Swedish Meteorological And Hydrological Institute Research Articles

Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product

Gridded global horizontal irradiance (GHI) databases are fundamental for analysing solar energy applications' technical and economic aspects, particularly photovoltaic applications. Today, there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements. Nonetheless, databases that generate data at latitudes above 65˚ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites. Amongst the high-latitude gridded GHI databases, the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is likely the most accurate one, providing data across Sweden. To further enhance the product quality, the calibration technique called "site adaptation" is herein used to improve the STRÅNG dataset, which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements. This study introduces a novel approach for site adaptation of solar irradiance based on machine learning techniques, which differs from the conventional statistical methods used in previous studies. Seven machine-learning algorithms have been analysed and compared with conventional statistical approaches to identify Sweden's most accurate algorithms for site adaptation. Solar irradiance data gathered from three weather stations of SMHI is used for training and validation. The results show that machine learning can substantially improve the STRÅNG model's accuracy. However, due to the spatiotemporal heterogeneity in model performance, no universal machine learning model can be identified, which suggests that site adaptation is a location-dependant procedure.

Impacts of climate change on dogsledding recreation and tourism in Arctic Sweden

The range of Arctic tourism supply is continuously increasing with a variety of tourism products on offer. However, climate change is becoming a more prominent issue threatening the operations of tourism businesses and the livelihood of some tourism actors, such as dogsledders. This article aims to fill the descriptive research gap that exists regarding the dependency on the physical environment, climate, and weather for dogsledding activities. This is achieved by studying how climate change may threaten possible climate and weather thresholds for these activities, and how climate change may affect the future opportunities for dogsledding in northern Sweden. The study is based on interviews with dogsledders in Arctic Sweden and climate projections from the Swedish Meteorological and Hydrological Institute (SMHI). The results demonstrate the following thresholds for dogsledding activities: (1) dogsledding requires 10–20 cm of packed snow and/or solid ice on bodies of water, (2) above 15 °C is too hot for dogs to pull (wheeled) sledges, (3) cold weather thresholds are determined by visitors’ preferences and are not considered a problem for dogsledders or dogs, and (4) rain can cancel tours for all dogsledders, and strong wind can cancel tours for dogsledders located in the mountain regions. Finally, extreme events such as heatwaves, storms, thunderstorms, forest fires, heavy rain, floods, and more rapid weather changes have already affected some dogsledders. These necessary thresholds for dogsledding activities could already be jeopardized for the southern and coastal locations of Arctic Sweden. In addition, the climate projections from SMHI show that warmer days and more precipitation in the form of rain will become more common in the future, especially in the absence of global mitigation measures. However, further research on vulnerability/resilience and adaption strategies for dogsledding activities is necessary to truly understand the impact of climate change.

Seasonal Streamflow Forecasting for Fresh Water Reservoir Management in the Netherlands: An Assessment of Multiple Prediction Systems

Abstract For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed seasonal forecast datasets derived from the European Flood Awareness System (EFAS), the Swedish Meteorological and Hydrological Institute (SMHI) European Hydrological Predictions for the Environment (E-HYPE), and Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), which differ in their underlying hydrological formulation, but are all forced by meteorological forecasts from ECMWF’s fifth generation seasonal forecast system (SEAS5). We postprocessed the streamflow forecasts using quantile mapping (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Averaged over the reforecast period, forecasts were skillful for up to 4 months in spring and early summer. Later in summer the skillful period deteriorated to 1–2 months. When investigating specific years with either low- or high-flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skillful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to 3 months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management. Significance Statement Lake IJssel is the Netherlands’ largest freshwater reservoir, with its main water source coming from a branch of the river Rhine. We investigate whether seasonal forecasts of river discharge can help in managing the lake level to create extra buffer capacity for dry periods. We compare three seasonal forecast systems and assess their quality. We find that statistical corrections are needed for all systems to be used. In spring discharge can be predicted up to 4 months ahead due to snow processes. In summer this time is shorter, but it increases with event extremity: severe low-flow events can be predicted longer ahead. This offers potential for water managers to base their lake management on other similar reservoirs.

The effects of station density in geostatistical prediction of air temperatures in Sweden: A comparison of two interpolation techniques

High fidelity gridded temperature datasets are difficult to obtain for areas with sparse coverage of meteorological stations given that sparsity of stations is known to introduce uncertainty in the interpolation of climatic variables generally. Inspired by their potential for optimal results especially for small sample datasets, the accuracy of interpolation results of Empirical Bayesian Kriging (EBK) and EBK-Regression Prediction (EBKRP) spatial prediction techniques were assessed and compared under varying sampling density scenarios, using monthly maximum temperature normals (1991–2020) for the entire area of Sweden. The objective of the was to understand how EBK and EBKRP interpolation techniques perform in different sampling density scenarios and in a sparse data setting, and the possible difference in the prediction accuracy between the two techniques. The 708 sampled stations obtained from the historical climatology database of the Swedish Meteorological and Hydrological Institute (SMHI) were split into seven sampling density subsets, ranging from 1 sample per 63,614 km 2 to 1 sample per 634,350 km 2 and representing both low and high sampling density scenarios. EBK interpolation technique was implemented using temperature data while land use land cover (LULC) and digital elevation model (DEM) were used as temperature covariates for the EBKRP interpolation models. The prediction accuracy assessment was based on five robust prediction performance indicators - mean error, mean absolute error, mean square error, root mean square error and Pearson correlation (R) - obtained from independent validation/cross-validation operations. Prediction accuracy was found to be generally positively related to sampling density and sampling density accounted for 85%–87% of interpolation accuracy for both EBK and EBKRP techniques. Although sampling density increased linearly, the rate of change in accuracy from one sampling density step to the next was not particularly proportional. A rapid increase in accuracy was observed when transiting from 40% to 60% sampling density scenario, but the rate of change in accuracy remained fairly stable before and after this (40% to 60%) sampling density scenario. For equivalent sampling density set-ups, EBKRP consistently performed better than EBK in all the accuracy metrics and EBKRP proved to be about 40% better than EBK. However, the two interpolation techniques produced generally very low prediction biases at all the sampling density scenarios investigated. Our study suggests that potential effects of low sampling density and non-stationarity of temperature data can be significantly reduced by applying EBK but especially EBKRP when coupled with relevant covariates. We also concluded that EBK and EBKRP can be applied to generate accurate prediction surfaces even in a widely varying sampling density setting including environments with moderately low sample density. This is especially true for continuous and slowly varying phenomena such as temperature and similar variables. • Station density is positively related to interpolation accuracy of air temperature but in non-linear fashion. • Univariate and multivariate Empirical Bayesian Kriging interpolation techniques can produce high prediction accuracy result even when data density is small or locally non-stationary. • Multivariate Empirical Bayesian Kriging technique performs better than the univariate technique in different sampling density settings when predicting air temperature. • Normally distributed data can generally improve prediction results even when data density is relatively small or sparse.

GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With Multi-GNSS Constellations

Snow depth monitoring is meaningful for climate analysis, hydrological research and snow disaster prevention. Global Navigation Satellite System-Reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing towards multi-constellation combined inversion. Aiming at the accuracy of snow depth inversion, this paper introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in-situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network, respectively. The root mean square error (RMSE) of the inversion results is reduced by 20-40% compared to the least squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this paper introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings, and provides an important reference for further research on GNSS-R technology.

Development of constructed wetlands in agricultural landscapes using remote sensing techniques

ABSTRACT In Sweden, drainage and the construction of water infrastructure have influenced agriculture in the last few centuries both positively and negatively. Recently, a trend has set in where wetlands are constructed to retain water, retain and reduce nutrients and to enhance the biodiversity. This study aimed to use remote sensing techniques to study landscape water retention over time. In this pilot study, water retention structures in Gotland (57°28′35.0′′N18°29′ 13.9′′E) and Kalmar Län (56°39′41′′N16°21′46′′E) for 2000/2001 and 2020 were identified and analyzed using Landsat data. In this study, it was found that the number of water retention structures (>0.8 ha) increased from 44 to 101 for Gotland Län and from 44 to 127 for Kalmar Län. Most water retention structures were <4 ha and were located in mid- and downstream areas. A comparison of the remote sensed results with the Swedish Meteorological and Hydrological Institute (SMHI) database showed a disagreement of the spatial coordinates of the wetlands in the database with the water retention structures. This pilot study has shown that remote sensed data can be used to identify water retention structures, although higher resolution imagery would be highly advisable in these kinds of studies.

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

Landfast ice is an integral component of the coastal ecosystem. Extracting the edge and mapping the extent of landfast ice are one of the main methods for studying ice changes. In this work, a standardized process for extracting landfast ice edge in the Baltic Sea using the InSAR coherence image is established with Sentinel-1 radar data and InSAR technology. A modified approach combining multiscale segmentation and morphological erosion is then proposed to provide a reliable way to extract landfast ice edge. Firstly, the coherence image is obtained using InSAR technology. Then, the edge is separated and extracted with the modified approach. The modified approach is essentially a four-step procedure involving image segmentation, median filter, morphological erosion, and rejection of small patches. Finally, the full extent of landfast ice can be obtained using floodfill algorithm. Multiple InSAR image pairs of Sentinel-1A acquired from 2018 to 2019 are utilized to successfully extract the landfast ice edge in the Gulf of Bothnia. The results show that the landfast ice edge and the extents obtained by the proposed approach are visually consistent with those shown in the ice chart issued by the Swedish Meteorological and Hydrological Institute (SMHI) over a coastline length of 345 km. The mean distance between land–water boundary and the coastline issued by the National Oceanic and Atmospheric Administration (NOAA) is 109.1 m. The modified approach obviously preserves more details in local edge than the reference method. The experimental results show that the modified approach proposed in this paper can extract the edge and map the extent of landfast ice more accurately and quickly, and is therefore expected to contribute to the further understanding and analyzing the changes of landfast ice in the future.

Understanding Sensitivity of the Soil Moisture Routine Parameters using Integrated Hydrological Modelling System (HBV) in a Small Semi-Arid Agricultural Watershed

Hydrologic processes and their principles are universal. However, the magnitude and impact of hydrological parameters influencing these processes vary along with space and time. Researchers since several decades are in the process of understanding and simulating them. One of such simulation is through the Hydrologiska Byrans Vattenbalansavedlning (HBV) model developed at the Swedish Meteorological and Hydrological Institute (SMHI) Stuttgart, Germany. This work explains how this model will perform in semi-arid conditions to understand the most sensitive parameter influencing soil moisture, among all and calibrate their optimum values. The watershed chosen is small agricultural watershed in a semi-arid region with fertile soil and limiting water. The study revealed that the “LP” a soil moisture value above which the evapotranspiration reaches its peak is the most sensitive. Thereafter parameters “fc”, “athron”, “ ” “cflux”, “pcalt”, “perc”, and “khq” follow suit. These parameters are sensitive within -10% and β +10% of their optimum values. Other parameters as prec”, “cevpl”, “alfa”, “hq”, “recstep”, “soilstep”, “stf, “uzlo” have new values that are not similar to the original values as recommended. All values have been tested for their one at a 2 time sensitivity using the objective functions values of the root mean square error , BIAS, Relative error, R and Nash Sutcliffe efficiency coefficient. This indicates that the model does perform well in semi-arid conditions to simulate the Soil Moisture Routine of an agricultural watershed. It is recommended that the HVB can be used in semi-arid conditions for successfully simulating the Soil Moisture processes.

«SVEA» — новое шведское национальное научно- исследовательское судно для международного мониторинга окружающей среды

In October 2019 the Swedish University of Agricultural Sciences (SLU) and Swedish Meteorological and Hydrological Institute (SMHI) received new research vessel of new generation, R/V «Svea». She was designed to perform monitoring of fish stocks and environmental situation of the Baltic Sea and surrounding waters. It is designed to perform monitoring of fish stocks and environmental situation of the Baltic sea and surrounding waters. Designing and building were conducted by Norwegian design bureau Skipsteknisk AS together with Spanish shipyard Armon de Vigo in 2017-2019 with taking into account ICES Recommendations N 209 regarding reduction of ship noise. As a result, according to DNV the vessel can be classified as low noise one. It allows to carry out fisheries researches using scientific echo sounder EK-80. Design features and scientific qualities of the vessel are described.

Future projection of winter precipitation over northwest India and associated regions using CORDEX-SA experiments

Precipitation during winter months of December, January, and February over northwest India is important for the production of winter crops as well as for glacial accumulation which goes on to feed major rivers in India with fresh water supply. Winter precipitation over this region is due to the movement of synoptic systems approaching from the Mediterranean known as Western Disturbances (WDs). So far, spatial pattern of winter precipitation over this region has not been studied using different RCMs. The present study evaluates the regional climate model employed in Coordinated Regional Climate Downscaling Experiment in South Asia (CORDEX-South Asia) framework to characterize winter precipitation over northwest India and Karakoram region. There are 16 CORDEX-South Asia experiments with 3 regional climate models (RCM) driven with 16 global climate models (GCM). Precipitation climatology is calculated for different RCM along with their ensemble. All the experiments show a wet bias over the region. The selection of best performing RCM is identified using various statistical analyses. The CORDEX experiment run by EC_EARTH RCM with RCA4 GCM developed by Swedish Meteorological and Hydrological Institute (SMHI) is identified as the best performing model when compared with observation. The change in future seasonal mean precipitation is assessed over northwest India and Karakoram region under two representation concentration pathways (RCP4.5 and RCP8.5) scenarios. A positive change in precipitation is observed over Karakoram region under the two RCP scenarios for near future and far future periods. The magnitude of precipitation change is found to be less over the study area under RCP 8.5 scenario for near future period compared to other scenario and time period.

A Remotely Controlled Sea Level Compensation System for Wave Energy Converters

The working principle of the wave energy converter (WEC) developed at Uppsala University (UU) is based on a heaving point absorber with a linear generator. The generator is placed on the seafloor and is connected via a steel wire to a buoy floating on the surface of the sea. The generator produces optimal power when the translator's oscillations are centered with respect to the stator. However, due to the tides or other changes in sea level, the translator's oscillations may shift towards the upper or lower limit of the generator's stroke length, resulting in a limited stroke and a consequent reduction in power production. A compensator has been designed and developed in order to keep the generator's translator centered, thus compensating for sea level variations. This paper presents experimental tests of the compensator in a lab environment. The wire adjustments are based on online sea level data obtained from the Swedish Meteorological and Hydrological Institute (SMHI). The objective of the study was to evaluate and optimize the control and communication system of the device. As the device will be self-powered with solar and wave energy, the paper also includes estimations of the power consumption and a control strategy to minimize the energy requirements of the whole system. The application of the device in a location with high tides, such as Wave Hub, was analyzed based on offline tidal data. The results show that the compensator can minimize the negative effects of sea level variations on the power production at the WEC. Although the wave energy concept of UU is used in this study, the developed system is also applicable to other WECs for which the line length between seabed and surface needs to be adjusted.

Multi-Scale Characteristics of Precipitation and Temperature over West Africa Using SMHI-RCA Driven by GCMs under RCP8.5

In this paper, we use simulations from the Swedish Meteorological and Hydrological Institute (SMHI) regional climate model (RCM) version 3.5 (SMHI-RCA3.5) following a multi-GCM boundary forcing approach. The model is run at a horizontal resolution of 50 km on the CORDEX-AFRICA domain. Key characteristics of precipitation, mean temperature and its extremes (minimum and maximum temperature) have been investigated over West Africa and on 3 designed sub-domains, the Sahel (SAH), the Senegal-Gambia (SEN) and the Gulf of Guinea (GOG). The analysis covers a historical period 1981-2005 and two future time slices, an intermediate term (IT) 2031-2055 and a fat term (FT) 2071-2095, under the Representative Concentration Pathways 8.5 (RCP8.5). The regional climate model RCA, forced by the reanalysis ERA-Interim, 6 CMIP5 GCMs and their ensemble, reproduces realistically the climatology of precipitation and temperatures over West Africa. Compared to observed datasets GPCP for precipitation and CRU for temperature, the ensemble outperforms both other GCMs and the verification model (ERA-Interim). The major biases in precipitation are the early onset over the Sahel and the little dry season (LDS), from mid-July to mid-September over the Gulf of Guinea, and a few models either overestimate and/or reflect rather poorly. The strong warming in extreme temperatures (minimum and maximum) combined with the drying mainly over Western Sahel (SEN) found in this study will very likely impact notably a vital sector like agriculture, both during the near and far terms.

Using MERIS data to assess the spatial and temporal variability of phytoplankton in coastal areas

ABSTRACTThis study aims to highlight how satellite data can be used for an improved understanding of ecological processes in a narrow coastal bay. The usefulness of the Medium Resolution Imaging Spectrometer (MERIS) data (2003–2011) as a complement to the in situ monitoring in Himmerfjärden (HF) bay is used as an example that can also be applied to other coastal areas. HF bay is one of the most frequently monitored coastal areas in the world, allowing for a rigorous comparison between satellites and ship-based monitoring data. MERIS data was used for the integration of chlorophyll-a (chl-a) over each waterbody in the HF area, following the national waterbody classification by the Swedish Meteorological and Hydrological Institute (SMHI). Chl-a anomaly maps were produced for the bay and its adjacent areas. The maps could be used to show events with high chl-a, both with natural causes (e.g. a Prymnesium polylepis bloom observed in summer 2008) and of anthropogenic causes (e.g. failure in the local sewage treatment plant resulting in a strong spring bloom in 2006). Anomaly maps thereby allow to scan larger coastal stretches to discriminate areas that may require additional sampling by ship, or to identify areas that do not differ much from the median value of the MERIS time series.

The influence of weather characteristics variability on individual’s travel mode choice in different seasons and regions in Sweden

This paper investigates the influence of weather on the Swedish people’s mode choice decision in different seasons and regions using a long term series of the Swedish National Transport Survey datasets (NTS) and weather data from the Swedish Meteorological and Hydrological Institute (SMHI). The weather data includes mean of daily temperature, amount of rain precipitation and road surface condition. The daily mean temperature is normalised based on each region and season and classified into five categories as ‘very cold’, ‘cold’, ‘normal’, ‘warm’, and ‘very warm’. This normalisation approach enables us to investigate the impact of individual’s heterogeneity in perceiving regional and seasonal variability of temperature. The impacts of these weather indicators’ variability on individual’s mode choice is investigated with multinomial logit models. The results show that the impacts of weather differ in different seasons and different regions. Pedestrians’ perception of variation of temperature differs between those in the northern Sweden and those in the central and southern Sweden. Such perception also differs in summer and in spring and autumn. Similarly, northern Sweden cyclists are more aware of temperature variation than cyclists in the central and southern Sweden in spring and autumn when temperature changes significantly. The influence of temperature variation on motorised modes also varies among seasons and regions. However, the trend is less straightforward than that on non-motorised modes. The findings highlight the importance to incorporate individual and regional unique anticipation and adaptations behaviours within our policy design and infrastructure management.

Jointly Modelling Individual's Daily Activity-travel Time Use and Mode Share by a Nested Multivariate Tobit Model System

Understanding mechanisms underlie the individual's daily time allocations is very important to understand the variability of individual's time-space constraints and to forecast his/her daily activity participation. At most of previous studies, activity time allocation was viewed as allocating a continuous quantity (daily time budget) into multiple discrete alternatives (i.e. various activities and trips to engage with). However, few researches considered the influence of travel time that needs to be spent on reaching the activity location. Moreover, travel time itself is influenced by individuals’ mode choice. This can lead to an over- or under-estimation of particular activity time location. In order to explicitly include the individual's travel time and mode choice considerations in activity time allocation modelling, in this study, a nested multivariate Tobit model is proposed. This proposed model can handle: 1. Corner solution problem (i.e. the present of substantial amount of zero observations); 2. Time allocation trade-offs among different types of activities (which tends to be ignored in previous studies); 3. Travel is treated as a derived demand of activity participation (i.e. travel time and mode share are automatically censored, and are not estimated, if corresponding activity duration is censored). The model is applied on a combined dataset of Swedish national travel survey (NTS) and SMHI (Swedish Meteorological and Hydrological Institute) weather record. Individuals’ work and non-work activity durations, travel time and mode shares are jointly modelled as dependent variables. The influences of time-location characteristics, individual and household socio demographics and weather characteristics on each dependent variable are examined. The estimation results show a strong work and non-work activity time trade-offs due to the individual's time-space constraints. Evidences on a potential positive utility of travel time added on non-work activity time allocation in the Swedish case, are also found. Meanwhile, the results also show a consistent mode choice preference for a given individual. The estimated nested multivariate Tobit model provides a superior prediction, in terms of the deviation of the predicted value against the actual value conditional on the correct prediction regarding censored and non-censored, compared to mutually independent Tobit models. However, the nested multivariate Tobit model does not necessarily have a better prediction for model components regarding non-work related activities.

Sea ice in the Baltic Sea – revisiting BASIS ice, a historical data set covering the period 1960/1961–1978/1979

Abstract. The Baltic Sea is a seasonally ice-covered, marginal sea in central northern Europe. It is an essential waterway connecting highly industrialised countries. Because ship traffic is intermittently hindered by sea ice, the local weather services have been monitoring sea ice conditions for decades. In the present study we revisit a historical monitoring data set, covering the winters 1960/1961 to 1978/1979. This data set, dubbed Data Bank for Baltic Sea Ice and Sea Surface Temperatures (BASIS) ice, is based on hand-drawn maps that were collected and then digitised in 1981 in a joint project of the Finnish Institute of Marine Research (today the Finnish Meteorological Institute (FMI)) and the Swedish Meteorological and Hydrological Institute (SMHI). BASIS ice was designed for storage on punch cards and all ice information is encoded by five digits. This makes the data hard to access. Here we present a post-processed product based on the original five-digit code. Specifically, we convert to standard ice quantities (including information on ice types), which we distribute in the current and free Network Common Data Format (NetCDF). Our post-processed data set will help to assess numerical ice models and provide easy-to-access unique historical reference material for sea ice in the Baltic Sea. In addition we provide statistics showcasing the data quality. The website http://www.baltic-ocean.org hosts the post-processed data and the conversion code. The data are also archived at the Data Publisher for Earth &amp;amp; Environmental Science, PANGAEA (doi:10.1594/PANGAEA.832353).

Calculation of the far range atmospheric transport of radionuclides after the Fukushima accident with the atmospheric dispersion model MATCH of the JRODOS system

The paper presents estimates of the far-range atmospheric dispersion of radionuclides after the accident at Fukushima Daiichi Nuclear Power Plant (NPP), obtained using the long-range atmospheric dispersion model MATCH. Software tools were developed to run MATCH in the EU nuclear emergency response system JRODOS using freely available numerical weather prediction (NWP) data of the Global Forecasting System (GFS) operated by the United States National Center of Environmental Prediction (NCEP). Comparisons are made of results with JRODOS/MATCH and a standalone MATCH operated by Swedish Meteorological and Hydrological Institute (SMHI) driven by the European Center for Medium-Range Weather Forecasts (ECMWF) NWP data.

Reliability Assessment of the Water Supply Systems under Uncertain Future Extreme Climate Conditions

Abstract Increase in global mean temperature and changes in rainfall amount, pattern, and distribution over the world are all indicative of climate change events. These changes alter the hydroclimatic condition of regions as well as the availability of water resources. In this study, the data generated by 14 general circulation models (GCMs) developed under the Special Report on Emissions Scenarios (SRES) A1B, A2, and B2 are downscaled and utilized to evaluate climate change impact on the hydroclimatic system of the Karaj River basin located in central Iran. The precipitation and temperature of the study region are downscaled using the change factor approach (CFA). The study analyzes future climate data, extreme changes of future climatic conditions of precipitation, and temperature. The Hydrologiska Byråns Vattenbalansavdelning (HBV) model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is used to simulate streamflow under extreme climate change conditions. Two different sources of uncertainty are investigated in this study. First, the model parameters uncertainty is analyzed with the Monte Carlo procedure, and then different datasets of GCMs projection are investigated under the climate of the twentieth-century climate simulation (20C3M). Results show the GCMs projections range can almost capture the historical records during the 1980s through 2000 for the Karaj basin. By applying the HBV model, considerable range of streamflow changes in the future can be projected that will affect the operation scheme of Karaj Reservoir. In this study, the system dynamics (SD) modeling approach is used to simulate the system behavior through time in an integrated fashion and evaluate its overall reliability in supplying water. The results of this study show that the runoff will decrease in the future under the climate change impact. This will result in more than 50% decrease in reliability of the Karaj Reservoir system under the extreme conditions. As a result, this research predicts that the Karaj Reservoir system will face more than 50% decrease in its reliability under the extreme conditions. Consequently, meeting the increasing water demands would be difficult and application of demand management strategies will be unavoidable.

76 Investigation of the correlation between various air pollution metrics and effects on asthma; a time series study in southern Sweden

IntroductionDifferent metrics of daily levels of particulate matter were used to study the association between air pollution and primary health care visits of asthma sufferers in an area with air...

Regional climate model assessment using statistical upscaling and downscaling techniques

Climate models are mathematical models that describe the temporal evolution of climate, oceans, atmosphere, ice, and land‐use processes, across a spatial domain via systems of partial differential equations. Because these models cannot be solved analytically, the model output is generated numerically over grid boxes. Regional climate models (RCMs), or the dynamic downscaling of global climate models to regional scales, are often used for planning purposes, and it is important to assess carefully the uncertainty of such models. We evaluate the Swedish Meteorological and Hydrological Institute (SMHI) RCM by comparing its model output at the grid box level, with the predictions obtained from two observation‐driven spatio‐temporal statistical models. The “downscaling model” combines the spatially and temporally smoothed climate model output with temperature observations at synoptic stations in a spatio‐temporal linear statistical model. The “upscaling model” describes the observational temperature alone at the daily scale, via a spatio‐temporal model that includes a wavelet‐based trend, spatially varying seasonality, along with volatility and long‐range dependence terms. Both statistical models have the ability to make predictions at a seasonal scale, both at point and grid box level. In the years 1962–2007 in South Central Sweden, we show that the climate model performs well in predicting the annual and seasonal average temperature at three reserved stations, but there are interesting differences among the model output and the statistical model‐based predictions at the grid box level. Copyright © 2012 John Wiley &amp; Sons, Ltd.