Browsing by study line "Meteorologia"

Clouds and aerosols are among the key components of Earth's energy budget, and a major source of uncertainty in climate models, affecting the predictability of the future climate. This thesis focuses on the microphysical processes governing cirrus clouds, wispy clouds composed of ice crystals. Understanding these processes is crucial due to the extensive global coverage of cirrus clouds and their potential warming effect on the atmosphere. The study investigates ice nucleation, the process by which ice crystals form in the atmosphere. Ice nucleation occurs via two main pathways: homogeneous freezing and heterogeneous nucleation. Homogeneous freezing is a process where droplets spontaneously freeze without an aid of an ice nucleating particle (INP). It occurs in highly supersaturated conditions and at cold temperatures below -38°C. Heterogeneous nucleation occurs when INPs act as surfaces to trigger freezing at temperatures below 0°C. The study is conducted using UCLALES-SALSA Large Eddy Simulation (LES) model, which offers high spatial and temporal resolution for atmospheric simulation. The aim is to investigate ice nucleation with five well-established parameterizations. Simulations produced with these parameterizations are compared with cirrus cloud properties measured during the MACPEX campaign. Among heterogeneous nucleation mechanisms, deposition ice nucleation is considered as a primary contributor to the formation of cirrus clouds in the upper troposphere and used as a mechanism to generate ice in the model study. Heterogeneous nucleation requires the presence of INPs which are assumed to be mineral dust is used as it known to dominate ice nucleation. Results show good agreement between modeled and measured data for ice concentration (Ni) and ice water content (IWC). The comparison between parameterizations revealed a relatively similar performance, with variations in Ni and IWC falling within the same order of magnitude. However, conclusive determination of the best-performing parameterization within the temperature and humidity ranges of the study was challenging. The study sheds light on the fundamental difficulties when using parameterizations with ice nucleation processes in cirrus clouds without accurate initial conditions and knowledge about the history of ice nucleation of the measured cirrus clouds. Also, the importance of proper validation of each parameterization by using different scenarios was emphasized.

The aim of this work is to develop and optimise an atmospheric inverse modelling system to estimate local methane (CH4) emissions in peatlands. Peatlands are a major source of CH4 regionally in boreal areas and they have significance on a global scale as a soil carbon storage. Data assimilation in the inverse modelling system is based on an ensemble Kalman filter (EnKF) which is widely used in global and regional atmospheric inverse models. The EnKF in this study is an implementation of the EnKF used in the global atmospheric inversion model CarbonTracker Europe-CH4 (CTE-CH4) applied to local setting in the peatland. Consistency of the methodology with regional and global models means that it is possible to expand the system in scale. Siikaneva fen in Southern Finland is used as a testbed for the optimisation of the system. Prior natural CH4 fluxes in Siikaneva are acquired from the HelsinkI Model of MEthane buiLd-up and emIssion for peatland (HIMMELI) which simulates exchange of gases in peatlands. In addition to the peatland fluxes, anthropogenic fluxes at the site are estimated as well in the inversion. For the assimilation of atmospheric CH4 concentration observations, the CH4 fluxes are transformed into atmospheric concentration with a simple one-dimensional box model. The optimisation of the system was done by changing parameters in the model which affect the data assimilation. In model optimisation tests it was discovered that the performance of the modelling system is unstable. There was large variability in the produced estimates between consecutive model runs. Model evaluation statistics did not indicate improvement of the estimates after the inversion. No exact reason for the unstability was able to be determined. Posterior estimates of CH4 fluxes for years 2012–2015 did not differ much from prior estimates and they had large uncertainty. However, evaluation against flux measurements showed reasonable agreement and posterior concentration estimates were within the uncertainty range of the observed concentration.

Local mountain winds have a diurnal cycle of flowing up the slopes and valleys daytime and down nighttime. It is important to improve the understanding on these thermally driven winds, because they have a major role in pollution transport in mountain areas, which are highly sensitive for air-quality problems. This thesis determines if the slope and valley winds in the Khumbu valley, Himalayas, are driven by the textbook mechanisms. By the textbook mechanisms the slope and valley winds are driven by horizontal temperature differences caused by uneven heating over an area of complex terrain. Slope winds are driven by the horizontal air temperature difference in the slope surface vicinity when the slope surface is heated or cooled. Valley winds are driven by the uneven heating caused by the air volume difference between the valley and above an adjacent plain. If the valley slopes narrow and the floor is elevated towards the head of the valley, both the valley and slope wind mechanisms drive the winds along the valley. The slope and valley winds in the Himalayas are studied using Weather Research and Forecasting model (WRF), that is run for 5 days period in December 2014 with 1 kilometer horizontal grid spacing and 61 vertical levels. Earlier studies have shown that WRF is capable of simulating the thermally driven mountain winds on this resolution with the length scales of the Khumbu valley topography. Horizontal gradient of air temperature and slope wind component at the slope surface have a matching daily cycle in the lower and middle parts of the valley. The boundary layer air volume decreases from the mouth of the valley towards the middle parts of the valley indicating the valley wind mechanism. The daytime potential temperature profiles yield that also the slope wind mechanism drives the winds along the valley. The slope winds have a textbook daily cycle in the lower and middle parts of the valley and the analysis yields that they are driven by the slope wind mechanism. In the upper part of the valley the thermally driven slope winds are dominated by synoptic scale channelling and gravity wave developing into the valley. The daytime up-valley winds are driven by both valley wind mechanism and slope wind mechanism due to the valley narrowing and elevation towards the head of the valley, respectively. Nocturnal along-valley winds are weak less than 0.5 meters per second flowing up or down-valley. The wind patterns are similar to what is shown in earlier studies done in Khumbu valley.

Fog has a significant impact on society, by making transportation and aviation industries difficult to operate as planned due to reduced visibility. Studies have estimated that 32 % of marine accidents, worldwide, and 40 %, in the Atlantic Ocean, took place during dense sea fog. Therefore forecasting fog accurately, and allowing society to function, would help mitigate financial losses associated with possible accidents and delays. However, forecasting the complex fog with numerical weather prediction (NWP) models remains difficult for the modelling community. A NWP model typically operates in the resolution of kilometres, when the multiple processes associated with fog (turbulence, cloud droplet microphysics, thermal inversion) have a smaller spatial scale than that. Consequently, some processes need to be simplified and parametrised, increasing the uncertainty, or more computational power is needed to be allocated for them. One of these NWP models is HARMONIE-AROME, which the Finnish Meteorological Institute develops in collaboration with its European colleague institutes. To improve the associated accuracy, a brand new, more complex and expensive, option for processing aerosols in HARMONIE-AROME, is presented. This near-real-time (NRT) aerosol option integrates aerosol concentrations from Copernicus Atmospheric Monitoring Services' NRT forecast into HARMONIE-AROME. The statistical performance of the model's sea fog forecast in the Baltic Sea was studied in a case study using marine observations. The quantitative metric, proportion score, was studied. As a result, a forecast using the NRT option showed a slight deterioration in visibility (0.52 versus 0.59), a neutral improvement in cloud base height (0.52 versus 0.51), and a slight deterioration in 2-meter relative humidity (0.73 versus 0.76) forecasts with respect to the reference option. Furthermore, the score in general remained weak against observations in the case of visibility and cloud base height. In addition, based on qualitative analysis, the spatial coverage of the forecasted sea fog in both experiments was similar to the one observed by the NWCSAF Cloud Type-product. In total, the new aerosol option showed neutral or slightly worse model predictability. However, no strong conclusions should be made from this single experiment sample and more evaluations should be carried out.

This thesis investigates how the Lorenz model state sensitivity appears on the prior state error of the Extended Kalman Filter (EKF) process. The Lorenz model is a well-known ordinary differential equation system. Its simple nonlinear equations show that a chaotic system, like the atmosphere, does not have a single deterministic solution. Edward N. Lorenz also showed that the predictability of the state depends on the flow itself, and numerical weather prediction models, therefore, cannot always be trusted equally. For this reason, when computing a forecast, it is necessary to consider both the model and observations with their weight uncertainties to get the most probabilistic analysis state. The EKF is an algorithm that provides a powerful data assimilation method for nonlinear systems. Its operating principle is based on the evolution of prior state (model evolution) and observation updates. Each observation update calculates the most likely state based on the prior state and observation errors. The process continues from the new analysis state by evolving the model until the next observation update. In this study, I made the EKF utilizing the Lorenz model and sent ensembles from the analysis states on the Lorenz attractor. I calculated the variance of evolved ensembles and compared them to the magnitude of prior state error at the observation update time levels. The results showed that these two parameters are positively correlated. For the 18-timestep observation interval, Pearson’s correlation coefficient was 0.850, which indicates a very high correlation. Therefore, it can be concluded that when the prior state error is small, the ensemble on the Lorenz attractor indicates good predictability (i.e., dispersion of ensemble members is small) and vice versa.

Global warming is rapidly reducing the Arctic sea ice cover. Along with its major impacts in the Arctic, the sea ice loss is known to affect the climate in remote continental areas. In this thesis, the remote effects are studied by analysing data from experiments carried out using the ECHAM6 atmospheric general circulation model forced with simple sea ice concentration and sea surface temperature configurations. The European and Asian midlatitude winter responses of surface air temperature are investigated, together with anomalies of variables potentially affecting them: sea level pressure, thermal advection, adiabatic and diabatic heating and surface energy fluxes. Arctic sea ice loss was found to have contributed weakly to the European warming from the 1980s to the 2010s. With sea ice and sea surface temperature conditions projected for 2071–2100, the warming response was about 1 °C relative to the 1979–2008 climatology, despite a negative North Atlantic Oscillation phase response. In Asia, the model simulates slight cooling of about 0.5 °C east of the Urals in the past and in the eastern parts of the continent in the future. However, the cooling responses are overwhelmed by the warming effect of global forcings. The effects of internal variability are large, and the role of the thermodynamic processes and surface energy fluxes in the link between the Arctic and Eurasia is not very clear. However, the temperature responses are mostly consistent with previous research, even though the model does not capture the observed past changes well.

TROPOMI eli TROPOspheric Monitoring Instrument on lokakuussa 2017 Sentinel-5 Precursor -satelliitin mukana laukaistu spektrometri, joka mittaa useiden eri ilmakehän hivenkaasujen pitoisuuksia, aerosoleja sekä pilviä. Se on samalla myös uusin ja resoluutioltaan tarkin typpidioksidin (NO2) pitoisuuksia mittaava satelliitti-instrumentti. NO2:ta havainnoivien satelliitti-instrumenttien mittaukset perustuvat ilmakehästä siroavaan auringon valoon, minkä perusteella niiden algoritmit laskevat ilmakehässä olevien NO2-molekyylien lukumäärän käyttäen apuna erilaisia syötetietoja. Saadussa tuloksessa on tämän vuoksi paljon erilaisia virhelähteitä, minkä vuoksi satelliitti-instrumenttien mittausten oikeellisuutta seurataan jatkuvasti vertaamalla niitä erilaisiin referenssiaineistoihin. Tällaista seurantaa kutsutaan myös instrumentin validoinniksi, ja se on erityisen tärkeää uusien instrumenttien kuten TROPOMIn tapauksessa. Tässä työssä validoidaan TROPOMIn NO2-mittaukset käyttäen Helsingin Kumpulassa sijaitsevan Pandora-referenssi-instrumentin mittauksia. Tämän lisäksi TROPOMIn herkkyyttä lähellä maanpintaa tapahtuville pitoisuusvaihteluille arvioidaan vertaamalla sen mittauksia Kumpulassa sijaitsevan in situ -ilmanlaatuaseman mittauksiin. Lopuksi arvioidaan TROPOMIn ja Pandoran mittausten ja niiden välisen vastaavuuden riippuvuutta rajakerroksen paksuudesta ja siellä vallitsevasta tuulesta. Tutkimus ajoittuu 19.4.–29.9.2018 väliselle ajalle. Vertailuissa tarkastellaan erityisesti instrumenttien mittausten välisiä eroja (TROPOMI – Pandora) ja niiden keskiarvoa, erojen suhteellisia arvoja (suhteessa Pandoraan) ja niiden mediaania, sekä mittausten välistä Pearsonin korrelaatiokerrointa. Näiden tunnuslukujen riippuvuutta ajasta tarkastellaan eripituisia aikavälejä kattavien aikasarjojen avulla. Tulosten mukaan TROPOMIn ja Pandoran mittausten välinen Pearsonin korrelaatiokerroin on 0,66 ja niiden välisten suhteellisten erojen mediaani 12,1 %. Tätä voidaan pitää hyvänä tuloksena, sillä TROPOMIlle asetettu suhteellisten erojen tavoite on enintään 30 %. Positiivinen arvo on kuitenkin epätyypillinen kaupungissa tehtävälle validoinnille, mikä voi tarkoittaa Kumpulan alueen edustavan pitoisuuksiltaan enemmän tausta-aluetta kuin tyypillistä kaupunkiympäristöä. Mittausten välisen korrelaation havaittiin riippuvan rajakerroksen paksuudesta, mikä voi johtua TROPOMIn tulkinta-algoritmin käyttämästä NO2:n pystyprofiilista tai paksussa rajakerroksessa tapahtuvasta voimakkaammasta sekoittumisesta. Asian selvittäminen edellyttää kuitenkin lisätutkimuksia. Lopuksi TROPOMIn todettiin olevan herkkä viikon- ja päivänsisäisille pitoisuusvaihteluille Pandora-instrumenttiin verrattuna, mikä on lupaava tulos TROPOMIn mahdollisten ilmanlaadun seurantaan liittyvien sovellusten kannalta. TROPOMIn parantuneen resoluution vaikutus on tutkimuksessa nähtävissä aiempiin instrumentteihin verrattuna parantuneena korrelaationa ja positiivisempina mittauseroina, sekä herkkyytenä päivänsisäisille pitoisuusvaihteluille. TROPOMIn voidaankin odottaa tulevaisuudessa lisäävän satelliittipohjaisten NO2-mittausten käyttökohteita.

In this thesis we try to find the measurement accuracy of our dronebound wind measurement setup and if the quality of the measurements is high enough for operational usage. The thesis goes over the most important theoretical concepts concerning effects of wind in the boundary layer. In the thesis we analyze wind data gathered by a drone-bound anemometer, and introduce a direct method of measuring wind with a UAV. The data includes stationary wind data gathered at height of 30 metres, as well as vertical wind profiles to 500 metres above ground level. The data is compared to reference data from a 30 metre wind mast and automatic radiosoundings. The measurements were conducted in Jokioinen, Finland between the 2nd of September 2022 and 10th of October 2022. Total of 20 measurement flights were conducted, consisting of 14 stationary wind measurements and six wind profile measurements. We found out the stationary wind measurement quality to be comparable with earlier studies. The vertical wind profile measurements were found to be hard to analyze, as the reference measurement was not as compatible as we had hoped for. The difference between automatic radiosoundings and our profile measurements was distinctly greater than the difference between the stationary drone and wind mast measurements. Lastly some optimization and improvements to the measurement arrangement are discussed. The application of these improvements and modifications will be left as future endeavour for some willing individual.

Tutkielmassani tutkin Suomessa esiintyneiden voimakkaiden keski- ja puuskatuulien suunta- ja vuodenaikajakaumia vuosilta 2010-2019. Työssä käyttämäni tuulihavainnot keräsin 19 sääasemalta Suomesta, joista yhdeksän oli sisämaa-asemia, kahdeksan oli meri- tai rannikkoasemia ja kaksi oli tunturiasemia. Työni tavoitteena oli selvittää, mistä suunnasta ja milloin voimakkaimmat tuulet esiintyvät sekä onko voimakkailla tuulilla alueellisia yhtäläisyyksiä suunta- ja vuodenaikajakaumissa. Työssä tutkin aluksi jokaisen aseman tuulihavaintojen voimakkainta kahta prosenttia eli noin 1500-1750 havaintoa. Vuodenaikoja esitellessä käytin myös 100 ja 30 voimakastuulisinta päivää 10 vuoden havaintojaksolta, jotta pystyin vertailemaan vuodenaikajakaumia kaikista voimakkaimmissa tuulissa. Kuukausijakaumissa käytin 100 voimakastuulisinta päivää. Lopuksi vertailin suuntia eri vuodenaikoina, jolloin rajasin jokaisen aseman kunkin vuodenajan tuulihavainnot voimakkaimpaan prosenttin. Meri- ja rannikkoasemilla havaitsin eniten voimakkaita keskituulia syksyllä ja talvella, sisämaassa loppusyksystä ja alkukeväällä sekä Pohjois-Suomessa puolestaan talvella ja alkukeväällä. Nämä voimakkaat keskituulet aiheutuvat talven voimakkaista matalapaineista sekä keväällä voimistuvasta turbulenssista. Sisämaa-asemilla havaitsin myös kesäkuussa voimakkaita keskituulia, joiden aiheuttajana on saattanut olla edellä mainittujen sijasta mesoskaalan konvektiivinen ilmiö. Voimakkaiden tuulten suuntajakaumissa eniten tuulia puhalsi etelän ja lännen väliltä. Sisämaassa kaikkein voimakkaimmat tuulet puhalsivat siitä suunnasta, mistä puhalsi suurin osa kaikista voimakkaista tuulista. Meri- ja rannikkoasemilla kaikkein voimakkaimmat keski- ja puuskatuulet tulivat luoteesta ja idästä.

Ilmakehän aerosolihiukkasilla on vaikutuksia maapallon säähän ja ilmastoon ja siksi niiden syntyä ja toimintaa pyritään tuntemaan yhä paremmin. Jos niiden rakenneosaset tunnetaan hyvin, voidaan myöskin aerosolihiukkasten ominaisuudet oppia tuntemaan paremmin. Mallinnusten ohella kokeelliset mittaukset ovat yksi keino saada lisää tietoa ilmakehän pienten rakenneosasten toiminnasta. Tässä työssä tutkin voidaanko differentiaalisella liikkuvuusanalysaattorilla (differential mobility analyzer, DMA) mitata α-pineenin hapetustuotteiden eri rakenteita. α-pineeni ja otsoni reagoivat virtausputkessa ja reaktioissa muodostuneet hapetustuotteet varattiin klusteroimalla ne elektrospraylla tuotettujen varattujen reagenssi-ionien kanssa. Näin syntyneiden varattujen klustereiden liikkuvuus ja massa-varaus-suhde mitattiin. Analysointiprosessissa käytin Matlab-pohjaista ToFTools-ohjelmaa ja INAR:in postdoc Lauri Ahosen tekemää Flat-DMA-analysointiohjelmaa. Tunnistin ToFToolsilla α-pineenin hapetustuotteista ja reagenssi-ionista koostuvien klustereiden kemiallisen koostumuksen niiden massan perusteella ja sovitin niiden liikkuvuusspektrin piikkien sisään Flat-DMA-analysointiohjelmalla piikkejä DMA:n maksimiresoluutiolla. Sovitettujen piikkien määrä kertoo siitä, kuinka monta rakennetta kullakin klusterilla mahdollisesti voisi olla. Mittauksissa havaittiin useita yhdisteitä, joita voitiin teorian perusteella olettaa syntyvän. Kuitenkin tunnistettiin myös sellaisia yhdisteitä, joita ei odotettu muodostuvan α-pineenin ja otsonin välisissä reaktioissa. On mahdollista, että yhdisteet on tunnistettu väärin tai mittauksiin on päässyt epäpuhtauksia. Näiden mittausten perusteella ei voida vielä varsinaisesti tehdä johtopäätöksiä siitä, miten monta isomeeria α-pineenin hapetustuotteilla on. Tulosten perusteella käytetty reagenssi-ioni vaikuttaa merkittävästi mitattujen rakenteiden määrään, joten jotta α-pineenin hapetustuotteiden rakenteita voitaisiin tutkia, täytyisi kunkin reagenssi-ionin vaikutus tuntea paremmin. Myöskin jotta rakenteiden määrää voitaisiin tutkia luotettavammin, täytyisi DMA:n resoluution olla huomattavasti nykyistä parempi. Nykyisellä resoluutiolla isomeerien määrän arviointi oli hankalaa eikä lopputuloksista voida olla varmoja.