Helsinki, Finland

The Finnish Meteorological Institute is the government agency responsible for gathering and reporting weather data and forecasts in Finland. It is a part of the Ministry of Transport and Communications but it operates semi-autonomously. The Institute is an impartial research and service organisation with expertise covering a wide range of atmospheric science activities other than gathering and reporting weather data and forecasts. The headquarters of the Institute is in Kumpula Campus, Helsinki, Finland. Wikipedia.


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VTT Technical Research Centre of Finland is coordinating the 5G-Safe project, which aims to reduce traffic accidents. This involves the development of new vehicular network solutions and the local road weather and safety services they enable, in support of drivers, road operators and autonomous vehicle management systems. The new services will require no action from motorists while driving - data will be gathered and warnings will be sent to users automatically. "The wide introduction of real-time services, based on sensor and video data collected from vehicles, is being made possible by next-generation 5G mobile network technology and new solutions supporting optimal data collection and exchange," says Tiia Ojanperä, a project manager from VTT. "5G will form the cornerstone of interaction between robot cars, for example. Finnish ICT firms have major export potential in this area. Contemporary driver support systems are mainly vision-based, relying on signals generated by the vehicle's sensors. 5G and short-range radios will also bring the power of speech and hearing to vehicles, taking their capabilities to a new level," states Ojanperä. The services currently being developed require no action during driving in order to send data or warnings. Instead, the prevailing local weather and road conditions are automatically identified based on data collected from vehicles. Warnings and other useful information are sent in real-time to road users, road operators and autonomous vehicle control systems. The new network and cloud computing technologies being researched under the project will reduce delays in data exchange and be more scalable than current services. The 5G-Safe project, which is part of Tekes' Challenge Finland competition, is focused on the identification of local weather and road conditions on the basis of data collected from vehicles, and the sending of warnings to road users. In addition, real-time video and radar data will be exchanged between passing vehicles. Other issues being investigated include the use of data on local road weather conditions to improve the situational awareness of autonomous vehicles and the enhancement of autonomous operation in harsh weather. New business is being sought for the participating companies via this project, which began recently and will end in 2018. The private-sector partners have been actively involved in defining the project's content from the beginning, which improves the prospects of commercialising the results. In addition to VTT, the research partners include Destia, the Finnish Meteorological Institute and its commercial services, Kaltio Technologies Oy, SITO, Tieto and Unikie. Nokia, Sunit and VR Transpoint are participating as sponsors. Support is also being provided by a technical expert group including Bittium, Dynniq (the Netherlands), the Finnish Transport Agency, Telia and the Finnish Transport Safety Agency (Trafi).


VTT Technical Research Centre of Finland is coordinating the 5G-Safe project, which aims to reduce traffic accidents. This involves the development of new vehicular network solutions and the local road weather and safety services they enable in support of drivers, road operators and autonomous vehicle management systems. The new services will require no action from motorists while driving—data will be gathered and warnings will be sent to users automatically. "The wide introduction of real-time services, based on sensor and video data collected from vehicles, is made possible by next-generation 5G mobile network technology and new solutions supporting optimal data collection and exchange," says Tiia Ojanperä, a project manager from VTT. "5G will form the cornerstone of interaction between robot cars, for example. Finnish ICT firms have major export potential in this area. Contemporary driver support systems are mainly vision-based, relying on signals generated by the vehicle's sensors. 5G and short-range radios will also bring the power of speech and hearing to vehicles, taking their capabilities to a new level," states Ojanperä. The services currently being developed require no action during driving in order to send data or warnings. Instead, the prevailing local weather and road conditions are automatically identified based on data collected from vehicles. Warnings and other useful information are sent in real-time to road users, road operators and autonomous vehicle control systems. The new network and cloud computing technologies being researched under the project will reduce delays in data exchange and be more scalable than current services. The 5G-Safe project, which is part of Tekes' Challenge Finland competition, is focused on the identification of local weather and road conditions on the basis of data collected from vehicles, and the sending of warnings to road users. In addition, real-time video and radar data will be exchanged between passing vehicles. Other issues being investigated include the use of data on local road weather conditions to improve the situational awareness of autonomous vehicles and the enhancement of autonomous operation in harsh weather. New business is being sought for the participating companies via this project, which began recently and will end in 2018. The private-sector partners have been actively involved in defining the project's content from the beginning, which improves the prospects of commercialising the results. In addition to VTT, the research partners include Destia, the Finnish Meteorological Institute and its commercial services, Kaltio Technologies Oy, SITO, Tieto and Unikie. Nokia, Sunit and VR Transpoint are participating as sponsors. Support is also being provided by a technical expert group including Bittium, Dynniq (the Netherlands), the Finnish Transport Agency, Telia and the Finnish Transport Safety Agency (Trafi). Explore further: Better products and services for winter maintenance and traffic safety


Vihma T.,Finnish Meteorological Institute
Surveys in Geophysics | Year: 2014

The areal extent, concentration and thickness of sea ice in the Arctic Ocean and adjacent seas have strongly decreased during the recent decades, but cold, snow-rich winters have been common over mid-latitude land areas since 2005. A review is presented on studies addressing the local and remote effects of the sea ice decline on weather and climate. It is evident that the reduction in sea ice cover has increased the heat flux from the ocean to atmosphere in autumn and early winter. This has locally increased air temperature, moisture, and cloud cover and reduced the static stability in the lower troposphere. Several studies based on observations, atmospheric reanalyses, and model experiments suggest that the sea ice decline, together with increased snow cover in Eurasia, favours circulation patterns resembling the negative phase of the North Atlantic Oscillation and Arctic Oscillation. The suggested large-scale pressure patterns include a high over Eurasia, which favours cold winters in Europe and northeastern Eurasia. A high over the western and a low over the eastern North America have also been suggested, favouring advection of Arctic air masses to North America. Mid-latitude winter weather is, however, affected by several other factors, which generate a large inter-annual variability and often mask the effects of sea ice decline. In addition, the small sample of years with a large sea ice loss makes it difficult to distinguish the effects directly attributable to sea ice conditions. Several studies suggest that, with advancing global warming, cold winters in mid-latitude continents will no longer be common during the second half of the twenty-first century. Recent studies have also suggested causal links between the sea ice decline and summer precipitation in Europe, the Mediterranean, and East Asia. © 2014, The Author(s).


Karpechko A.Y.,Finnish Meteorological Institute
Geophysical Research Letters | Year: 2010

Atmospheric circulation variability associated with the Northern Annular Mode (NAM) modulates the climate over large areas in the Northern Hemisphere. Therefore, it is expected that future NAM changes in response to greenhouse gas concentration increases and other forcing will influence the climate change in these regions. Climate models simulate a wide range of future NAM changes, which introduces an uncertainty into regional climate predictions. To quantify this uncertainty we use the intermodel spread of the climate projections by the models participated in the Intergovernmental Panel on Climate Change Fourth Assessment Report. We show that the intermodel spread of the future NAM projections account for up to 40% of the variance of the surface temperature and precipitation projections over some regions in Eurasia and North America across the simulations. This result implies that the uncertainty in the future NAM makes a considerable contribution into the overall uncertainty in regional climate predictions. © 2010 by the American Geophysical Union.


Schultz D.M.,Finnish Meteorological Institute
Bulletin of the American Meteorological Society | Year: 2010

The factors leading to the rejection rates for journal publishing in the atmospheric sciences is discussed. the rejection rates of submissions is less than the ultimate success rate for manuscripts because manuscripts can be withdrawn by the author and rejected manuscripts can be revised and resubmitted to the same or to a different journal with the hope of being published. Journals with low rejection rates include IJM (2%), unnamed (9%), NHESS (10%) and ACP (12%). The low rejection rates of these journals with a low number of submissions may be results of striving to improve received submissions and grow the journal. The reason for low rejection rate is the interactive peer review and public discussion which indeed has a deterring deficient submission and counteract the flooding of the scientific publication market.


Leinonen J.,Finnish Meteorological Institute
Optics Express | Year: 2014

The PyTMatrix package was designed with the objective of providing a simple, extensible interface to T-Matrix electromagnetic scattering calculations performed using an extensively validated numerical core. The interface, implemented in the Python programming language, facilitates automation of the calculations and further analysis of the results through direct integration of both the inputs and the outputs of the calculations to numerical analysis software. This article describes the architecture and design of the package, illustrating how the concepts in the physics of electromagnetic scattering are mapped into data and code models in the computer software. The resulting capabilities and their consequences for the usability and performance of the package are explored.©2014 Optical Society of America.


Karvonen J.,Finnish Meteorological Institute
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

High-resolution ice concentration information is required for navigation purposes, validating ice models, and data assimilation. The currently available operational ice concentration products are based on microwave radiometer data, and their typical resolution is several kilometers. We present an algorithm for the estimation of ice concentration based on dual-polarized (HH/HV) C-band synthetic aperture radar (SAR) data. The algorithm is based on the multilayer perceptron (MLP) neural network. Ice concentration estimated based on the HH channel is used as one MLP input, and the local incidence angle is used as another. The additional inputs are based on the HV channel. Digitized Finnish Ice Service ice charts, which were also used as the training data, the SAR-based HH-channel ice concentration, and the ice concentration based on a radiometer algorithm are used as reference data sets. The results for the dual-polarized algorithm show improvement compared to the algorithm based on HH-polarized SAR data only. © 2014 IEEE.


L. Van De Kamp M.M.J.,Finnish Meteorological Institute
Annales Geophysicae | Year: 2013

The ionosphere above Scandinavia in December 2006 is successfully imaged by 4-dimensional tomography using the software package MIDAS from the University of Bath. The method concentrates on medium-scale structures: between 100 km and 2000 km in horizontal size. The input consists of TEC measurements from the dense GPS network Geotrim in Finland. In order to ensure sufficient vertical resolution of the result, EISCAT incoherent scatter radar data from Tromsø are used as additional input to provide the vertical profile information. The TEC offset of the measurements is unknown, but the inversion procedure is able to determine this automatically. This auto-calibration is shown to work well. Comparisons with EISCAT radar results and with occultation results show that the inversion using EISCAT data for profile information is much better able to resolve vertical profiles of irregular structures than the inversion using built-in profiles. Still, with either method the intensities of irregular structures of sizes near the resolution (about 100 km horizontal size) can be underestimated. Also, the accuracy of the inversion worsens above areas where no receivers are available. The ionosphere over Scandinavia in December 2006 often showed a dense E-layer in early morning hours, which generally disappeared during midday when a dense F-layer was present. On 14 December, a strong coronal mass ejection occurred, and many intense irregularities appeared in the ionosphere, which extended to high altitudes. © 2013 Author(s).


Karvonen J.,Finnish Meteorological Institute
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2012

Ice concentration measurements are important information e.g., for navigation, ice modeling, and climate change research. Here we present an algorithm for estimating ice concentration from C-band SAR data. The resolution of SAR data is significantly higher than the resolution of the current operational ice concentration products based on radiometer data. Our algorithm is based on segment-wise autocorrelation distributions. The algorithm results were compared with two reference data sets: ice concentrations from the Finnish Ice Service (FIS) ice charts, and ice concentrations from the radiometer-based operational ice concentration algorithm of University of Bremen. The new algorithm gives reasonable ice concentration estimates in a high resolution (500 m) for an arbitrary segmentation. © 2008-2012 IEEE.


Eerola K.,Finnish Meteorological Institute
Weather and Forecasting | Year: 2013

The High-Resolution Limited-Area Model (HIRLAM) international research program maintains a synoptic-scale NWP system. At the Finnish Meteorological Institute, the HIRLAM system has been run operationally since 1990. The HIRLAM forecasts from 1990 to 2012 have been verified against the numerical analysis. In 2-day forecasts, the monthly rms error of the mean sea level pressure has decreased from about 4 to about 2 hPa; that is, the error is now about half of the value it was in the early 1990s. Similar reduction is seen in the 500-hPa height. The negative bias has decreased significantly. In addition, the dependence on the weather regime, measured as the correlation between the North Atlantic Oscillation (NAO) index and rms error, has decreased. The reason for these improvements can often be attributed to changes in the HIRLAM system. A single improvement, improving most significantly the forecast skill, is the rerun concept, which improves the HIRLAM first guess by utilizing the high-quality ECMWF analysis. Verifying against observations or against the initial analysis gives similar results for a 48-h forecast. For a 6-h forecast, however, the field verification gives lower rms error values and lower bias values. In summary, the results indicate that the goal of the HIRLAM program has been fulfilled: to develop and maintain an up-to-date NWP system for 1- and 2-day forecasts on a limited domain. © 2013 American Meteorological Society.

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