A gridded dataset (SMHI Gridded Climatology - SMHIGridClim) has been produced forthe years 1961 - 2018 over an area covering the Nordic countries on a grid with 2.5 kmhorizontal resolution. The variables considered are the two meter temperature and twometer relative humidity on 1, 3 or 6 hour resolution, varying over the time periodcovered, the daily minimum and maximum temperatures, the daily precipitation and thedaily snow depth. The gridding was done using optimal interpolation with the gridppopen source software from the Norwegian Meteorological Institute.Observations for the analysis are provided by the Swedish, Finish and Norwegianmeteorological institutes, and the ECMWF. The ECA&D observation data set (e.g. usedfor the gridded E-OBS dataset) was considered for inclusion but was left out because ofcomplications with time stamps and accumulation periods varying between countries andperiods. Quality check of the observations was performed using the open source softwareTITAN, also developed at the Norwegian Meteorological Institute.The first guess to the optimal interpolation was given by statistically downscaledforecasts from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution. Thedownscaling was done to fit the output from the operational MEPS NWP system at 2.5km with a daily and yearly variation in the downscaling parameters.The quality of the SMHIGridClim dataset, in terms of annual mean RMSE, was shown tobe similar to that of gridded datasets covering the other Nordic countries; “seNorge”from Norway and the dataset “FMI_ClimGrid” from Finland.

An experimental comparison of spectral aerosol optical depth tau(a,lambda) derived from measurements by two spectral radiometers [a LI-COR, Inc., LI-1800 spectroradiometer and a Centre Suisse d'Electronique et de Microtechnique (CSEM) SPM2000 sun photometer] and a broadband field pyrheliometer has been made. The study was limited to three wavelengths ( 368, 500, and 778 nm), using operational calibration and optical depth calculation procedures. For measurements taken on 32 days spread over 1 yr, the rms difference in tau(a,lambda) derived from the two spectral radiometers was less than 0.01 at 500 and 778 nm. For wavelengths shorter than 500 nm and longer than 950 nm, the performance of the LI-1800 in its current configuration did not permit accurate determinations of tau(a,lambda). Estimates of spectral aerosol optical depth from broadband pyrheliometer measurements using two models of the Angstromngstrom turbidity coefficient were examined. For the broadband method that was closest to the sun photometer results, the mean (rms) differences in tau(a,lambda) were 0.014 (0.028), 0.014 (0.019), and 0.013 ( 0.014) at 368, 500, and 778 nm. The mean differences are just above the average uncertainties of the sun photometer tau(a,lambda) values (0.012, 0.011, and 0.011) for the same wavelengths, as determined through a detailed uncertainty analysis. The amount of atmospheric water vapor is a necessary input to the broadband methods. If upper-air sounding data are not available, water vapor from a meteorological forecast model yields significantly better turbidity results than does using estimates from surface measurements of air temperature and relative humidity.

A novel dealiasing algorithm for Doppler radar velocity data has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). Unlike most other methods, it does not need independent wind information from other instruments (e.g., nearby radiosonde or wind profiler) or numerical weather prediction (NWP) models. The innovation of the new technique is that it maps the measurements onto the surface of a torus. Dealiased volume radar data can be used in variational assimilation schemes for NWP models through the generation of so-called superobservations. Their use is expected to improve with the introduction of the proposed dealiasing method.

Ten years of measurements of UV irradiance, monitored by the Robertson-Berger (RB) meter in Norrkoping, 58.58 degrees N, 16.15 degrees E, Sweden, have been combined with concurrent synoptic cloud observations, measurements of sunshine duration, and global radiation to establish the relative influence of clouds on UV irradiance. It is shown that the cloud effect for UV wavelengths is less than for the whole solar spectrum (global radiation). Relations retrieved for global radiation may be used by correcting for the differences. High-level clouds are more transparent than low- and medium-level clouds. As expected, it was found that precipitating clouds in general are more opaque than nonprecipitating clouds. If there is any solar elevation dependency in the effect of clouds, it is small. Using only total cloud amount as parameter to model, the cloud effect on UV irradiance will give a substantial uncertainty, which can be decreased considerably using cloud type and/or information on precipitation conditions. It has also been shown that sunshine duration can be used in a similar way as cloud covet.

The emissions of carbon dioxide (CO2) from inland waters are substantial on a global scale. Yet the fundamental question remains open which proportion of these CO2 emissions is induced by sunlight via photochemical mineralization of dissolved organic carbon (DOC), rather than by microbial respiration during DOC decomposition. Also, it is unknown on larger spatial and temporal scales how photochemical mineralization compares to other C fluxes in the inland water C cycle. We combined field and laboratory data with atmospheric radiative transfer modeling to parameterize a photochemical rate model for each day of the year 2009, for 1086 lakes situated between latitudes from 55 degrees N to 69 degrees N in Sweden. The sunlight-induced production of dissolved inorganic carbon (DIC) averaged 3.8 +/- 0.04 g C m(-2) yr(-1), which is a flux comparable in size to the organic carbon burial in the lake sediments. Countrywide, 151 +/- 1 kt C yr(-1) was produced by photochemical mineralization, corresponding to about 12% of total annual mean CO2 emissions from Swedish lakes. With a median depth of 3.2m, the lakes were generally deep enough that incoming, photochemically active photons were absorbed in the water column. This resulted in a linear positive relationship between DIC photoproduction and the incoming photon flux, which corresponds to the absorbed photons. Therefore, the slope of the regression line represents the wavelength-and depth-integrated apparent quantum yield of DIC photoproduction. We used this relationship to obtain a first estimate of DIC photoproduction in lakes and reservoirs worldwide. Global DIC photoproduction amounted to 13 and 35 Mt C yr(-1) under overcast and clear sky, respectively. Consequently, these directly sunlight-induced CO2 emissions contribute up to about one tenth to the global CO2 emissions from lakes and reservoirs, corroborating that microbial respiration contributes a substantially larger share than formerly thought, and generate annual C fluxes similar in magnitude to the C burial in natural lake sediments worldwide.

Irradiance measurements on a horizontal surface often deviate from theory where the irradiance is supposed to be proportional to the cosine of the angle of incidence. This discrepancy is known as the cosine error. In this paper, three different methods for cosine error correction are investigated. The simplest method is based on the assumption of an isotropic sky radiance distribution, regardless of sky conditions, and the irradiance is treated as a single component. In the second method the irradiance is divided into one direct solar and one diffuse sky component, where the latter is assumed to have an isotropic distribution. Finally, a third method also divides the irradiance into two components but under the assumption of an anisotropic sky radiance distribution. Irradiances under general sky conditions are found by interpolation between clear and overcast cases on the basis of sunshine duration or cloud cover. The three methods are applied to data from a Robertson-Berger sunburning meter located in Norrkoping, Sweden. Both methods, where the irradiance is divided into two components, produce acceptable and similar results, while the isotropic one-component method does not.

Today, modern analysis systems synthesise meteorological data from a number of sources, e.g.\ round based SYNOP, satellites, radar, etc., into field information which enable us to model radiation at the Earth’s surface on the mesoscale. At the Swedish Meteorological and Hydrological Institute (SMHI) we have set up a model system that produce hourly information in terms of field data with a resolution of about 22 ´ 22 km2 for a geographic area covering Scandinavia and the run off region of the Baltic sea.Presently, the model calculates fields of global-, photosynthetically active- (PAR), UV- and direct radiation based on output from a mesoscale analysis system, a high resolution limited area numerical weather prediction model (NWP), an ice model for the Baltic sea together with satellite measurements of total ozone. A spectral clear sky model lies at the heart of the model system. Its output is multiplied by a function which captures the influence of clouds and precipitation. Different cloud effect functions are applied to the different radiation components, with the exception of global- and PAR for which the same relation is assumed.Measurements from the radiation network of SMHI were used for estimation and validation purposes. A first evaluation of the model system suggests that the RMSE for hourly global radiation data is on the order of 28% and about 16% for daily values. These errors are comparable to those obtained for models purely based on synoptic observations (SYNOP) (29% and 13%) . For UV radiation the figures are similar but for the direct radiation component they are worse; 53% and 31% respectively compared to 25% and 15% for the SYNOP models. To some extent the larger errors for the direct component could be explained by its sensitivity to scale differences when model grid squares are validated against point measurements.

The estimation of surface rainfall from reflectivity data derived from weather radar has been much studied over many years. It is now clear that central to this problem is the adjustment of these data for the impacts of vertical variations in the reflectivity. In this paper a new procedure (known as Down-to-Earth, DTE) is proposed and tested for combining radar measurements aloft with information from a numerical weather-prediction (NWP) model and an analysis system. The procedure involves the exploitation of moist cloud physics in an attempt to account for physical processes impacting on precipitation during its descent from the height of radar echo measurements to the surface. The application of DTE leads to increased underestimation in the radar measurements compared to precipitation gauge observations at short and intermediate radar ranges (0-120 km), but is successful at reducing the bias at further ranges. However the application of DTE does not lead to significant decreases in the random error of the surface rain rate estimate. No improvement is made when attempting to account for the precipitation phase measured by radar. It is concluded that further work on radar data quality control, along with improvements to the NWP model, are essential to improve upon results using such a physically based procedure.

Atmospheric state variables from a Numerical Weather Prediction (NWP) model are combined with analyzed cloud base heights in a neural network, with the objective to model corresponding cloud water profiles. It was found that the neural network was incapable of resolving the inherently non-linear vertical cloud water distributions. Copyright (C) 2004 Royal Meteorological Society

Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system's yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5-6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets.