Browsing by master's degree program "Ilmakehätieteiden maisteriohjelma (Atmospheric Sciences)"

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.

The parameterization of deep convection is simulated poorly over the Central and East Pacific. This could lead to issues in predicting the annual total precipitation in the tropics, such as the existence of a double intertropical convergence zone over the equatorial Pacific. Resolving tropical deep convection instead of parameterization leads to the presence of more linear systems. Observations over Atlantic indicate that shear-perpendicular lines (squall lines) propagate faster than shear- parallel lines, mainly due to their connection with the low-level vertical wind shear (VWS). The study examines the different movement speeds of mesoscale convective systems (MCSs) over the East- and West Pacific to determine whether this could explain the reason why climate models have problems with predicting deep convection. A higher proportion of fast moving MCSs (squall lines) could contribute to the prediction problems in the tropics. The MCS motion is determined by the sum of the mean wind and propagation speed. In squall lines, the MCS motion is mainly influenced by the propagation, which is associated with the low-level VWS. Therefore, the effect of the low-level VWS on the fast- and slow moving MCSs is also investigated. The Global High-Resolution Mesoscale Convective System Database is used, which provides infor- mation about the time, location and movement of MCSs. Additionally, ERA5 wind data is used to obtain the mean wind and VWS. Two specific areas over the northern equatorial Pacific are chosen to compare the different types of MCSs. These areas are over the East Pacific (120°W - 140°W and 5°N - 12°N) and West Pacific (140°E - 160°E and 0°N - 7°N). The movement speed is used to categorize the MCSs into three groups: slow moving MCSs (< 3 m/s), moderate moving MCSs (3 m s−1 − 7 m s−1) and fast moving MCSs (> 7 m/s). The study reveals that the share of the fast moving MCSs is 9.8% over the East Pacific and 13.8% over the West Pacific. This is only a 4 percent point difference between the two areas. Therefore, it is not shown that the fast moving MCSs contribute to the existing issues that models have in predicting the annual total precipitation over the East Pacific. Moreover, approximately 85-90% are categorized as slow- to moderate moving MCSs. Hence, the influence of fast moving MCSs is relatively small when compared to the other types. A difference is seen in the mean wind and VWS over the East Pacific, but do not explain the MCS motion vector. Therefore, the difference between fast- and slow moving MCSs cannot be explained by only the monthly averaged mean wind and low-level VWS over the East Pacific. Over the West Pacific, the mean wind direction and VWS are about the same in direction and speed. Therefore, the difference between fast- and slow moving MCSs is not explained by the low-level VWS over the West Pacific.

The Differential Mobility Particle Sizer (DMPS) is a widely used instrument in the size distribution measurements of sub-micron aerosol particles. The particles are size classified based on their electrical equivalent diameter by a Differential Mobility Analyser (DMA) in the DMPS. Only charged particles can be measured with a DMPS. An aerosol charger is hence required since most ambient particles are neutral. An estimation of the size dependent charge distribution of the aerosol particles is required to reach a representative size distribution of the whole aerosol population from the raw measurement data. The charged fractions of the population are conventionally calculated by applying bipolar charging (i.e., an ion atmosphere including ions of both polarities charges aerosols by diffusion and ion attachment onto the particles) theories. Fixed charger ion properties have been used to derive approximation to these theories. The charger ion properties in ambient “real-world” measurements are, however, not fixed but proven to vary substantially. These variations may lead to significant differences when comparing to the approximations. A new method for aerosol charging that reduces the uncertainties that originate from unknown properties of the charger ions was tested in this thesis. The method consists of the introduction of a known trace compound (hereafter called doping) into the aerosol sample flow upstream of a bipolar charger. The effect of doping on charger ion mobility distribution was successfully tested in laboratory experiments. A possible enhancement of charging efficiencies of nanoparticles was studied. The charger ion doping was also tested in atmospheric measurements, where it showed an effect on charger ion properties.

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.

Thawing of permafrost is widely observed, and its rate is expected to be accelerated due to the global warming caused by anthropogenic climate change. Although permafrost thawing has been acknowledged in IPCC Assessment reports, uncertainties related to model-based estimates of its extent and magnitude in the future exist due to the challenges for the models to account for heterogeneous changes in permafrost under the changing climate. Various one-dimensional finite element conductive heat transfer model codes have been successfully used for simulating permafrost, while models created with the COMSOL multiphysics tool have seen little use. In this work, COMSOL version 5.6 was chosen for modelling the heat transfer in permafrost. COMSOLs' ability to accurately simulate thermal evolution in porous medium experiencing freezing was demonstrated using the Interfrost test case T1, which is a benchmark modelling problem adapted to use by the Intercomparison project for TH (Thermo-Hydro) coupled heat and water transfers in permafrost regions. Benchmark results agreed with Lunardini's analytical solution, although compared to the previous studies, the results had more deviation from the analytical solution. Discontinuous permafrost in North-Western Siberia is thawing. Based on the temperature measurements available from three boreholes located in the area (Nadym), and an observed increasing mean annual air temperature trend of 0.5\textdegree C per decade, the rate of thawing could be increasing. A one-dimensional heat transfer model for one of the boreholes was created and benchmarked against soil temperature measurements to form a basis for future estimates of the permafrost evolution. The temperature time series produced by the model agreed moderately with the measurements, but the need for further model improvements was identified. Adjustments proposed in this work and parameter changes indicated by the sensitivity analysis form a basis for further model development. Additionally, the results of the conducted sensitivity analysis showed the importance of using accurate soil properties in modelling works.

The effects of atmospheric aerosol particles on Earth’s radiative balance are mainly cooling, which is mostly due their indirect effects with clouds. In the Arctic more than half of the cloud condensation nuclei (CCN) production is originated from secondary aerosols, and to further the understanding of Arctic climate and its changes due to the global warming, it is necessary to better understand the secondary aerosol processes there. Highly oxygenated organic molecules (HOM) are known to be important for the formation and especially for the growth of newly formed secondary aerosols to climate relevant sizes. Because of the low volatilities of HOM, they can condense onto the smallest particles, which is crucial for the growth of the new particles. Volatile organic compounds (VOC), especially monoterpenes, are known to be sources of HOM in boreal forest, but in the Arctic where the vegetation is scarce the sources of HOM have not yet been identified. The processes related to secondary aerosol formation in the Arctic are still not fully understood. Especially the observations of HOM and their sources are lacking. Recent studies in Ny-Ålesund, Svalbard showed that multiple aerosol precursors are found to be present in the Arctic atmosphere, as well as contributing to the early stages of the formation of secondary particles. However, more molecular scale observations of aerosol precursors are still needed to form a full picture of the Arctic climate processes. In this thesis, the different aerosol precursors and their contributions to the new particle formations in high Arctic location Ny-Ålesund, Svalbard were analysed. Chemical compositions of HOM were identified for the first time from Arctic atmosphere, and their contributions to new particle formation in high Arctic location were investigated. Because of the high concentrations of HOM during the observed NPF events, it can be suggested that they were contributing to the nucleation of aerosol particles. Particle growth rate calculation shows that the HOM present in the study site were responsible for up to 50% of the total growth of the newly formed particles. VOC flux measurements done in same location were also analysed, and Arctic tundra in Svalbard was found out to be a source of at least four different VOC. Furthermore, the identified HOM were linked to the VOC flux measurements, suggesting a possible link between Arctic VOC and HOM.

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.

Norway spruce (Picea abies (L.) Karst.) is one of the economically most important tree species in Finland. It is known to be drought-sensitive species and expected to suffer from the warming climate. In addition, warmer temperatures benefit pest insect Eurasian spruce bark beetle (Ips typographus L.) and pathogen Heterobasidion parviporum, which both use Norway spruce as their host and can make the future of Norway spuce in Finland even more difficult. In this thesis, adult Norway spruce mortality was studied from false colour aerial photographs taken in years between 2010 and 2021. Dead trees were detected from the photos by visual inspection, and mortality was calculated based on the difference in the number of dead trees in the photos from different years. The aim was to find out if Norway spruce mortality in Finland had increased over time, and what were the factors that had been driving tree mortality. The results indicate that tree mortality was the highest in the last third of the studied 10-year period, so it was concluded that tree mortality had increased over time. Various possible tree mortality drivers were analysed and found to be connected to tree mortality. Each driver was analysed individually by testing correlation with tree mortality. In addition, linear regression analysis and segmented linear regression with one breakpoint were used with the continuous variables. Increased tree mortality correlated with higher stand mean age, mean height, mean diamater, and mean volume, supporting the findings in earlier research. Mortality was connected to the proportion of different tree species in the stand: the higher the proportion of spruce, the higher the mortality, and the higher the proportion of deciduous trees, the lower the mortality. Of different fertility classes, tree mortality was the highest in the second most fertile class, herb-rich heat forest, and mortality decreased with decreasing fertility. Dead trees were also found to be located closer to stand edges than the stand centroid. Increased temperature resulted in increased mortality. Increased vapour pressure deficit (VPD) and drought, which was analysed with Standardized Precipitation Evapotranspiration Index (SPEI) of different time scales, were also connected with increased tree mortality. Further research is required for understanding and quantifying the joint effect of all the interacting mortality drivers. Nevertheless, it seems that for Norway spruce, the warmer future with increased mortality is already here, and it should be taken into consideration in forest management. Favouring mixed stands could be one of the solutions to help Norway spruce survive in the warming climate.

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.