The simultaneous occurrences of several extreme events, known as compound extremes, are often associated with greater impact than univariate extremes. Flooding and windstorms are widespread hazards in Europe which can lead to severe property damage and fatalities. During winter, extreme wind and precipitation often co-occur, since they are associated with a common driver, namely extratropical cyclones. In this project, the occurrences of compound wind and precipitation events in Europe are investigated using the ERA5 reanalysis dataset. The analysis covers the years 1979-2019 with a focus on boreal winter. Areas that experience the highest occurrence of compound wet-windy extremes are the west coast of Norway, the Iberian peninsula, parts of central Europe, and southeast of the Black Sea. A few case studies are discussed with the purpose to give an idea of the magnitude of possible impacts. Further, the relationship between extreme wind and precipitation events and the North Atlantic Oscillation (NAO) is presented. During days with positive NAO, extreme precipitation and wind events occur in the central and northern parts of Europe while the negative phase brings extreme wind and precipitation to the southern parts of Europe. Lastly, a short analysis to discover changes in the occurrences of compound precipitation and wind events for the two periods 1979-1999 and 2000-2019 is performed. The result showed no clear changes. The number of compound extremes does not seem to vary for the two periods. / Olika extrema väderhändelser som sammanfaller orsakar ofta större skada än enskilda extrema händelser på många håll i samhället. Översvämningar och vindstormar är vanligt förekommande i Europa och kan leda till kostsamma skador och dödsfall. Extrema vind-och nederbördshändelser sammanfaller vanligen under vintern eftersom de båda ofta orsakas av Nordatlantiska cykloner, som är vanligast under den årstiden. I detta projekt kartläggs sammanfallande vind- och nederbördshändelser i Europa under vintermånaderna december-februari, med hjälp av ERA5 reanalysdata för åren 1979-2019. Områden med relativt hög förekomst av sammanfallande vind- och nederbördshändelser är Norges västkust, Iberiska halvön, delar av Centraleuropa och östra Turkiet. Några fallstudier kopplade till dessa områden är diskuterade för att ge en uppfattning om konsekvenserna av dessa sammanfallande händelser. Eventuella kopplingar mellan sammanfallande vind- och nederbördshändelser och den Nordatlantiska Oscillationen (NAO) är också undersökt. Under den positiva fasen av NAO sker extrema nederbörd- och vindhändelser i norra och centrala delar av Europa medan den negativa fasen ger extrem vind och nederbörd i de södra delarna av Europa.En kort analys för undersöka om förekomsten av extrema sammanfallande händelser har ändrats genomfördes genom att jämföra andelen sammanfallande händelser under de två perioderna 1979-1999 och 2000-2019. Ingen betydande förändring i andelen sammanfallande händelser mellan dessa perioder hittades.

A new theory for predicting the windprofile over a canopy has been evaluated. The theory was first presented by Harman and Finnigan (2007). The theory relies on the forming of a mixing-layer above the canopy, due to different mean wind in and above the canopy. Characteristics from both mixing-layer and Monin Obukhov similarity theory have been used to develop the governingequations that give the wind profile. The theory has been used to calculate wind profiles for sixdifferent atmospheric stabilities. In order to evaluate the theory, profiles from the theory have beencompared to measurements from Jädraås forest, Sweden. Profiles from Monin Obukhov similarity theory were also used for comparison.In general the mixing-layer theory gives better results than Monin Obukhov similarity theory. Agreement with measurements is good in neutral conditions, but fails when the atmospheric stability is altered, especially in convective conditions. This is believed to be due to the canopy lacking in thickness. The mean wind speed is systematically underestimated and this is also believed to be caused by insufficient thickness of the canopy. A correction for this behaviour is proposed. The theory gives higher values of the mean wind speed in convective conditions with the correction and the calculated values of mean wind speed are closer to the measurements.

Black carbon has been reported to have major impacts on climate, environmental quality, and health. A chemothermal oxidation method, ordinarily used to isolate black carbon from soils, sediments and aquatic samples was applied to explore atmospheric samples. The results were then compared with thermal optical reflectance measurements, and then investigated in parallel with a health survey conducted during sampling. This study assesses atmospheric emissions from the NTPC Farakka coal-fired power plant in West Bengal, the distribution of emissions, the mobilization of black carbon, and its impact on pulmonary and cardiovascular conditions among respondents in Murshidabad and Malda, in West Bengal, India. Two measurement campaigns were conducted during winter and summer (November 2021 – June 2022); a total of 81 samples (Murshidabad) and 65 samples (Malda) were collected and analyzed and 193 respondents were surveyed for lung functionality. Over the study period, the mean concentration of PM2.5 and soot were observed to be 147 μg/m³; 113 μg/m³ and 1.52 μg/m³; 1.54 μg/m³ respectively. From the spirometry tests, it was concluded that there was chronic exposure to PM2.5 at both the sites, during the winter and monsoon campaigns. With increases in PM2.5 corresponding with higher numbers of restrictive and obstructive cases.

Accurate fog prediction is an important task facing forecast centers since low visibility can affect anthropogenic systems, such as aviation. Therefore, this study investigates the use of Machine Learning classification algorithms for post-processing the output of the Danish Meteorological Institute’s operational Numerical Weather Prediction (NWP) model to improve visibility prediction. Two decision tree ensemble methods, XGBoost and Random Forest, were trained on more than 4 years of archived forecast data and visibility observations from 28 locations in Denmark. Observations were classified into eight classes, while models were optimized with resampling and Bayesian optimization. On an independent 15-month period, the Machine Learning methods show an improvement in balanced accuracy, F1-score, and Extremal Dependency Index compared to the NWP and persistence models. XGBoost performs slightly better. However, both methods suffer from an increase in overprediction of the low visibility classes. The models are also discussed regarding usability, coping with model changes and preservation of spatial features. Finally, the study shows how the interpretation of the post-processing models may be included operationally. Future research recommendations include incorporating more variables, using alternative class imbalance methods and further analyzing the models’ implementation and usage. Overall, the study demonstrates the potential of these models to improve visibility point forecasts in an operational setting. / Begränsad sikt kan på olika sätt påverka samhällen och naturen. Till exempel kan dimma störa både flyg- och biltrafiken. Därför är det viktigt att kunna förutspå sikt. Eftersom traditionella prognosmetoder, som numeriska vädermodeller, inte alltid är tillförlitliga för detta ändamål, är det viktigt att utforska alternativa metoder. I den här studien undersöks användningen av maskininlärning för att förbättra numeriska prognoser av sikt. Två olika maskininlärningsalgoritmer användes för att post-processera Danmarks Meteorologiska Instituts numeriska vädermodell och de tränades på siktobservationer från 28 olika platser. Resultaten visar att maskininlärnings-metoderna förbättrar den numeriska vädermodellen, med 10 - 30% beroende på hur man mäter. Dock har algoritmerna en liten tendens att förutspå låg sikt för ofta, och båda är bättre på kustnära platser. Den bäst presterande av de två algoritmerna lyckas identifiera förväntade meteorologiska förhållande i samband med låg sikt. Dessutom presenteras en metod för att förbättra förståelsen av de post-processerade modellerna. Men det finns fortfarande utmaningar med att implementera metoden operationellt. Därför föreslås det att framtida studier bland annat undersöker om algoritmerna presterar bättre med fler väderparametrar, hur de presterar på nyaplatser samt att djupare analys av hur de hanterar updateringar till den numeriska vädermodellen görs. Sammanfattningsvis visar studien att maskininlärning är ett lovande verktyg för att förbättra numeriska prognoser av sikt.

This Master thesis examines a phenomenon that occurs in the upper polar atmosphere, namely, Polar Mesospheric Winter Echoes, or PMWEs. PMWEs are radar echoes observed by Very High Frequency Mesosphere Stratosphere Troposphere (VHF MST) radar, from altitudes of 60 to 76 km at 7 to 15 UT, in the winter months from the middle of September to the beginning of May. The aforementioned specifications are the partial results of this thesis.PMWEs are generally understood to be caused by turbulence; however, radar data indicate some rare cases where PMWEs can be created with velocities exceeding the speed of sound, which is not possible with current turbulence theory. Kirkwood et al., 2006 and Belova, Kirkwood, and Sergienko, 2013 hypothesised that infrasound could generate the necessary conditions for PMWEs with velocities equal to or exceeding the speed of sound. Observations of PMWEs presented in this thesis have been carried out by the MST radar ESRAD, located at Esrange (67 56’N, 21 04’E) near Kiruna in northern Sweden. The radar operates at 52 MHz and has been performing continuous radar observations since Dec 1996. Observations of the infrasounds presented in this thesis were carried out by a microbarometer located close to Rymdcampus in Kiruna. Access to the data is restricted, but through university administration, this master’s thesis has been granted permission to use the data for the study of PMWEs. The instrument performed continuous infrasound measurements from the 24th of May 2016 and forward. This thesis will perform a full analysis of all radar data from the 17th of Dec 1996 until the 31st of Jan 2021 to assess the altitude interval, diurnal interval and yearly interval. The data given in the first section are a result of this work. Additionally, space weather parameters relation with the occurrence of PMWEs is analysed. Space weather parameters are very important for the chemistry and conditions present in the mesosphere. It is shown thatt here is a strong relation between solar wind and PMWE occurrence, decent relation with Kp index and no to weak relation with solar particle event (SPE). Correlating space weather and PMWEs in greater detail could be the subject of other studies. Last but most interesting, microbarometer data will be analysed for days where high-speed PMWEs are detected. Because of the rarity of these high-speed PMWEs, only seven total cases were found from 24 May 2016 onward, making the analysis of the infrasound measurements very limited, and no connection was found. However, it was found that days with high-speed PMWEs had an abnormally low amount of infrasound detections, further making analysis difficult but raising questions of why. This could mean that infrasound signals might be hindered from reaching the ground on days where we have high-speed PMWEs. No further conclusions can be made, as this indicates relation but not causation. Airborne infrasound instruments could be used to detect weaker infrasound signals due to being unaffected by wind disturbance at the ground.

The atmospheric boundary layer in the Arctic is essential for the understanding of climate change and improving regional weather prediction. The aim of this study is to investigate to which degree wind speed profiles retrieved in the Arctic agree with well known wind profile concepts and understand which local impact factors influence the wind speed profile. As part of the Nansen Legacy project, scientists from the University Centre in Svalbard and the University of Bergen installed two wind lidars onboard the research vessel “Kronprins Haakon” during the “Winter Process Cruise” in February 2021. Wind speed profiles were collected over a period of two weeks. They were manually classified into three categories based on their shape. The ideally shaped profiles were fitted against the wind profile power law to identify the exponent, α, for use in the Arctic marine boundary layer. α was found to be 4-5 times smaller than the conventionally applied α = 1/7 for profiles retrieved over open water, which was associated with unstable atmospheric conditions. Additionally, α was found to be considerably larger than 1/7 when sea ice was present, which was associated with stable conditions. A dependency on wind speed was also found. These results underline the importance of adjusting the exponent in order to ac- curately model the wind speed in the Arctic marine boundary layer. The results might be important for optimizing potential wind energy production, which is of great interest with the increasing human activ- ity in the Arctic. Reversed profiles (wind speed maxima closest to the surface) were mainly measured over open ocean and during low wind speeds and were speculated to be related to swell conditions. Pro- files containing a maxima in low levels were primarily measured during stable atmospheric conditions when sea ice was present. Future research in Arctic conditions would benefit from extending wind speed measurements to even lower levels and including stability measurements for an even deeper analysis.

Studien handlade om att belysa hur UAV:s kan komma till nytta i meteorologisk forskning och att ta reda på hur bra drönare är på att mäta meteorologiska storheter. Drönare, som också kallas UAV:s (Unmanned Aerial Vehicle) är mindre obemmanade luftfartyg som kan flyga autonomt eller fjärrstyras. Idag används drönare alltmer i meteorologi vilket beror mycket på den tekniska utvecklingen. Exempel på meteorologiska applikationer är mätning av vindhastighet och koncentrationen av koldioxid i luften som kan användas för att studera de lägsta atmosfäriska skikten. Storheter som mättes i detta projekt var vindhastighet, vindriktning, temperatur och relativ fuktighet på olika höjder. Mätningarna gjordes två gånger i en mätstation i Marsta, som ligger utanför Uppsala. Efteråt jämfördes datan från drönaren med data från en instrumenterad mast vid väderstationen. Resultaten visade att drönarens vindprofiler stämde ganska väl överens med mastens vindprofiler och den logaritmiska vindlagen. Under den första fältmätningen uppskattade drönaren att vindhastigheten var 8.13 ± 1.33 m/s vid hovring på tio meters höjd medan masten angav 8.41 ± 0.958 m/s. Drönarens mätvärden för vindriktningen var ibland bra och ibland mindre bra. Mätvärdena för temperaturen och den relativa fuktigheten avvek med upp mot 1 ◦C respektive 10 procentenheter. / The aim of this report was to study how UAV:s can be applied in meteorological research and find out how good drones are at measuring meteorological parameters. A drone, also known as an UAV (Unmanned Aerial Vehicle) is a smaller unmanned aircraft that can fly autonomously or under remote control. Today, drones are used more frequently in meteorology, mostly due to the recent technological development. Examples of meteorological applications include measurements of wind speed and the amount of carbon dioxide in the air which can be used to analyze the lower parts of the atmosphere. In this project, the wind speed and its direction, the temperature and the relative humidity were measured at different heights. The measurements took place twice in Marsta which is a field station outside Uppsala. Then, the data from the drone was compared to the data from a weather tower at the instrumented station. The results showed that the drone’s wind profiles were relatively similar to the profiles according to the tower and the logarithmic wind profiles. For instance, during the first flight the drone estimated the wind speed to be 8.13 ± 1.33 m/s while hovering ten metres above the ground. The tower measured 8.41 ± 0.958 m/s at the same height. The drone’s estimations of the wind direction were sometimes accurate and sometimes not accurate. The temperature and relative humidity however was different by 1 ◦C and ten percentage units respectively.

Two post processing methods for quantifying the shadow effect of the offshore wind farm Princes Amalia (PA) onto Egmond aan Zee (OWEZ) wind farm are analyzed and benchmarked. The first method is the author’s proposed shadow effect determination method (SEDM), which quantifies an offshore wind farm’s shadow effect based on mesoscale WRF (Weather Research Forecast) idealized modeling and the observed frequency of the analyzed site’s wind conditions. The Fitch turbine parametrization scheme and Mellor-Yamada-Nakanishi-Niino (MYNN) surface layer and planetary boundary layer (PBL) schemes were used to simulate the wind farm’s interactions, based on site conditions. The proposed physical downscaling method (SEDM) uses filtered simulated atmospheric stability and wind speed conditions, in order to calculate the percent wind speed deficit downstream of PA, with regard, first, to observed wind speed frequency and, secondly, to wind speed and corresponding atmospheric stability regimes. Then a statistical downscaling method, based on the established Analog Ensemble (AnEn) technique, developed by Luca Delle Monache et al. (2011) was employed to verify the results from the first method. This method runs a post processing algorithm using the weighted average of the observations that were verified when the 15 best analogs were valid. Observed wind speed data at 10 m and 50 m height was used as Numerical Weather Prediction (NWP) input data and fit to observed time series data. From this, wind speeds at 116 m were extrapolated, in order to estimate the reconstructed atmospheric stability. The two methods were benchmarked and shadow effects were quantified in the range of 7.53% - 22.92% for the SEDM and within an 80% confidence interval of 0.23% -1.83% for the statistical downscaling method. Given the physical method’s exceedance of this confidence interval, WRF idealized modeling proves itself as a consistent means of quantifying an offshore wind farm’s wake, as demonstrated by comparable studies, however inaccurate when benchmarked to statistical modelling methods that use observed wind speed data to recreate atmospheric conditions. / Wake Research Group

The work of this master thesis is based on analyses of monthly precipitation data from 70 stations of the SMHI (Swedish Meteorological and Hydrological Institute) in Sweden, in the period 1860-2014, using the information for the year of introduction of the wind shield at each station. The primary goal is the calculation of correction factors which will be applied on the precipitation data in the period of measurements before the introduction of the wind shield. This correction will counterbalance the underestimation of the collected precipitation by the unshielded precipitation gauges due to the effect of the wind. The wind induced error, related to aerodynamical effects, increases with increasing wind speed. The stronger the wind, the more capable it is of deflecting the precipitation water droplets or snowflakes, falling towards the gauge orifice, away from it. In spite of the important efficiency of the wind shield which acts to diminish the wind error, the long-term effect of changing the measuring instrumentation at some time in the observations history is the production of inhomogeneous data in the measurements records. Inhomogeneous precipitation data are sources of errors in climatology and hydrology and result in misleading conclusions regarding the climate change and climate variations, hence they should be identified and corrected through a homogenization method. The analysis includes the comparison of the precipitation data of each station during two periods, one before and one after the introduction of the wind shield. This comparison leads to the calculation of ratios representing the increase in the catch between the two periods due to the introduction of the wind shield. Temperature data are also processed in order to estimate the type of precipitation (snow/rain) in each case. The monthly corrections ranged between 5 %, for rain, and 27 % for snow precipitation. The absolute value of the increase of the average annual precipitation due the implementation of the correction was 50 mm. The comparison of the corrected against the uncorrected precipitation time series indicated a less pronounced increase (0.74 mm/y) of the precipitation during the last 150 years, after the application of the correction, compared to the increase indicated from the uncorrected data (1.19 mm/y). / Kontinuerliga samt felfria nederbördsmätningar är av stor betydelse för geovetenskaper som klimatologi och hydrologi därför att nederbördsdata är en av de primära meteorologiska parametrarna för forskning om klimatförändringen. Att säkerställa felfria (homogena) nederbörds tidsserier betyder i stort sett att säkerställa homogenitet genom att identifiera och korrigera inhomogena data. Icke homogena data uppkommer på grund av förändringar i mätmetoder och mätförhållanden under observationstiden, sedan 1860-talet tills idag alltså. Denna studies syfte är att beräkna en korrektion som ska användas för att korrigera nederbördsmätningar som utfördes sedan 1860 utan vinskydd. Vindskyddet eller vindskärmen, en speciell utrustning som användas på nederbördsinsamlare, infördes gradvis under perioden 1900-1960 vid de svenska nederbördstationerna. Vindskyddet introducerades med avsikt att minska vindens påverka vid nederbördsinsamling. Men trotts den positiva effekten som vindskyddet ledde till, genom den ökade nederbördsmängden som samlades in, skapade denna förändring av mätarutrustningen inhomogena data. Bearbetningen skedde för månadsnederbördsdata från 70 stationer från SMHIs meteorologiska nätverk genom att jämföra nederbördsobservationer som genomfördes under perioderna tio år före och tio år efter införandet av vindskydd. Dessutom användes temperaturdata från samma stationer för att uppskatta nederbördslag (snö/regn). Skälet till detta är att vinskyddseffekten är olika mellan snö och regn. Beräkningarna och bestämningen av nederbördslag ledde till en 5 % respektive 27 % nederbörds ökning för regn och snö för de mätningarna som utfördes utan vindskydd. I genomsnitt har de korrigerade värdena, under perioden som vinskyddet saknades, ökat med omkring 50 mm.

Uncertainties with a modeling framework consisting of a weather generator, two precipitation disaggregation models and the hydrological HBV model was assessed with respect to hydrometeorological extremes in Tyrol, Austria. Extreme precipitation events are expected to increase in intensity and frequency in the Alps during a warmer climate. The Alpine regions may be particularly vulnerable to such changes in climate where many floods in Europe occurred during recent years and caused major damage and loss of life. Weather generators typically provide time series at daily resolution. Different disaggregation methods have therefore been proposed and successfully tested to increase temporal resolution in precipitation. This is essential since flood peaks may be maintained for as little as minutes. Here, the non-parametric method of fragments was tested and compared with the multiplicative microcanonical cascade model with uniform splitting on the reproduction of precipitation extremes. It is also demonstrated that the method of fragments model can be transformed to disaggregate temperature with slight changes in the model structure. Preliminary test results show that the simulation of discharge peaks can be improved by disaggregating temperature in comparison with using daily averages as input in the HBV model.  Test results show that precipitation extremes were simulated within confidence bounds for Kelchsauer and Gurglbach when using historical observations as input. These two catchments had longer records of data available in comparison with Ruetz where the majority of simulated precipitation extremes were found outside confidence ranges. This indicates that the model is data driven. Synthetic data series were constructed with the weather generator from historical data and disaggregated with the two disaggregation models. The differences between the models were bigger for Ruetz where less observed data was available. The method of fragments simulates extremes with the closest resemblance to extremes. This is also true for the reproduction of wet spells and simulated variance. To account for parameter uncertainty in the HBV model, it is highly motivated to simulate discharge with different but suitable parameter sets to account for equifinality. However, the large amount of data produced when disaggregating the weather generated time series transcended the data capacity of the HBV model and made it crash. Other uncertainties related to the framework are the use of theoretical probability distributions in the weather generator and the dependence of high-resolution data for the disaggregation model. Despite these uncertainties, the framework is closer to a physical understanding of the causes of floods than the uncertain frequency analysis method. The framework is also applicable to land-use and climate change studies.