Finnish Meteorological Institute Research Articles

The Associations of Long-Term Temperature and Precipitation with Chronic Respiratory Symptoms: Projections for the Changing Climate

PurposeTo clarify the associations of climatic indices with chronic respiratory symptoms, with a final aim to approximate the effects of climate change on them.MethodsAn e-mail survey was directed to the members of the Finnish Pensioners` Federation. The mean 20-years’ precipitation and temperature in each subjects’ home municipality were obtained from the Finnish Meteorological Institute, separately for summer and winter. Adjusted multivariate models were utilized to investigate the associations of the climatic indices with chronic rhinosinusitis, chronic cough, wheezing with dyspnea, and sleep apnea.ResultsThere were 6189 responders from 283 municipalities. Chronic rhinosinusitis and chronic cough were most prevalent in the southeastern regions of the country, where the precipitation counts were highest. In the multivariate models, winter precipitation in the home municipality increased the risks of chronic rhinosinusitis and chronic cough [adjusted OR 1.80 (1.30–2.51) per 100 mm, p < 0.001, and 1.57 (1.19–2.07) per 100 mm, p = 0.001, respectively]. Wheezing with dyspnea and sleep apnea were not associated with the climatic indices.ConclusionChronic rhinosinusitis and chronic cough were associated with long-term winter precipitation. Given the anticipated increase in winter precipitation in Northern America and Northern Europe, the prevalences of chronic rhinosinusitis and chronic cough may increase there.

Improving relative humidity measurements on Mars: new laboratory calibration measurements

Abstract. In this paper we present new calibration measurements that have been performed with the ground reference models of the relative humidity instruments of the Mars Science Laboratory (MSL), Mars 2020 and ExoMars missions. All instruments are based on capacitive sensor head technology, and they are developed, manufactured and tested by the Finnish Meteorological Institute (FMI). Calibration of capacitive humidity sensors for the Martian environment has been a challenging task and special facilities are needed in order to create Martian conditions including all relevant environmental parameters that can be accurately controlled and measured: low pressure, low temperature, carbon dioxide environment and especially humidity. A measurement campaign was performed at the German Aerospace Center (DLR) PASLAB (Planetary Analog Simulation Laboratory) to determine relative humidity calibration datasets for REMS-H, MEDA HS and METEO-H instruments in temperatures from −30 °C down to −70 °C in low-pressure CO2. In addition to the stable point humidity calibration measurements in CO2, the instrument performance was tested with the actual Martian atmosphere composition and during long continuous measurements. The new calibration dataset has already been used in the flight calibration of the MEDA HS instrument, resulting in successful calibration and excellent accuracy. The results from this campaign will further improve relative humidity measurements on Mars by providing the means to reanalyze the current calibration of the REMS-H flight model and by allowing more accurate comparison between the two instruments currently on the Martian surface.

U-net with ResNet-34 backbone for dual-polarized C-band baltic sea-ice SAR segmentation

Abstract In this study, the U-net with ResNet-34, i.e. a residual neural network with 34 layers, backbone semantic segmentation network is applied to C-band sea-ice SAR imagery over the Baltic Sea. Sentinel-1 Extra Wide Swath mode HH/HV-polarized SAR data acquired during the winter season 2018–2019, and corresponding segments derived from the daily Baltic Sea ice charts were used for training the segmentation algorithm. C-band SAR image mosaics of the winter season 2020–2021 were then used to evaluate the segmentation. The major objective was to study the suitability of semantic segmentation of SAR imagery for automated SAR segmentation and also to find a potential replacement for the outdated iterated conditional modes (ICM) algorithm currently in operational use. The results compared to the daily Baltic Sea ice charts and the operational ICM segmentation and visual interpretation were encouraging from the operational point of view. Open water areas were located very well and the oversegmentation produced by ICM was significantly reduced. The correspondence between the ice chart polygons and the segmentation results was also reasonably good. Based on the results, the studied method is a potential candidate to replace the operational ICM SAR segmentation used in the Copernicus Marine Service automated sea-ice products at Finnish Meteorological Institute.

Baltic sea ice thickness estimation based on X-band SAR data and background information

Abstract In this study an operational sea ice thickness (SIT) estimation algorithm, based on HH-polarized X-band synthetic aperture radar (SAR) data, background information from the most recent, typically from the previous day, available daily Baltic Sea ice chart and the operational Finnish Meteorological Institute (FMI) thermodynamic ice model, was developed and evaluated. The algorithm was designed to complement the C-band SAR SIT algorithm developed earlier at FMI and applied daily as part of the operational Copernicus Marine Service (CMS). The X-band SIT algorithm was developed by utilizing the sea ice thickness measurements made onboard the Finnish and Swedish ice breakers during two winters seasons: 2021–2022 and 2022–2023. The former season measurements were used for defining the algorithm parameters and the later season for evaluation of the algorithm performance. According to the evaluation metrics the X-band algorithm performance is slightly better than that of the operational CMS C-band SAR SIT algorithm, indicating its suitability for operational use in CMS.

Early auroral photography and observations at the Sodankylä Geophysical Observatory in Finland, 1927–1929

Abstract. In Finland, auroral photography started in 1927 at the Sodankylä Geophysical Observatory (SGO) with the initiative of famous Norwegian scientist Carl Störmer. In less than 2 years about 600 photographs of auroras were taken at Sodankylä. Some of the images were obtained simultaneously at auxiliary stations for parallactic determinations of the height of auroral arcs. Most of the pictures of auroras were lost in the destruction of the SGO during the war in 1944. About 200 images were rescued in the archive of the Finnish Meteorological Institute, where they were recently found. These pictures of auroras are the first ones taken in Finland. These photographs are now digitized and archived in the SGO. During the polar year period 1932–1933, auroral photography was mostly discontinued, but visual observations of auroras were made instead at several sites in Lapland. The main sources of information about the history of auroral images are handwritten notebooks of Eyvind Sucksdorff for 1927–1929. They contain relevant data for each photograph (date, exposure time, orientation of camera etc.). In Appendix A there are tables showing the dates of rescued auroral photographs as well as the lost ones. In Finland, Sucksdorff's contribution to studies of auroras was a pioneering effort with minimal resources. Regular photographing of auroras started in Finland during the International Geophysical Year (IGY) 1957–1958.

Pedestrian wintertime slip and fall injuries-effectiveness of weather warning service in prevention.

In climates with wintry conditions, slip and fall injuries of pedestrians during wintertime can result in high healthcare and societal costs. The aim of this study was to evaluate the effectiveness of early warnings about slipperiness in preventing such injuries in Finland. Since 2004, the Finnish Meteorological Institute (FMI) has been providing a weather service for pedestrians, which has been developed continuously over time. Time series of pedestrian slip and fall injuries in commuting on foot in the 19 regions of Finland in 2005-2022 was obtained from the Finnish Workers' Compensation Center and analysed for trends. We also analysed daily warnings about slippery sidewalks from the FMI archives for the period 2011-2022. To identify steps in the service chain that require improvement, we conducted a weather service chain analysis (WSCA) based on a slip and fall accident storyline. There was no significant trend in the number of slipping injuries during the study period. The exposure of the Finnish population to slippery conditions varies according to the climate of the region and characteristics of the winters. We found that the responses of the users of the warning information may be inadequate. Changes in the behaviour of individuals and more efficient winter maintenance of sidewalks are required. Furthermore, it is necessary to promote the role and safety of walking in the planning of traffic systems and urban planning. Further improvements to the pedestrian weather service are needed as well.

Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios

The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under “representative concentration pathway” (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European “energy performance of buildings” directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that annual thermal energy demand in Finland in 2080 can decrease up to 74.6kWh/m2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in South Finland (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better.

DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties

Abstract. Precipitation nowcasting (forecasting locally for 0–6 h) serves both public security and industries, facilitating the mitigation of losses incurred due to, e.g., flash floods and is usually done by predicting weather radar echoes, which provide better performance than numerical weather prediction (NWP) at that scale. Probabilistic nowcasts are especially useful as they provide a desirable framework for operational decision-making. Many extrapolation-based statistical nowcasting methods exist, but they all suffer from a limited ability to capture the nonlinear growth and decay of precipitation, leading to a recent paradigm shift towards deep-learning methods which are more capable of representing these patterns. Despite its potential advantages, the application of deep learning in probabilistic nowcasting has only recently started to be explored. Here we develop a novel probabilistic precipitation nowcasting method, based on Bayesian neural networks with variational inference and the U-Net architecture, named DEUCE. The method estimates the total predictive uncertainty in the precipitation by combining estimates of the epistemic (knowledge-related and reducible) and heteroscedastic aleatoric (data-dependent and irreducible) uncertainties, using them to produce an ensemble of development scenarios for the following 60 min. DEUCE is trained and verified using Finnish Meteorological Institute radar composites compared to established classical models. Our model is found to produce both skillful and reliable probabilistic nowcasts based on various evaluation criteria. It improves the receiver operating characteristic (ROC) area under the curve scores 1 %–5 % over STEPS and LINDA-P baselines and comes close to the best-performer STEPS on a continuous ranked probability score (CRPS) metric. The reliability of DEUCE is demonstrated with, e.g., having the lowest expected calibration error at 20 and 25 dBZ reflectivity thresholds and coming second at 35 dBZ. On the other hand, the deterministic performance of ensemble means is found to be worse than that of extrapolation and LINDA-D baselines. Last, the composition of the predictive uncertainty is analyzed and described, with the conclusion that aleatoric uncertainty is more significant and informative than epistemic uncertainty in the DEUCE model.

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 &lt; Ni &lt; Ti &lt; Br &lt; V &lt; Mn &lt; Cu &lt; Zn &lt; Cl &lt; Al &lt; Fe &lt; K &lt; Ca &lt; Na &lt; Pb &lt; Si &lt; 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 (&lt;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.