Finnish Meteorological Institute Research Articles

From MASK-air and SILAM to CATALYSE (Climate Action To Advance HeaLthY Societies in Europe).

Plant species vary under different climatic conditions and the distribution of pollen in the air. Trends in pollen distribution can be used to assess the impact of climate change on public health. In 2015, the Mobile Airways Sentinel networK for rhinitis and asthma (MASK-air®) was launched as a project of the European Innovation Partnership on Active and Healthy Ageing (EIP-on-AHA, DG Santé and DG CONNECT). This project aimed to develop a warning system to inform patients about the onset of the pollen season, namely, the System for Integrated modeLling of Atmospheric coMposition (SILAM). A global-to-meso-scale dispersion model was developed by the Finnish Meteorological Institute (FMI). It provides quantitative information on atmospheric pollution of anthropogenic and natural origins, particularly on allergenic pollens. Impact of Air Pollution on Asthma and Rhinitis (POLLAR, EIT Health) has combined MASK-air clinical data with SILAM forecasts. A new Horizon Europe grant (Climate Action to Advance HeaLthY Societies in Europe [CATALYSE]; grant agreement number 101057131), which came into force in September 2022, aims to improve our understanding of climate change and help us find ways to counteractit. One objective of this project is to develop early warning systems and predictive models to improve the effectiveness of strategies for adapting to climate change. One of the warning systems is focused on allergic rhinitis (CATALYSE Task 3.2), with a collaboration between the FMI (Finland), Porto University (Portugal), MASK-air SAS (France), ISGlobal (Spain), Hertie School (Germany), and the University of Zurich (Switzerland). It is to be implemented with the support of the European Academy of Allergy and Clinical Immunology. This paper reports the planning of CATALYSE Task 3.2.

The contribution of residential wood combustion to the PM2.5 concentrations in the Helsinki metropolitan area

Abstract. This article has investigated the contribution of residential wood combustion (RWC) to the fine particulate matter (PM2.5) concentrations in the Helsinki metropolitan area (HMA) for 6 years, from 2009 to 2014. We have used the PM2.5 concentrations measured at eight air quality measurement stations. The dispersion of emissions on an urban scale was evaluated with multiple-source Gaussian dispersion models, UDM-FMI (urban dispersion model of the Finnish Meteorological Institute) and CAR-FMI (Contaminants in the Air from a Road model of the Finnish Meteorological Institute), and on a regional scale using the chemical transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). The overall agreement of the predicted concentrations with measurements of PM2.5 was good or fairly good for all stations and years; e.g. at the permanent residential station the daily average values of the index of agreement ranged from 0.69 to 0.81, and the fractional bias values ranged from −0.08 to 0.11, for the considered 6 years. Both the measured and predicted daily averaged concentrations showed increasing trends towards the lower-temperature values. The highest predicted annual averaged concentrations in the region occurred in the vicinity of major roads and streets and in the suburban residential areas to the northwest, north, and northeast of the city centre. The average concentrations of PM2.5 attributed to RWC in winter were up to 10- or 15-fold, compared to the corresponding concentrations in summer. During the considered 6-yearly period, the spatially highest predicted fractions of RWC of the annual PM2.5 concentrations ranged from 12 % to 14 %. In winter, the corresponding contributions ranged from 16 % to 21 %. The RWC contribution was higher than the corresponding urban vehicular traffic contribution at all the residential stations during all years. The study has highlighted new research needs for the future, in particular (i) the modelling of the RWC emissions that would be explicitly based on the actual ambient temperatures and (ii) the modelling of the impacts of the most important holiday periods on the emissions from RWC.

Final report, ongoing key comparison BIPM.QM-K1, ozone at ambient level, comparison with FMI, March 2023

Main textAs part of the ongoing key comparison BIPM.QM-K1, a comparison has been performed between the ozone national standard of Finland, maintained by the Finnish Meteorological Institute (FMI) and the common reference standard of the key comparison, maintained by the Bureau International des Poids et Mesures (BIPM). The instruments have been compared over a nominal ozone amount fraction range of 0 nmol mol-1 to 500 nmol mol-1.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Undetected biogenic volatile organic compounds from Norway spruce drive total ozone reactivity measurements

Abstract. Biogenic volatile organic compounds (BVOCs) are continuously emitted from terrestrial vegetation into the atmosphere and react with various atmospheric oxidants, with ozone being an important one. The reaction between BVOCs and ozone can lead to low volatile organic compounds, other pollutants, and the formation of secondary organic aerosols. To understand the chemical and physical processes taking place in the atmosphere, a complete picture of the BVOCs emitted is necessary. However, the large pool of BVOCs present makes it difficult to detect every compound. The total ozone reactivity method can help understand the ozone reactive potential of all BVOCs emitted into the atmosphere and also help determine whether current analytical techniques can measure the total BVOC budget. In this study, we measured the total ozone reactivity of emissions (TOZRE) from a Norway spruce tree in Hyytiälä in late summer using the total ozone reactivity monitor (TORM) built at the Finnish Meteorological Institute (FMI). Additionally, we conducted comprehensive chemical characterisation and quantification of BVOC emissions using a gas chromatograph coupled with a mass spectrometer (GC–MS), enabling us to estimate the calculated reactivity of emissions (COZRE). TOZRE reached up to 7.4×10-9 m3 s−2 g−1, which corresponds to 65 µg g−1 h−1 of α-pinene. Stress-related sesquiterpenes, such as β-farnesene and α-farnesene, and an unidentified sesquiterpene contributed the most to the observed emissions. However, COZRE made up only 35 % of the TOZRE, with sesquiterpenes being the most important sink for ozone. High TOZRE values were especially seen during high-temperature periods, with up to 95 % of TOZRE remaining unexplained. Emissions of unidentified stress-related compounds could be the reason for the high fraction of missing reactivity.

Pressure sensor for the Mars 2020 Perseverance rover

The Perseverance rover of NASA’s Mars 2020 mission carries a pressure sensor (PS) provided by the Finnish Meteorological Institute (FMI). The sensor belongs to the Mars Environmental Dynamics Analyzer (MEDA), a set of environmental sensors designed to characterize the near-surface atmospheric conditions. MEDA PS is based on the same Vaisala sensor and measurement technology as the pressure sensor of the Curiosity rover, but utilizes newer-generation sensor heads. The sensor has been calibrated in the pressure range of 0–14 hPa and temperature range from −45 to ＋55 °C. The calibration is based on tests performed in FMI’s pressure calibration laboratory, as well as measurements done after integration to MEDA and the rover. Since February 2021, MEDA PS has operated flawlessly on board Perseverance, delivering regular measurements of the local atmospheric pressure in Jezero crater. According to the evaluation based on the first 530 sols, MEDA PS is found to provide high-quality data with performance meeting expectations.

Effect of the urban microenvironment on the indoor air temperature of the residential building stock in the Helsinki region

Due to climate change, there is an increased risk of apartment overheating. In Nordic countries, heatwaves have not been common in the past and hence apartments are not equipped with mechanical cooling systems, so wise urban design might be a solution. This study evaluated the influence of the urban microenvironment on residential building indoor air temperature via green view index (GVI), floor area ratio and distance from the sea. We analyzed a large dataset of over 2000 apartments in the Helsinki region during summers of 2018 and 2021, where severe heatwaves were presented, and combined it with the aforementioned parameters. In the method used, closely situated buildings were clustered into groups by the microenvironment parameters. The results showed consistent correlations between clustered groups and microenvironment parameters, where the best-performing group had an indoor air temperature of about 1 °C lower than the average during the summers and 0.7 °C lower during the severe heatwaves. Building groups with higher GVI demonstrated a greater ability to endure long heatwaves, where combined influence of other urban microenvironment factors was significantly reduced. A substantial influence of sea distance and floor area ratio was observed during short heatwaves in the middle and late summer. The indoor temperature difference of the groups was compared to the average outdoor temperature difference of group areas based on the Finnish Meteorological Institute HARMONIE-AROME weather model implemented with the SURFEX module. The results revealed a consistent correlation between predicted outdoor and indoor temperatures and their distribution of group-to-area differences.

Trace Elements Concentrations in Urban Air in Helsinki, Finland during a 44-Year Period

The atmospheric concentrations of seventeen elements were measured in air filters at the Finnish Meteorological Institute station in Helsinki, Finland, during a period of 44 years (1962–2005). The mean annual concentrations were calculated and are presented from the lowest values to the highest ones Cr < Ni < Ti < Br < V < Mn < Cu < Zn < Cl < Al < Fe < K < Ca < Na < Pb < Si < S. Most of the elements (Fe, Si, Ti, K, Ca, Zn, Br, Pb, V, Ni, S, Cr, Na, Al, and Cl) present higher values during spring and winter season, while in summer the elements (Ti, Ca, S, and Na) are found in higher concentrationsdue to the weather conditions across seasons and the sources and emissions of air pollutants. There is a strong correlation between the elements (V-Ni, Si-Pb, Fe-Ca, V-Cr, Si-K, K-Ca, Fe-Ti, K-Na, Si-Ca, and V-S), indicating their common source. The identification of the sources of trace elements was performed based on positive matrix factorization analysis, using SoFi software. Four Suspended Particulate Matter (PM) sources were identified: road dust (due to usage of leaded fuel), heavy oil combustion/secondary sulfates, traffic emissions, and natural dust (soil). For the total of 44 years studied, significant decreases in concentrations were observed for all elements, most of which were over 50%: Na (−74%), Al (−86%), Si (−88%), S (−82%), K (−82%), Ca (−89%), Ti (−80%), V (−89%), Cr (−82%), Mn (−77%), Fe (−77%), Ni (−61%), Zn (−72%), and Pb (−95%). In general, a significant decline has been observed in the majority of the elemental concentrations since the end of the 1970s, underlying the effectiveness of different environmental policies that have been applied during the last few decades.

Value of Spatially Distributed Rainfall Design Events—Creating Basin-Scale Stochastic Design Storm Ensembles

Current design storms used in hydrological modeling, urban planning, and dimensioning of structures are typically point-scale rainfall events with a steady rainfall intensity or a simple temporal intensity pattern. This can lead to oversimplified results because real rainfall events have more complex patterns than simple design series. In addition, the interest of hydrologists is usually in areal estimates rather than point values, most commonly in river-basin-wide areal mean rainfall estimates. By utilizing weather radar data and the short-term ensemble prediction system pySTEPS, which has so far been used for precipitation nowcasting, ensembles of high-resolution stochastic design storms with desired statistical properties and spatial structure evolving in time are generated. pySTEPS is complemented by adding time-series models for areal average rainfall over the simulation domain and field advection vectors. The selected study area is the Kokemäenjoki river basin located in Western Finland, and the model parametrization is carried out utilizing the Finnish Meteorological Institute’s weather radar data from the years 2013 to 2016. The results demonstrate how simulated events with similar large-scale mean areal rainfall can produce drastically different total event rainfalls in smaller scales. The sampling method, areal vs. gauge estimate, is also shown to have a prominent effect on total event rainfall across different spatial scales. The outlined method paves the way towards a more thorough and wide-spread assessment of the hydrological impacts of spatiotemporal rainfall characteristics.

Use of Webcams in Support of Operational Snow Monitoring

Ultrasonic sensors are one of the most common automatic monitoring methods in operational snow depth monitoring with reliable results. On the other hand, there is significant uncertainty when measuring small snow depths (<2 cm), thus it cannot provide binary snow presence (on/off) information. The use of webcams in monitoring snow cover has proven to be successful in recent studies and applications. In this study, we applied an adaptive thresholding technique on images from webcams to obtain reliable snow on/off information to complement the ultrasonic snow depth measurements. Camera and ultrasonic sensor data from two weather stations in Finland were studied. The webcam data was processed using FMIPROT (Finnish Meteorological Institute Image Processing Tool) software, operating in a cloud computing environment, which can generate near real-time data. Our results indicate that webcam-derived data can be successfully used for quality control or as auxiliary data to support operational ultrasonic sensor measurements and provide a cost-effective improvement to operational monitoring capabilities. Webcam monitoring is especially useful during the melting season when the snow depth is below 15 mm, with accuracy values between 72% and 94%.

Sky View Factor and Screening Impacts on the Forecast Accuracy of Road Surface Temperatures in Finland

Abstract Forecasting road conditions is important, especially in areas with wintry conditions and rapidly changing weather. Accurate forecasts help authorities keep roads safe and optimize maintenance. Considering local features is important when making the forecast because the road surface temperature can vary significantly depending on the road surroundings. For example, in a shadowed location, the road surface temperature can be significantly lower than in open surroundings. A road weather model developed at the Finnish Meteorological Institute is used to forecast the road surface temperature and road conditions. However, the model still assumes open road surroundings. In this study, sky view factor and screening are included in the model, and their effects on the forecast road surface temperature is tested. Road surface temperature hindcasts were performed for 23 selected road weather stations in Finland for three winter periods (October–March) between 2018 and 2021. The results were location dependent, and even changing the lane had a great effect on the verification results in some cases. At best, the screening considerably decreased RMSE values during the day. However, there were many cases in which the screening increased RMSE. In general, the used shadowing algorithm increased the already negative bias during the day. Nevertheless, there were also cases in which the shadowing algorithm improved the bias, especially in February. During the night, the sky view factor made the forecast generally a little warmer, which often slightly decreased the negative bias in the forecast. Significance Statement The screening caused by objects surrounding a road has a great effect on the road surface temperature. Recently, a screening algorithm was added to the Finnish Meteorological Institute’s model that forecasts road conditions. The purpose of this study was to test how the algorithm affects the accuracy of road surface temperature forecasts. According to the results, the screening greatly improved the forecast accuracy in some cases. However, in some cases, the screening made the already overly cold forecast even colder. The study has increased our understanding of the effect of shadowing in the modeled road surface temperatures and helps to create more accurate forecasts in the future.

Low temperature, cold spells, and cardiorespiratory hospital admissions in Helsinki, Finland

There is only limited scientific evidence with varying results on the association between hospital admissions and low ambient temperatures. Furthermore, there has been no research in Northern Europe on cold-associated morbidity. Therefore, this study investigated the associations of daily wintertime temperature and cold spells with cardiorespiratory hospital admissions in the Helsinki metropolitan area, Finland. Daily number of non-elective hospital admissions for 2001–2017 was obtained from the national hospital discharge register and meteorological data from the Finnish Meteorological Institute. Quasi-Poisson regression models were fitted, controlling for potential confounders such as time trend, weekday, holidays, air pollution, barometric pressure, and influenza. The associations of cold season daily mean ambient temperature and cold spells with hospital admissions were estimated using a penalized distributed lag linear models with 21 lag days. Decreased wintertime ambient temperature was associated with an increased risk of hospitalization for myocardial infarction in the whole population (relative risk [RR] per 1 °C decrease in temperature: 1.017, 95% confidence interval [CI]: 1.002–1.032). An increased risk of hospital admission for respiratory diseases (RR: 1.012, 95% CI: 1.002, 1.022) and chronic obstructive pulmonary disease (RR: 1.031, 95% CI: 1.006, 1.056) was observed only in the ≥ 75 years age group. There was an independent effect of cold spell days only for asthma admissions (RR: 2.348, 95% CI: 1.026, 5.372) in the all-ages group. Cold temperature increases the need for acute hospital care due to myocardial infarction and respiratory causes during winter in a northern climate.

MEDA HS: Relative humidity sensor for the Mars 2020 Perseverance rover

The Finnish Meteorological Institute (FMI) provides a relative humidity measurement sensor (HS) for NASA's Mars 2020 rover. The sensor is a part of the Mars Environmental Dynamic Analyzer (MEDA), a suite of environmental sensors provided by Spain's Centro de Astrobiología. The main scientific goal of the humidity sensor is to measure the relative humidity of the Martian atmosphere near the surface and to complement previous Mars mission atmospheric measurements for a better understanding of Martian atmospheric conditions and the hydrological cycle. Relative humidity has been measured from the surface of Mars previously by Phoenix and Curiosity. Compared to the relative humidity sensor on board Curiosity, the MEDA HS is based on a new version of the polymeric capacitive humidity sensor heads developed by Vaisala. Calibration of humidity devices for Mars conditions is challenging and new methods have been developed for MEDA HS. Calibration and test campaigns have been performed at the FMI, at University of Michigan and the German Aerospace Center (DLR) in Berlin to achieve the best possible calibration. The accuracy of HS and uncertainty of the calibration has been also analysed in detail with VTT Technical Research Centre of Finland. Assessment of sensor performance after landing on Mars confirms that the calibration has been successful, and the HS is delivering high quality data for the science community.

Bayesian Classification of Nonmeteorological Targets in Polarimetric Doppler Radar Measurements

Abstract The latest established generation of weather radars provides polarimetric measurements of a wide variety of meteorological and nonmeteorological targets. While the classification of different precipitation types based on polarimetric data has been studied extensively, nonmeteorological targets have garnered relatively less attention beyond an effort to detect them for removal from meteorological products. In this paper we present a supervised learning classification system developed in the Finnish Meteorological Institute (FMI) that uses Bayesian inference with empirical probability density distributions to assign individual range gate samples into 7 meteorological and 12 nonmeteorological classes, belonging to five top-level categories of hydrometeors, terrain, zoogenic, anthropogenic, and immaterial. We demonstrate how the accuracy of the class probability estimates provided by a basic naive Bayes classifier can be further improved by introducing synthetic channels created through limited neighborhood filtering, by properly managing partial moment nonresponse, and by considering spatial correlation of class membership of adjacent range gates. The choice of Bayesian classification provides well-substantiated quality estimates for all meteorological products, a feature that is being increasingly requested by users of weather radar products. The availability of comprehensive, fine-grained classification of nonmeteorological targets also enables a large array of emerging applications, utilizing nonprecipitation echo types and demonstrating the need to move from a single, universal quality metric of radar observations to one that depends on the application, the measured target type, and the specificity of the customers’ requirements. Significance Statement In addition to meteorological echoes, weather radars observe a wide variety of nonmeteorological phenomena including birds, insects, and human-made objects like ships and aircraft. Conventionally, these data have been rejected as undesirable disturbance, but lately their value for applications like aeroecological monitoring of bird and insect migration has been understood. The utilization of these data, however, has been hampered by a lack of comprehensive classification of nonmeteorological echoes. In this paper we present a comprehensive, fine-grained, probabilistic classifier for all common types of nonmeteorological echoes which enables the implementation of a wide range of novel weather radar applications.

Association of summertime daily mean temperature and sick leaves among employees of the City of Helsinki, Finland.

BACKGROUND AND AIM Heat affects work productivity and well-being at work. People working outdoors are the most vulnerable to heat-related health issues that may cause absenteeism from work. However, the phenomenon concerns indoor workers without sufficient air conditioning as well. Previous studies concerning occupational heat stress have concentrated on health effects, the incidence of heat-related illness, preventative measures, and productivity losses. Correlation between heat waves and sickness related work absenteeism rates has not been studied. Therefore, this study aims to evaluate the relationship of daily summertime temperatures and heatwaves with sick leaves rates among employees of Helsinki City, Finland. METHODS We used Poisson time series models adjusted for potential confounders, such as time trend, air pollution, pollen, and barometric pressure to investigate the associations of daily temperature with daily rates of new sick leaves. Daily number of sick leaves for May-August 2002-2017 was obtained from the Helsinki City employee register and daily weather information from the Finnish Meteorological Institute. RESULTS Daily mean temperature ranged from 1.5 °C to 26.6 °C during the warm season. Preliminary results indicate that increased daily mean temperature is associated with decreased risk of sick leaves at lag 0 and lags 2-5. At 1 day lag, increased daily mean temperature is associated with decreased risk of sick leaves until the daily mean temperature exceeds 24 °C after which the risk begins to increase. Risk ratio [RR] is 1.003 at 26 °C compared to 24 °C. CONCLUSIONS Preliminary results indicate that increased daily summertime mean temperature is mostly associated with decreased daily sick leave rate. However, our preliminary analyzes suggest that there may be an increase in daily sick leave rate at 1 day lag when daily mean temperature exceeds a threshold. In further analysis, the effect of prolonged heatwaves will be evaluated. KEYWORDS occupational health, heat

Short-Term Coarse Particulate Exposure and Daily Cardio-Respiratory Hospital Admissions in the Helsinki Capital Region

BACKGROUND AND AIM Short-term exposure to coarse inhalable particles (2.5 µg/m3>PMcoarse<10 µg/m3) has been suggested to increase the risk of respiratory disease independently of fine particles, but studies present mixed findings which are even less clear for other outcomes. This study aims to assess the relationship between short-term exposure to PMcoarse and total admissions for cardio-pulmonary diseases. METHODS We conducted a seventeen-year (01/2001-12/2017) time-series study of the daily admissions for cardio-respiratory disease in relation to the mean levels of coarse particles. We obtained air pollution data from the Helsinki Region Environmental Services Authority, meteorological data from the Finnish Meteorological Institute, and hospital exit records from the Finnish Institute for Health and Welfare. We modelled the association between particles and cardiorespiratory admissions using log-linked quasi-Poisson regression. Penalised splines were used to adjust for the time-trend and the meteorological variables. These models were also adjusted for the weekday, pollen concentration, and influenza outbreaks. Different lag structures were explored in both single-pollutant and multipollutant models. The multipollutant models were simultaneously adjusted for PM2.5, NO2, and O3. RESULTS The mean(standard deviation) concentration of PMcoarse was 6.6(5.9) (µg/m3), while the mean (standard deviation) daily numbers of cardiovascular and respiratory admissions were 25.9(7.3) and 21.3(7.4), respectively. A 10 µg/m3 increase in the same day PMcoarse was associated with a 1.7% (95% CI, 0.4%—3.1%) and a 1.9% (95% CI, 0.5%—3.4%) excess daily risk of respiratory admissions in the single-pollutant and multipollutant models, respectively; same-day excess cardiovascular admissions risks were 0.6% (95% CI, -0.5%—1.7%) and 0.3% (95% CI, -0.9%—1.4%) in similar models. CONCLUSIONS Short-term exposure to PMcoarse can increase respiratory admission risks independently of co-pollutants but has a less determinate effect on cardiovascular admissions. A guideline framework should be considered for PMcoarse. KEYWORDS Coarse Particles, Cardiovascular Admissions, Respiratory Admissions, Time-Series, Fine Particles

Associations of long-term solar insolation with specific depressive symptoms: Evidence from a prospective cohort study

Evidence suggests that sunlight counteracts depression, but the associations of long-term sunlight exposure with specific symptoms of depression are not well known. We evaluated symptom-specific associations of average 1-year solar insolation with DSM-5 depressive symptoms in a representative cohort of Finnish adults. The sample included 1,845 participants from the Cardiovascular Risk in Young Finns Study with data on DSM-5 depressive symptoms, place of residence and covariates. Daily recordings of global solar radiation were obtained from the Finnish Meteorological Institute. Each participant's residential zip code on each day one year prior to the assessment of depressive symptoms was linked to the solar radiation data, and 1-year average daily solar insolation was calculated. Associations of the average 1-year solar insolation with depressive symptoms were assessed with linear and logistic regression analyses adjusting for season, sex, age, as well as individual- and neighborhood-level socioeconomic characteristics. Average daily solar insolation over one year prior to the depressive symptom assessment was not associated with the total number of depressive symptoms reported by participants. In symptom-specific analyses, participants exposed to higher levels of solar insolation in their residential neighborhood were less likely to report suicidal thought (OR = 0.61, 95% CI, 0.39–0.94), and more likely to report changes in appetite (OR = 1.24, 95% CI, 1.00–1.54), changes in sleep (OR = 1.30, 95% CI, 1.06–1.59) and feelings of worthlessness/guilt (OR = 1.33, 95% CI = 1.07–1.65). These findings suggest that solar insolation may contribute to symptom-specific differences in depression. Studies in other populations residing in different geographical locations are needed.

Wind and Turbulence Statistics in the Urban Boundary Layer over a Mountain–Valley System in Granada, Spain

Urban boundary layer characterization is currently a challenging and relevant issue, because of its role in weather and air quality modelling and forecast. In many cities, the effect of complex topography at local scale makes this modelling even more complicated. This is the case of mid-latitude urban areas located in typical basin topographies, which usually present low winds and high turbulence within the atmospheric boundary layer (ABL). This study focuses on the analysis of the first ever measurements of wind with high temporal and vertical resolution throughout the ABL over a medium-sized city surrounded by mountains in southern Spain. These measurements have been gathered with a scanning Doppler lidar system and analyzed using the Halo lidar toolbox processing chain developed at the Finnish Meteorological Institute. We have used the horizontal wind product and the ABL turbulence classification product to carry out a statistical study using a two-year database. The data availability in terms of maximum analyzed altitudes for statistically significant results was limited to around 1000–1500 m above ground level (a.g.l.) due to the decreasing signal intensity with height that also depends on aerosol load. We have analyzed the differences and similarities in the diurnal evolution of the horizontal wind profiles for different seasons and their modelling with Weibull and von Mises probability distributions, finding a general trend of mean daytime wind from the NW with mean speeds around 3–4 m/s at low altitudes and 6–10 m/s at higher altitudes, and weaker mean nocturnal wind from the SE with similar height dependence. The highest speeds were observed during spring, and the lowest during winter. Finally, we studied the turbulent sources at the ABL with temporal (for each hour of the day) and height resolution. The results show a clear convective activity during daytime at altitudes increasing with time, and a significant wind-shear-driven turbulence during night-time.

Intercomparison Experiment of Water-Insoluble Carbonaceous Particles in Snow in a High-Mountain Environment (1598 m a.s.l.)

The harmonization of sampling, sample preparation and laboratory analysis methods to detect carbon compounds in snow requires detailed documentation of those methods and their uncertainties. Moreover, intercomparison experiments are needed to reveal differences and quantify the uncertainties further. Here, we document our sampling, filtering, and analysis protocols used in the intercomparison experiment from three laboratories to detect water-insoluble carbon in seasonal surface snow in the high-mountain environment at Kolm Saigurn (47.067842° N, 12.98394° E, alt 1598 m a.s.l.), Austria. The participating laboratories were TU Wien (Austria), the University of Florence (Italy), and the Finnish Meteorological Institute (Finland). For the carbon analysis, the NIOSH5040 and EUSAAR2 protocols of the OCEC thermal-optical method were used. The median of the measured concentrations of total carbon (TC) was 323 ppb, organic carbon (OC) 308 ppb, and elemental carbon (EC) 16 ppb. The methods and protocols used in this experiment did not reveal large differences between the laboratories, and the TC, OC, and EC values of four inter-comparison locations, five meters apart, did not show meter-scale horizontal variability in surface snow. The results suggest that the presented methods are applicable for future research and monitoring of carbonaceous particles in snow. Moreover, a recommendation on the key parameters that an intercomparison experiment participant should be asked for is presented to help future investigations on carbonaceous particles in snow. The work contributes to the harmonization of the methods for measuring the snow chemistry of seasonal snow deposited on the ground.

An extensive data set for in situ microphysical characterization of low-level clouds in a Finnish sub-Arctic site

Abstract. Continuous, semi-long-term, ground-based in situ cloud measurements were conducted during eight Pallas Cloud Experiments (PaCEs) held in autumn between 2004 and 2019. Those campaigns were carried out in the Finnish sub-Arctic region at the Sammaltunturi station (67∘58′24′′ N, 24∘06′58′′ E; 560 m a.m.s.l.), the part of the Pallas Atmosphere–Ecosystem Supersite and Global Atmosphere Watch (GAW) program. Two cloud spectrometer ground setups and a weather station were installed on the roof of the station to measure in situ cloud properties and several meteorological variables. Thus, the obtained data sets include the size distribution of cloud droplets as a measured cloud parameter along with the air temperature, dew point temperature, humidity, pressure, horizontal wind speed and direction, (global solar) sun radiation, and visibility at the station. Additionally, the number concentration, effective diameter, median volume diameter, and liquid water content from each instrument were derived. The presented data sets provide a insight into microphysics of low-level clouds in sub-Arctic conditions over a wide range of temperatures (−25.8 to 8.8 ∘C). The data are available in the Finnish Meteorological Institute (FMI) open data repository for each campaign and each cloud spectrometer ground setup individually: https://doi.org/10.23728/FMI-B2SHARE.988739D21B824C709084E88ED6C6D54B (Doulgeris et al., 2021).

Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies.

There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. These emerging technologies can considerably enhance the operational performance of communication between vehicles and road-side infrastructure. This paper analyzes the performance of cellular-based long-term evolution (LTE) and 5GTN (5G Test Network) in pilot field measurements (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) when delivering road weather and traffic information in real-time environments. Measurements were conducted on a test track operated and owned by the Finnish Meteorological Institute (FMI), Finland. The results showed that 5GTN outperformed LTE when exchanging road weather and traffic data messages in V2V and V2I scenarios. This comparison was made by mainly considering bandwidth, throughput, packet loss, and latency. The safety critical messages were transmitted at a transmission frequency of 10 Hz. The performance of both compared technologies (i.e., LTE and 5GTN) fulfilled the minimum requirements of the ITS-Assisted Road weather and traffic platform to offer reliable communication for enhanced road traffic safety. The field measurement results also illustrate the advantage of cellular networks (LTE and 5GTN) with a clear potential to use it heterogeneously in future field tests with short-range protocols, e.g., IEEE 802.11p.