mean changes and their global averages were analysed. Additionally, the intermodel consistency of the simulated changes was studied with the intermodel standard deviations and the ratio of multimodel mean change to the intermodel standard deviation. Furthermore, the intermodel correlation between the change in surface temperature and each energy budget variable was discussed. A general finding was that the multimodel mean surface temperature increases everywhere, more over land than oceans, and that the warming is amplified over the northern polar regions. The changes were largest for the thermal radiation fluxes, and the dominating contribution to the surface warming was concluded to be the change in clear- sky atmospheric re-radiation component. However, increase in absorbed shortwave radiation, presumably due to reduced ice/snow cover and increase in atmospheric water vapor content, was also found to be substantial, and there was a strong negative correlation between the clear-sky downward shortwave radiation flux and the change in temperature over the low-to-mid latitudes. The comparison of contribution of the changes in longwave and shortwave fluxes to global warming in the near-future and long-term climate model projections could be an interesting subject for future studies. Additionally, the changes in the surface energy fluxes were found to modify the pattern of surface warming.

This thesis analyses the alterations of vertically integrated atmospheric meridional energy transport due to polar amplification on an aqua planet. We analyse the energy transport of sensible heat, latent energy, potential energy and kinetic energy. We also cover the energy flux of the mean meridional circulation, transient eddies and stationary eddies. In addition, we also address the response of the zonal mean air temperature, zonal mean zonal wind, zonal mean meridional wind, zonal mean stream function and zonal mean specific humidity. Numerical model experiments were carried out with OpenIFS in its aqua planet configuration. A control (CTRL) and a polar amplification (PA) simulation was set up forced by different SST (sea surface temperature) patterns. We detected tropospheric warming and atmospheric specific humidity increase 15-90° N/S and reduction of the meridional temperature gradient throughout the troposphere. We also found reduced strength of the subtropical jet stream and slowdown of the mean meridional circulation. Important changes were identified in the Hadley cell: the rising branch shifted poleward and caused reduced lifting in equatorial areas. Regarding the total atmospheric vertically integrated meridional energy transport, we found reduction in case of the mean meridional circulation and transient eddies in all latitudes. The largest reduction was shown by the Hadley cell transport (-15%) and by midlatitude transient eddy flux (-23%). Unlike most studies, we did not observe that meridional latent energy transport increases by polar amplification. Therefore, it is stated that the increased moisture content of the atmosphere does not imply increased meridional latent energy transport, and hence there is no compensation for the decrease of meridional dry static energy transport. Lastly, we did not detect stationary eddies in our simulations which is caused by the simplified surface boundary (i.e. the water-covered Earth surface). The main finding of this thesis is that polar amplification causes decreasing poleward energy transport on an aqua planet.

A stably stratified layer is often observed to form near the surface during nighttime. If the terrain is not flat, cold air near the surface can start to flow down the slope despite the wind direction above the stable layer being different. This slope flow is called katabatic wind. Katabatic winds are challenging for eddy covariance measurements that are commonly used to calculate the fluxes of gases and energy between the soil and the atmosphere. If the measurement is done above the canopy, the katabatic wind may lead to significant advective transport of gas and energy not detected by the measurement. Therefore eddy covariance measurements might underestimate the fluxes by a significant amount. Therefore it is important to understand the mechanisms of katabatic flow so that the effects of it can be taken into account when eddy covariance measurements are used in sloping terrain. This thesis determines how the katabatic flow within a boreal forest canopy in Hyytiälä, Finland depends on the static stability and depth of the stable layer within the canopy. The measurements are also compared to a simplified theory to find out how well the existing formulations of katabatic flow within canopies describe the observed conditions. Wind measurements done with sonic anemometers and temperature measurements done with distributed temperature sensing system during June-October 2019 are analyzed in this thesis to form understanding of the vertical profiles of temperature and wind within the canopy layer at the measurement site. In addition to the wind and temperature measurements, solar radiation measurements are also used to find the dominant driver for the formation of the stably stratified canopy layer. The measurement site represents typical Finnish Scots pine forest and has sloping terrain with main slope of 2° in the north-south direction. This study found evidence of katabatic northerly flow forming at the measurement site during stable nights. However, this study could not find a relation between the depth of the stable layer and strength of the katabatic flow. The katabatic flow was observed to get stronger in the open trunk space with increasing static stability within the canopy layer. The results of this study suggest, that katabatic flow follows the simplified theory well within the upper part of the canopy, where the majority of the foliage is. At lower levels within the open trunk space, the simplified model greatly underestimates the flow speed.