Concentrations of NO2, PM10 and PM2.5 have been calculated for the whole of Sweden for the year 2019 as well as two scenarios for 2030 in this study. Calculations have been performed using a new methodology, allowing almost seam-less combination of dispersion modelling at regional and urban scale without double-counting emissions. The concentrations have been calculated at 250x250 m2 resolution, producing a uniquely complete and detailed dataset at national scale. The  methodology used can well reproduce the measured pollution levels at most urban background stations in the modelling domain. The spatial resolution of 250 m captures concentration gradients that are of importance for exposure calculations. An important strength of using dispersion modelling to calculate concentrations is the direct relation with emission inventories, allowing for source attribution and scenario evaluation that is consistent with emission inventories and projections. 

The modelled concentrations are used together with gridded population data in order to calculate exposure. The annual average population weighted exposure is 5.08 µg/m3 for NO2, 9.95 µg/m3 for PM10 and 5.21 µg/m3 for PM2.5 in 2019. A large decrease, by approximately 2 µg/m3, is seen for exposure to NO2 in 2030 compared to 2019. The exposure to PM10 and PM2.5 is also decreasing in 2030, but not as drastically, by about 0.2 µg/m3.   

A general conclusion is that exposure is higher in the age span of 21-50 years. An explanation is that these age groups more often live in urban areas, where there are more emissions and higher concentrations of pollution.    

Zero percent of the population is exposed to levels above the annual air quality standards for NO2, PM10 and PM2.5 for 2019 and 2030.  It is to be noted that the model results represent annual averaged urban background concentrations, not local hotspot concentrations. 

The modelled exposures to PM2.5 and urban NO2 have been used for a national health impact assessment. The health impact assessment is similar to an earlier study of premature deaths and incident cases of mainly chronic diseases. Our results differ to a varying degree from similar impact assessments. Most important among the complicated reasons for differences in the estimated health impacts are the assumed exposure-response functions for the specific exposures, the slope and if there is a lower threshold below which no association exists. We have in this study decided to follow the strong evidence from high quality epidemiological studies that the exposure-response relationship between long-term exposure to PM2.5 and total mortality in adults is supra-linear with a much steeper slope at the lower end, with stronger effects of near source exposure, and no evidence of a threshold level below which no effects are observed. When adding the yearly number of premature deaths attributed to the regional background PM2.5 levels and the deaths associated with PM2.5 exposure from local sources, the total number becomes 4 264 deaths related to the fine particle exposure situation in 2019. At the same time, the urban contribution of NO2 is estimated to result in additional 428 premature deaths per year. 

In 2030 the population exposure to PM2.5 from the regional background is expected to be about 2% lower and from urban sources 22% lower compared to 2019, which indicates how much the attributed number of preterm deaths would change if everything else stays the same. 

A case with intense convective snowbands over the Baltic Sea is examined using the High-Resolution Limited Area Model. The intention is to gain a better insight into the importance of the shape of the cowta, the orography, and the surface roughness on the formation and evolution of the snowbands. Among the factors studied am the shape of the coast from which the air departs and that to which it arrives. These factors are so important that two new concepts-coast of departure and coast of arrival-are introduced.

Although the links between stratospheric dynamics, climate and weather have been demonstrated, direct observations of stratospheric winds are lacking, in particular at altitudes above 30 km. We report observations of winds between 8 and 0.01 hPa (similar to 35-80 km) from October 2009 to April 2010 by the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the International Space Station. The altitude range covers the region between 35-60 km where previous space-borne wind instruments show a lack of sensitivity. Both zonal and meridional wind components were obtained, though not simultaneously, in the latitude range from 30 degrees S to 55 degrees N and with a single profile precision of 7-9 ms(-1) between 8 and 0.6 hPa and better than 20 ms(-1) at altitudes above. The vertical resolution is 5-7 km except in the upper part of the retrieval range (10 km at 0.01 hPa). In the region between 1-0.05 hPa, an absolute value of the mean difference <2 ms(-1) is found between SMILES profiles retrieved from different spectroscopic lines and instrumental settings. Good agreement (absolute value of the mean difference of similar to 2 ms(-1)) is also found with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis in most of the stratosphere except for the zonal winds over the equator (difference >5 ms(-1)). In the mesosphere, SMILES and ECMWF zonal winds exhibit large differences (>20 ms(-1)), especially in the tropics. We illustrate our results by showing daily and monthly zonal wind variations, namely the semi-annual oscillation in the tropics and reversals of the flow direction between 50-55 degrees N during sudden stratospheric warmings. The daily comparison with ECMWF winds reveals that in the beginning of February, a significantly stronger zonal westward flow is measured in the tropics at 2 hPa compared to the flow computed in the analysis (difference of similar to 20 ms(-1)). The results show that the comparison between SMILES and ECMWF winds is not only relevant for the quality assessment of the new SMILES winds, but it also provides insights on the quality of the ECMWF winds themselves. Although the instrument was not specifically designed for measuring winds, the results demonstrate that space-borne sub-mm wave radiometers have the potential to provide good quality data for improving the stratospheric winds in atmospheric models.

Winds measured by lidar from the Aeolus satellite are compared with winds measured by two ground-based radars - MARA in Antarctica (70.77 degrees S, 11.73 degrees E) and ES-RAD (67.88 degrees N, 21.10 degrees E) in Arctic Sweden - for the period 1 July-31 December 2019. Aeolus is a demonstrator mission to test whether winds measured by Doppler lidar from space can have sufficient accuracy to contribute to improved weather forecasting. A comprehensive programme of calibration and validation has been undertaken following the satellite launch in 2018, but, so far, direct comparison with independent measurements from the Arctic or Antarctic regions have not been made. The comparison covers heights from the low troposphere to just above the tropopause. Results for each radar site are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds, sunlit ("summer") and non-sunlit ("winter") seasons, and ascending and descending satellite tracks. Horizontally projected line-of-sight (HLOS) winds from Aeolus, reprocessed using baseline 2B10, for passes within 100 km of the radar sites, are compared with HLOS winds calculated from 1 h averaged radar horizontal wind components. The agreement in most data subsets is very good, with no evidence of significant biases (<1ms(-1)). Possible biases are identified for two subsets (about -2ms(-1) for the Rayleigh winds for the descending passes at MARA and about 2ms(-1) for the Mie winds for the ascending passes at ESRAD, both in winter), but these are only marginally significant. A robust significant bias of about 7ms(-1) is found for the Mie winds for the ascending tracks at MARA in summer. There is also some evidence for increased random error (by about 1ms(-1) / for the Aeolus Mie winds at MARA in summer compared to winter. This might be related to the presence of sunlight scatter over the whole of Antarctica as Aeolus transits across it during summer.

Sweden was a pioneering country in the development of NumericalWeather Prediction (NWP). The worlds first operational numerical forecast was produced already in 1954 by the International Meteorological Institute in Stockholm. SMHI started a bit later, but in 1961 a long term program for development of NWP was initiated. The activities grew gradually during the 1960’s and resulted in a core component for the SMHI forecast services. An early challenge was to overcome the limited computational resources with slow computational speed, small memory size and primitive software support. It was necessary to compensate for these limitations with dedicated work and creativity. A core component in this work was the software system MAC (Meteorological Auto Code) that was developed by the NWP group at SMHI. The MAC system is described in detail in this report and it included all computational software needed for the weather service, for example numerical models, objective analysis techniques, automatic data extraction, quality control of observations as well as forecast products in graphical or digital form.

We hope that this report will provide the younger generation with some insight into the conditions for development of NWP during the 1960’s.

A stochastic parametrization for deep convection, based on cellular automata, has been evaluated in the high-resolution (2.5 km) ensemble prediction system Hirlam Aladin Regional Mesoscale Operational NWP Ensemble Prediction System (HarmonEPS). We studied whether such a stochastic physical parametrization, whilst implemented in a deterministic forecast model, can have an impact on the performance of the uncertainty estimates given by an ensemble prediction system. Various feedback mechanisms in the parametrization were studied with respect to ensemble spread and skill, in both subgrid and resolved precipitation fields. It was found that the stochastic parametrization improves the model skill in general, by reducing a positive bias in precipitation. This reduction in bias, however, led to a reduction in ensemble spread of precipitation. Overall, scores that measure the accuracy and reliability of probabilistic predictions indicate that the net impact (improved skill, degraded spread) of the ensemble prediction system is improved for 6 h accumulated precipitation with the stochastic parametrization and is rather neutral for other quantities examined.

Because of the limited resolution of numerical weather prediction (NWP) models, subgrid-scale physical processes are parameterized and represented by gridbox means. However, some physical processes are better represented by a mean and its variance; a typical example is deep convection, with scales varying from individual updrafts to organized mesoscale systems. This study investigates, in an idealized setting, whether a cellular automaton (CA) can be used to enhance subgrid-scale organization by forming clusters representative of the convective scales and thus yield a stochastic representation of subgrid-scale variability. The authors study the transfer of energy from the convective to the larger atmospheric scales through nonlinear wave interactions. This is done using a shallow water (SW) model initialized with equatorial wave modes. By letting a CA act on a finer resolution than that of the SW model, it can be expected to mimic the effect of, for instance, gravity wave propagation on convective organization. Employing the CA scheme permits the reproduction of the observed behavior of slowing down equatorial Kelvin modes in convectively active regions, while random perturbations fail to feed back on the large-scale flow. The analysis of kinetic energy spectra demonstrates that the CA subgrid scheme introduces energy backscatter from the smallest model scales to medium scales. However, the amount of energy backscattered depends almost solely on the memory time scale introduced to the subgrid scheme, whereas any variation in spatial scales generated does not influence the energy spectra markedly.

One desirable property within an ensemble forecast system is to have a one-to-one ratio between the root-mean-square error (rmse) of the ensemble mean and the standard deviation of the ensemble (spread). The ensemble spread and forecast error within the ECMWF ensemble prediction system has been extrapolated beyond 10 forecast days using a simple model for error growth. The behavior of the ensemble spread and the rmse at the time of the deterministic predictability are compared with derived relations of rmse at the infinite forecast length and the characteristic variability of the atmosphere in the limit of deterministic predictability. Utilizing this methodology suggests that the forecast model and the atmosphere do not have the same variability, which raises the question of how to obtain a perfect ensemble.

A cellular automaton (CA) is introduced to the deep convection parametrization of the high-resolution limited-area model Aire Limitee Adaptation/Application de la Recherche a l'Operationnel (ALARO). The self-organizational characteristics of the CA allow for lateral communication between adjacent numerical weather prediction (NWP) model grid boxes and add additional memory to the deep convection scheme. The CA acts in two horizontal dimensions, with finer grid spacing than the NWP model. It is randomly seeded in regions where convective available potential energy (CAPE) exceeds a threshold value. Both deterministic and probabilistic rules, coupled to the large-scale wind, are explored to evolve the CA in time. Case studies indicate that the scheme has the potential to organize cells along convective squall lines and enhance advective effects. An ensemble of forecasts using the present CA scheme demonstrated an ensemble spread in the resolved wind field in regions where deep convection is large. Such a spread represents the uncertainty due to subgrid variability of deep convection and could be an interesting addition to an ensemble prediction system.

Horizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1-3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

The paper deals with the comparison of the most common periodization methods used to obtain spectral fields of limited-area models for numerical weather prediction. The focus is on the impact that the methods have on the spectra of the fields, which are used for verification and tuning of the models. A simplified model is applied with random fields that obey a known kinetic energy spectrum. The periodization methods under consideration are detrending, the discrete cosine transform and the application of an extension zone. For the extension zone, three versions are applied: the Boyd method, the ALADIN method and the HIRLAM method. The results show that detrending and the discrete cosine transform have little impact on the spectra, as does the Boyd method for extension zone. For the ALADIN and HIRLAM methods, the impact depends on the width of the extension zone - the wider the zone, the more artificial energy and the larger impact on the spectra. The width of the extension zone correlates to the modifications in the shape of the spectra as well as to the amplitudes of the additional energy in the spectra.

The ETKF rescaling scheme has been implemented into the HIRLAM forecasting system in order to estimate the uncertainty of the model state. The main purpose is to utilize this uncertainty information for modelling of flow-dependent background error covariances within the framework of a hybrid variational ensemble data assimilation scheme. The effects of rank-deficiency in the ETKF formulation is explained and the need for variance inflation as a way to compensate for these effects is justified. A filter spin-up algorithm is proposed as a refinement of the variance inflation. The proposed spin-up algorithm will also act to prevent ensemble collapse since the ensemble will receive 'fresh blood' in the form of additional perturbation components, generated on the basis of a static background error covariance matrix. The resulting ETKF-based ensemble perturbations are compared with ensemble perturbations based on targeted singular vectors and are shown to have more realistic spectral characteristics.

This article focuses on computations of low frequency sound propagation from an off-shore wind farm. Two different methods for sound propagation calculations are combined with meteorological data for every 3 hours in the year 2010 to examine the varying noise levels at a reception point at 13 km distance. It is shown that sound propagation conditions play a vital role in the noise impact from the off-shore wind farm and ordinary assessment methods can become inaccurate at longer propagation distances over water. Therefore, this paper suggests that methodologies to calculate noise immission with realistic sound speed profiles need to be combined with meteorological data over extended time periods to evaluate the impact of low frequency noise from modern off-shore wind farms. (C) 2014 Acoustical Society of America.

When using climate data for various applications, users are confronted with the difficulty to assess the uncertainties of the data. For both in-situ and remote sensing data the issues of representativeness, homogeneity, and coverage have to be considered for the past, and their respective change over time has to be considered for any interpretation of trends. A synthesis of observations can be obtained by employing data assimilation with numerical weather prediction (NWP) models resulting in a meteorological reanalysis. Global reanalyses can be used as boundary conditions for regional reanalyses (RRAs), which run in a limited area (Europe in our case) with higher spatial and temporal resolution, and allow for assimilation of more regionally representative observations. With the spatially highly resolved RRAs, which exhibit smaller scale information, a more realistic representation of extreme events (e.g. of precipitation) compared to global reanalyses is aimed for. In this study, we discuss different methods for quantifying the uncertainty of the RRAs to answer the question to which extent the smaller scale information (or resulting statistics) provided by the RRAs can be relied on. Within the European Union's seventh Framework Programme (EU FP7) project Uncertainties in Ensembles of Regional Re-Analyses (UERRA) ensembles of RRAs (both multi-model and single model ensembles) are produced and their uncertainties are quantified. Here we explore the following methods for characterizing the uncertainties of the RRAs: (A) analyzing the feedback statistics of the assimilation systems, (B) validation against station measurements and (C) grids derived thereof, and (D) against gridded satellite data products. The RRA ensembles (E) provide the opportunity to derive ensemble scores like ensemble spread and other special probabilistic skill scores. Finally, user applications (F) are considered. The various methods are related to user questions they can help to answer.

In order to improve the accuracy of forecasting near-surface atmospheric variables over a heterogeneous landscape, a framework of subgrid surface types and the ISBA parameterisation scheme for land surfaces have been tested in the operational weather forecast model HIRLAM, using a 5.5 km grid resolution. Surface energy fluxes measured during a single summer day at six fixed sites in the NOPEX area, representing agricultural fields, boreal forests and lakes, were used for verification. Both, in-situ field measurements and the HIRLAM simulation indicated that the Bowen ratio over forests was about twice as large as that of adjacent agricultural fields. This difference could be explained by the more effective turbulent mixing and larger surface resistance associated with the forest, thus making the sensible heat flux relatively large there. The use of initial soil moisture from a routine hydrological model gave improved agreement with measured surface fluxes and radiosonde temperature and humidity profiles compared to initialising from routine HIRLAM surface data. The differences in heat fluxes between the various surface types were also demonstrated by airborne flux measurements flown along a track at a height of ca. 100 m above the terrain. Modelled heat fluxes along the flight track were considerably smoothed due to the grid resolution used, e.g. the effect of a lake in reducing grid-averaged sensible heat flux could only be weakly detected, because the lake surface represented only 10% of the grid area. When the proportion of a contrasting surface type (lake) was altered from 10 to 100%, the surface fluxes calculated for the lake surface were almost unchanged; the results of the comparison did not provide evidence that more complex aggregation schemes for heat fluxes than straightforward area-weighted averaging would be required. The hourly variation of the modelled and simulated heat fluxes during the day studied could not be directly compared, because the simulated cloudiness did not exactly match that observed at the field sites. When the simulated net radiation was replaced with direct measurements, the model-based estimates of sensible and latent heat fluxes were closer to the corresponding field measurements. The divergence of sensible heat flux with height, as inferred from the tower measurements made over the forest, were supported by the aircraft measurements and the HIRLAM simulations. (C) 1999 Elsevier Science B.V. All rights reserved.

This document describes the CF conventions for climate and forecast metadata designed to promotethe processing and sharing of files created with the netCDF Application Programmer Interface[NetCDF]. The conventions define metadata that provide a definitive description of what the data ineach variable represents, and of the spatial and temporal properties of the data. This enables usersof data from different sources to decide which quantities are comparable, and facilitates buildingapplications with powerful extraction, regridding, and display capabilities.The CF conventions generalize and extend the COARDS conventions [COARDS]. The extensionsinclude metadata that provides a precise definition of each variable via specification of a standardname, describes the vertical locations corresponding to dimensionless vertical coordinate values,and provides the spatial coordinates of non-rectilinear gridded data. Since climate and forecastdata are often not simply representative of points in space/time, other extensions provide for thedescription of coordinate intervals, multidimensional cells and climatological time coordinates, andindicate how a data value is representative of an interval or cell. This standard also relaxes theCOARDS constraints on dimension order and specifies methods for reducing the size of datasets.

This document describes the CF conventions for climate and forecast metadata designed to promotethe processing and sharing of files created with the netCDF Application Programmer Interface[NetCDF]. The conventions define metadata that provide a definitive description of what the data ineach variable represents, and of the spatial and temporal properties of the data. This enables usersof data from different sources to decide which quantities are comparable, and facilitates buildingapplications with powerful extraction, regridding, and display capabilities.The CF conventions generalize and extend the COARDS conventions [COARDS]. The extensionsinclude metadata that provides a precise definition of each variable via specification of a standardname, describes the vertical locations corresponding to dimensionless vertical coordinate values,and provides the spatial coordinates of non-rectilinear gridded data. Since climate and forecastdata are often not simply representative of points in space/time, other extensions provide for thedescription of coordinate intervals, multidimensional cells and climatological time coordinates, andindicate how a data value is representative of an interval or cell. This standard also relaxes theCOARDS constraints on dimension order and specifies methods for reducing the size of datasets.

The Barents Oscillation (BO) is an anomalous wintertime atmospheric circulation pattern in the Northern Hemisphere that has been linked to the meridional flow over the Nordic Seas. There are speculations that the BO has important implications for the Arctic climate; however, it has also been suggested that the pattern is an artifact of Empirical Orthogonal Function (EOF) analysis due to an eastward shift of the Arctic Oscillation/North Atlantic Oscillation (AO/NAO). In this study, EOF analyses are performed to show that a robust pattern resembling the BO can be found during different time periods, even when the AO/NAO is relatively stationary. This BO has a high and stable temporal correlation with the geostrophic zonal wind over the Barents Sea, while the contribution from the AO/NAO is small. The surface air temperature anomalies over the Barents Sea are closely associated with this mode of climate variability.

The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, having a significant impact on medium-range weather forecasts and climate integrations. To pursue this further, a moderately stratified Arctic case is simulated by nineteen single-column turbulence schemes. Statistics from a large-eddy simulation intercomparison made for the same case by eleven different models are used as a guiding reference. The single-column parameterizations include research and operational schemes from major forecast and climate research centres. Results from first-order schemes, a large number of turbulence kinetic energy closures, and other models were used. There is a large spread in the results; in general, the operational schemes mix over a deeper layer than the research schemes, and the turbulence kinetic energy and other higher-order closures give results closer to the statistics obtained from the large-eddy simulations. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored.

We propose to add an extra source of information to the data-assimilation of the regional HIgh Resolution Limited Area Model (HIRLAM) model, constraining larger scales to the host model providing the lateral boundary conditions. An extra term, J(k), measuring the distance to the large-scale vorticity of the host model, is added to the cost-function of the variational data-assimilation. Vorticity is chosen because it is a good representative of the large-scale flow and because vorticity is a basic control variable of the HIRLAM variational data-assimilation. Furthermore, by choosing only vorticity, the remaining model variables, divergence, temperature, surface pressure and specific humidity will be allowed to adapt to the modified vorticity field in accordance with the internal balance constraints of the regional model. The error characteristics of the J(k) term are described by the horizontal spectral densities and the vertical eigenmodes (eigenvectors and eigenvalues) of the host model vorticity forecast error fields, expressed in the regional model geometry. The vorticity field, provided by the European Centre for Medium-range Weather Forecasts (ECMWF) operational model, was assimilated into the HIRLAM model during an experiment period of 33 d in winter with positive impact on forecast verification statistics for upper air variables and mean sea level pressure.

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright (C) 2011 Royal Meteorological Society