OSA2.4

Surface transport forecasting has traditionally focused on deterministically categorising the state - for example, dry, damp, or icy - of a road surface, to provide decision support to winter gritting services. However, with the emerging Connected Autonomous Vehicle (CAV) sector, the ability to accurately describe a broader range of weather conditions, at a much smaller temporal and spatial scale, is becoming equally important to ensuring public safety. Localised conditions such as road spray, rain and fog can all degrade the performance of CAV sensors, whilst settled snow also has the potential to impede navigation by obscuring road markings. The difficulty of deterministically forecasting such precise localised conditions requires us to quantify uncertainty, driving a move towards probabilistic, risk-based forecasting. Therefore, the Met Office is currently developing a new Surface Transport Forecasting (STF) post-processing system, designed to accommodate these future user requirements.

The new STF post-processing system is centred on the Joint UK Land Environment System (JULES); a community model used as the land-surface component of the Met Office Unified Model (UM), but which can also be used – as we do here – as a stand-alone land-surface model driven by forecast output from Numerical Weather Prediction (NWP) models. To produce probabilistic forecasts for locations within the United Kingdom, we are using output from the Met Office regional ensemble model MOGREPS-UK to drive JULES. MOGREPS-UK is a 2.2km resolution 18-member ensemble which provides forecasts out to 5 days. It is generated by time-lagging over 6 hours, initialising three new ensemble members every hour from perturbed initial conditions. By running JULES for each ensemble member, we create a set of possible road forecast outputs. Considering these predictions in aggregate allows us to generate probabilistic forecasts of road weather conditions.

Our ensemble-driven STF system has been verified using standard ensemble verification techniques, including rank histograms and reliability plots. Initial analysis of results, using data from approximately 300 locations in the United Kingdom for which good quality road weather observations are available between 2015 and 2021, indicate that observed road surface states are generally captured within the spread of predictions. The new system has been compared with the current deterministic STF system.  In particular, in challenging meteorological conditions (for example, where there is variable cloud cover, scattered showers, or on marginal temperature nights) the probabilistic approach allows us to quantify the uncertainty in the road state forecast in a way that the deterministic approach does not. Further work will focus on the most effective way to communicate probabilistic forecasts to end-users, ensuring they are able to apply the output to enhance their decision-making.

How to cite: Eyles, J., Lake, A., Susorney, H., and Odbert, H.: Developing Forward-looking Probabilistic Road Weather Forecasts at the Met Office, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-113, https://doi.org/10.5194/ems2022-113, 2022.

Spatially accurate road condition forecasts are important for road maintenance personnel to keep roads safe in changing weather conditions. Road surface temperature can vary significantly depending on the road surroundings. Buildings, trees, and terrain features can block solar radiation and cause the temperature to be lower than in open surroundings. On the other hand, the long wave radiation from the surrounding objects can make the road warmer on clear nights. Sky view factor and shadowing algorithm were added to the Finnish Meteorological Institute’s road weather model to take the surrounding features into account in road weather forecasts. Sky view factor means the portion of the sky that is visible at a certain point and shadowing algorithm determines whether the road point receives direct solar radiation at a certain time using local horizon angles. The local horizon angles and sky view factors were determined from the digital surface model (DSM) that was generated from National Land Survey of Finland’s laser scanning data. The data’s resolution is at average 0.5 points per square meter. The DSM differ from digital elevation model (DEM) as it includes vegetation and buildings. The DSM was generated in pieces to cover a 150 km long motorway from Helsinki to Turku in southern Finland. The sky view factors and local horizon angles were calculated for road points located every 50 m on both west and east leading carriageways. Some small-scale road structures like light and electricity poles and road signs caused problems as they appeared too bulky in the DSM. An overlay covering the carriageways was taken from a 2m resolution DEM and was added over the DSM to smooth the problematic features. As this was not enough to remove all the small-scale structures at the side of the road, the local horizon angles went through an algorithm that smoothed too large spikes and bulky features. The produced local horizon angles and sky view factors were given to the road weather model. The forecasts are generated once in an hour for all the selected road points on the motorway. Spatial differences in the forecasted surface temperatures can be seen especially in autumn and spring when the sun is low. At many places the northern carriageway leading to the west is warmer in these situations, as the trees or rock cuttings prevent the east leading southern carriageway from getting direct solar radiation. The forecast system is now in testing phase but has potential to improve spatial accuracy of road surface temperature forecasts.

How to cite: Karsisto, V. and Horttanainen, M.: Updated road weather forecast system using sky view factor and screening implemented on a motorway in Finland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-49, https://doi.org/10.5194/ems2022-49, 2022.

The climate of Ireland is changing. Consequently, climate maps to support building design standards are being updated to inform national Technical Guidance Documents. Additionally, weather files, comprising test reference years and design summer years, are being produced for locations in Ireland to assess the risk of overheating in buildings. This project is carried out by Met Éireann under its strategy ‘Making Ireland Weather and Climate Prepared’ and funded by the Department of Housing, Local Government and Heritage. The outputs of this project are part of Ireland’s Climate Action Plan 2021 under action 197 – develop specific climate maps and data for use in building design to enhance resilience in support of climate change adaptation. The climate services produced in this project will support a wide range of stakeholders, currently collaborating with Met Éireann, which include the National Standards Authority of Ireland, ARUP, Transport Infrastructure Ireland, Office of Public Works, Sustainable Energy Authority of Ireland, Kavanagh Mansfield & Partners, and the Department of Housing, Local Government and Heritage. These climate services will also inform policy in delivering key national infrastructure, including housing, drainage, transport networks and building renovation.

The presentation will focus on the importance, data, methodology and outputs of the project’s work packages:

• Work package 1: Maps of driving rain intensity (maps of spell index following two orientations: N as per ISO 15927-3:2009 and SW as the predominant wind direction in Ireland).
• Work package 2: Maps of isotherms of the highest maximum shade air temperature (50, 100 and 120 years return periods). Maps of isotherms of the lowest minimum shade air temperature (50, 100 and 120 years return periods).
• Work package 3: Maps and gridded dataset of estimation of point rainfall frequencies based on the depth-duration-frequency model with durations varying from 1 to 25 days, 24 hours to 15 minutes, and shorter than 15 minutes (50, 100 and 120 years return periods).
• Work package 4: Maps of isotherms of the lowest 10cm soil temperature (50, 100 and 120 years return periods). Maps of snow loads (50, 100 and 120 years return periods).
• Work package 5: Current and future weather data sets (Test Reference Years and Design Summer Years) to assess overheating risk in buildings.

The produced climate services will be made available open-access through the Met Éireann and the Department of Housing, Local Government and Heritage websites.

How to cite: Mateus, C., Lambkin, K., Coonan, B., Griffin, S., McGovern, J., McGuinness, S., Murphy, K., Eames, M., and Coyle, O.: Climate maps and data to support building design standards in Ireland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-106, https://doi.org/10.5194/ems2022-106, 2022.

Buildings constructed today will be exposed to climatic changes during their lifetime. Changes in the outdoor climate also affect the indoor climate in buildings where people spend a substantial part of their day. In addition, the building sector significantly contributes to energy demand and greenhouse gas emissions. Therefore, it is vital to design buildings in a way that ensures comfortable indoor temperatures today and in the future while minimizing energy use. For Switzerland, an additional warming of 2-3 degrees Celsius by mid-Century is projected without climate change mitigation. According to the Swiss Climate Scenarios CH2018, heatwaves are set to increase significantly in frequency, intensity and duration, whereas coldwaves will be less common. To adapt to these changes and design buildings optimally for a warmer future climate, climate projections tailored to the needs of the building sector are required.

To serve this demand and together with partners from universities and the industry, MeteoSwiss has created a reference dataset for the future climate for use in building simulations. For this, observations were combined with climate change information from the CH2018 scenarios. The resulting reference data represent the possible future climate at locations across Switzerland. Hourly and physically consistent data for the future for the variables temperature, humidity, wind and solar radiation have been calculated. These represent typical years and warm summer years. The data are available for the near future and the middle of the century and are based on two different greenhouse gas emission scenarios: 'no climate change mitigation' (RCP8.5) and 'concerted climate change mitigation efforts' (RCP2.6). The data are also available for four extra-urban and urban station pairs, which allows evaluating the urban heat island effect.

The described reference data were compiled in close collaboration with users and allows them to design buildings tailored to a warmer climate. Findings from using this data in building simulations allow inferring requirements for the architecture and operation of the buildings and show an increasing demand for cooling systems in the future.

How to cite: Wehrli, K., Gubler, S., Settembrini, G., Sidler, F., and Kotlarski, S.: Deriving future climate reference data for the Swiss building sector, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-395, https://doi.org/10.5194/ems2022-395, 2022.

According to the current National Building Code of Finland, the room temperature in new residential buildings should not exceed the threshold of 27 °C by more than 150 degree hours per year. The code also demands that new buildings pass the nearly zero-energy requirements for energy performance and conformity. The fulfilment of these requirements in the design phase of the new buildings needs to be verified by dynamic building performance simulation tools that use specific meteorological reference year weather data sets as hourly input. In addition to commercial purposes, such simulation software’s, like the IDA Indoor Climate and Energy (ICE) tool, may be used for education and research.

Here we describe several sets of hourly weather data that have been recently used in Finland as input to IDA-ICE simulations. TRY2020 data sets describe typical present-day weather conditions during twelve months that originate from different years of the period 1989-2018. They were selected based on a standard methodology (ISO 15927-4) with a few modifications. The same 30-year period was used to choose new cooling design days (1% risk) for Finland on the basis of another standard method (ISO 15927-2). HWS2018 data set, in turn, consists of weather observations of heatwave summer 2018.

All the data sets include the following variables: air temperature, relative humidity, global, diffuse and normal direct solar radiation as well as wind speed and direction. While the data sets describing the recent past climate are based on observations, their counterparts corresponding to future climates are based on CMIP5 climate model projections and delta-change methods.

The IDA-ICE tool was used, among others, to simulate the risk of too warm indoor conditions. According to the results, already nowadays during a prolonged heat wave the annual degree hours above 27 °C in apartment buildings without mechanical cooling may be several times the limit of 150. In the future, hotter summers are expected to become more common and the risk of overheating of dwellings will continue to increase. This would cause an increase in the heat-related health and mortality risks. Although solar protection windows and openable windows were found to be effective in reducing indoor temperatures, the results show that active cooling systems will be needed to eliminate the overheating risk.

Ref: Velashjerdi Farahani et al. 2020, https://doi.org/10.3390/app11093972

How to cite: Korhonen, N., Velashjerdi Farahani, A., Jokisalo, J., Kosonen, R., Lestinen, S., and Jylhä, K.: Weather impacts on simulated indoor conditions, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-653, https://doi.org/10.5194/ems2022-653, 2022.

As urbanization expands, the urban heat island (UHI) effect caused by high temperatures is becoming obvious increasingly. Previous researches have shown that urban types and anthropogenic heat emission are the main influencing factors. However, the differences in the types of urban areas lead to different UHI intensity between regions. In this context, more and more studies begin to explore the impact of meteorological data on building energy consumption. The temperature of the air conditioner setting and the time of use account for a large percentage of the total building energy consumption. Cooling degree hours (CDH) is the cumulative value of the temperature difference between the outside air temperature and the base temperature of the cooled room. This study was used the ASHRAE standard cold room temperature of 23°C. Information such as climate data, building morphology, zoning and building electricity collection can be integrated through the Geographic Information System (GIS). This study used existing electricity consumption data and GIS maps to estimate energy consumption information in Taichung, and combine the historical climate reconstruction data of Taiwan from The Taiwan Climate Change Projection and Information Platform Project (TCCIP) to analyze the relationship between urban high temperature and residential energy consumption.

The study area is Taichung City, Taiwan, excluding the mountainous areas with low temperature. This study divided Taichung into 500×500 m² grids, and analyzed the estimated residential energy consumption with climate data. The highest average temperature in Taiwan is in July, so this study collects temperature data for July 2021 in Taichung. According to the analysis, it can calculate the Taichung average temperature from July is between 26-28°C and the average RH is 80-85%. CDH in the suburbs below 80°C -hour. City center CDH is between 105-180°C -hour. After integrating building form and land use with GIS, the average residential energy consumption of the grid was calculated. The average residential energy consumption of Taichung is about 3,000MWh.Get rid of the grid without building and compare the energy consumption of the building, CDH increases as the energy consumption increases. However, when the energy consumption exceeds 4,000 MWh, the CDH approaches 50°C-hour. Based on this result, the energy consumption below 1,000MWh is the slope maximum. The energy consumption between 1,000 MWh and 3,000 MWh is smaller slope value. The slope of energy consumption above 3,000 MWh tends to be nearly 0, revealing that the energy consumption was less affected by the increasing CDH. The results can be used in future urban energy planning to develop regional improvement strategies for areas with higher energy consumption.

How to cite: Chang, C., Chen, C.-Y., and Lin, T.-P.: Analysis of residential energy consumption based on urban climate patterns, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-100, https://doi.org/10.5194/ems2022-100, 2022.

More than 80 % of total electricity consumption in Finland is covered by domestic production. While 52 % of the Finnish electricity production in 2020 was based on renewable energy sources, 34 % of electricity was generated with nuclear power. Currently there are five nuclear power plant (NPP) units in the country, two in Loviisa in the south and three in Olkiluoto in the west, all along the seashore. Because of the importance of the NPPs to national electricity production, no external or internal events should hamper their normal operation.

As defined by the Finnish Radiation and Nuclear Safety Authority (STUK), overall safety management over the life cycle of an NPP unit requires, among others, probability estimates for external events triggered by exceptional weather events, such as very high and low atmospheric temperatures, high winds including tornadoes and downbursts, rain, snow, hail, freezing rain, lightning, and drought. The probability estimates are used in the safety assessments of existing and new NPP units and in the design of new safety features.

This presentation shows some results from our weather-related studies conducted within the Finnish Research Programme on Nuclear Power Plant Safety (SAFIR2022). A closely related presentation in this session, given by Leijala et al., focuses on sea level research relevant for nuclear safety.

Our current topics include the climatology of convective sea-effect snowfall, heavy precipitation jointly with high sea level, large-scale windstorms and derechoes, i.e., clusters of downbursts.  Changes in the seasonal cycle of sea-effect snowfall have been studied based on reanalysis data (ERA5) and trends in the frequency of the compound precipitation and sea level events using observational data. In addition, we have examined tracks and clustering of large-scale windstorms based on ERA5 and occurrence of derechoes based on the FMI network of meteorological stations and weather radar data.

Even very low annual probabilities of occurrence are of relevance for the NPPs. Therefore, meteorological and climatological research conducted for other applications, including building regulations, land use planning and infrastructure protection, is supportive but typically not sufficient. The topics discussed here have been selected based on feedback from STUK and the Finnish NPP companies. For example, although intense snowfall does not pose a direct threat to the safety systems of the NPPs, it might hamper the normal operation of the support systems and their interface with the environment, e.g., by blocking ventilation air intakes.

References:

Leijala et al.: Examining extreme sea levels for the support of nuclear power plant safety in Finland.

Official Statistics of Finland (OSF): Production of electricity and heat [e-publication].

How to cite: Jylhä, K., Olsson, T., Rantanen, M., Rauhala, J., Laurila, T., Leijala, U., Luomaranta, A., Särkkä, J., and Virman, M.: Exceptional weather and nuclear power plant safety in Finland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-646, https://doi.org/10.5194/ems2022-646, 2022.

Urbanized low-lying coastal areas become evidently increasingly vulnerable in the future climate, where higher mean sea level and more powerful storm surges are expected. Safe planning and operation particularly of critical coastal infrastructure, such as nuclear power plants, requires careful evaluation of external events due to extreme weather and sea level conditions. Finland is located in the northeast part of the Baltic Sea, where sea level variations are driven by short-lasting phenomena (storm surges, internal oscillations in the Baltic Sea, tides), long-term mean sea level change (global mean sea level, the post-glacial land uplift and the Baltic Sea water balance), and wind waves. In Finland, there are altogether five nuclear power plant units along the coast and approximately one third of electricity production in the country is grounded on nuclear energy.

The Finnish Meteorological Institute has studied weather, climate and sea level hazards potentially posing risks to nuclear power plants since 2007 in several research projects. In this presentation, we will introduce some preliminary results of studies on extreme sea levels on the Finnish coast, which are conducted in the PREDICT project (https://en.ilmatieteenlaitos.fi/predict). Their focus is on the short-term sea level variations, which might be several meters in the Baltic Sea, even if the tides in the region are mostly negligible.

In the first study, Bayesian hierarchical modelling is used to estimate return levels of annual sea level maximum on the Finnish coast and non-stationarity in the related sea level extremes. Our model setup enables sharing information on sea level extremes between the neighboring tide gauge stations. Additionally, it accounts temporal variations in the distribution parameters by incorporating climate indices such as North Atlantic oscillation (NAO) in the model. Preliminary results suggest that hierarchical approach reduces the range of uncertainty in the estimated parameters.

The second study tackles a question “What is the most severe flooding that could occur in the Baltic Sea coast in the present climate if the weather conditions are optimal?”. In this study, effects of low-pressure intensity, speed, direction and point of origin on the sea level extremes is examined by making simulations with numerical sea level model combined with the synthetic cyclones for the Finnish tide gauge locations. Tentative results indicate that the highest sea level extremes on the Finnish coast are caused by large and slowly propagating wind storms.

How to cite: Leijala, U., Johansson, M. M., Jylhä, K., Laine, M., Rantanen, M., Räihä, J., Räty, O., and Särkkä, J.: Examining extreme sea levels for the support of nuclear power plant safety in Finland, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-651, https://doi.org/10.5194/ems2022-651, 2022.

How to cite: Filla, S., Rautio, T., Sane, M., and Veijalainen, N.: Added value of spatially high-resolution flood and statistical data: Starting from Assessment of the cost of inaction regarding climate change, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-648, https://doi.org/10.5194/ems2022-648, 2022.

The rainfall intensity for various return periods are commonly used for hydrological design. In this study, we focus on rare, short-term precipitation extremes and related return values which are relevant durations in the planning and operating demands of drainage and sewerage systems in Hungary.

Hungarian Meteorological Service together with the National Water Directorate (Municipal Water Management Department) with the professional assistance of the Hungarian Chamber of Engineers (Water Management and Water Construction Section) developed a user service for design purposes.  The user can download the return levels of the short-term rainfall intensity for the closest meteorological station to the location of the planned object specified with geographical coordinates.

Automatic stations replaced the ombrometer in many places in Hungary, particularly from the late 1990s. The change of the measurement practice do not allow simply merging the data recorded form the registering paper in the past and the recent 10 minutes measurements.   The most intense 5, 10, 20, 30, 60, 180 min sub-totals per rainfall events were recorded from the ombrometer registering paper before atomization, typically until 1993. By contrast, the 10 min precipitation sum from the AWSs are stored in the meteorological database of the Hungarian Meteorological Service from automatization. In order to join together the older and the AWS measurements it was necessary to develop a method to make this possible. Therefore, we downscaled the 10 min data in time. The sampling of the AWSs is one minute, although the 1-minute data are available only for some stations in the digital database. We applied a linear regression model to downscale the 10-minute data for 1 min. After this, we can derive the most intense sub-totals per events from the AWS data as if they have been measured with the ombrometers. Therefore, several station data series can be made longer, thus the confidence intervals of the return level estimates are narrowing, and the quality of the service is improving.

How to cite: Lakatos, M., Izsák, B., Szentes, O., and Bihari, Z.: Analysis of short-term precipitation extremes for design purposes in Hungary, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-575, https://doi.org/10.5194/ems2022-575, 2022.