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Oslo, Norway

The Norwegian Meteorological Institute , also known as MET Norway, is Norway's national institute which provides weather forecasts.Its three main offices are located in Oslo, Bergen and Tromsø. MET Norway has around 500 employees and some 650 part-time observers around the country. It also operated the last remaining weather ship in the world, MS Polarfront, stationed in the North Atlantic, until it was discontinued due to budgetary issues on 1 January 2010 and replaced with satellite and buoy data.The institute was founded in 1866 with the help of Norwegian astronomer and meteorologist Henrik Mohn who served as its director until 1913. He is credited with founding meteorological research in Norway.The institute represents Norway in international organizations like the World Meteorological Organization , the European Centre for Medium-Range Weather Forecasts , and EUMETSAT. The Institute is also partner to a number of international research and monitoring projects including EMEP, MyOcean, MyWave and the North West Shelf Operational Oceanographic System . Wikipedia.


Benestad R.E.,Norwegian Meteorological Institute
Environmental Research Letters | Year: 2013

Variations in the annual mean of the galactic cosmic ray flux (GCR) are compared with annual variations in the most common meteorological variables: temperature, mean sea-level barometric pressure, and precipitation statistics. A multiple regression analysis was used to explore the potential for a GCR response on timescales longer than a year and to identify 'fingerprint' patterns in time and space associated with GCR as well as greenhouse gas (GHG) concentrations and the El Niño-Southern Oscillation (ENSO). The response pattern associated with GCR consisted of a negative temperature anomaly that was limited to parts of eastern Europe, and a weak anomaly in the sea-level pressure (SLP), but coincided with higher pressure over the Norwegian Sea. It had a similarity to the North Atlantic Oscillation (NAO) in the northern hemisphere and a wave train in the southern hemisphere. A set of Monte Carlo simulations nevertheless indicated that the weak amplitude of the global mean temperature response associated with GCR could easily be due to chance (p-value = 0.6), and there has been no trend in the GCR. Hence, there is little empirical evidence that links GCR to the recent global warming. © 2013 IOP Publishing Ltd. Source


Benestad R.E.,Norwegian Meteorological Institute
Theoretical and Applied Climatology | Year: 2010

A new method for predicting the upper tail of the precipitation distribution, based on empirical-statistical downscaling, is explored. The proposed downscaling method involves a re-calibration of the results from an analog model to ensure that the results have a realistic statistical distribution. A comparison between new results and those from a traditional analog model suggests that the new method predicts higher probabilities for heavy precipitation events in the future, except for the most extreme percentiles for which sampling fluctuations give rise to high uncertainties. The proposed method is applied to the 24-h precipitation from Oslo, Norway, and validated through a comparison between modelled and observed percentiles. It is shown that the method yields a good approximate description of both the statistical distribution of the wet-day precipitation amount and the chronology of precipitation events. An additional analysis is carried out comparing the use of extended empirical orthogonal functions (EOFs) as input, instead of ordinary EOFs. The results were, in general, similar; however, extended EOFs give greater persistence for 1-day lags. Predictions of the probability distribution function for the Oslo precipitation indicate that future precipitation amounts associated with the upper percentiles increase faster than for the lower percentiles. Substantial random statistical fluctuations in the few observations that make up the extreme upper tail implies that modelling of these is extremely difficult, however. An extrapolation scheme is proposed for describing the trends associated with the most extreme percentiles, assuming an upper physical bound where the trend is defined as zero, a gradual variation in the trend magnitude and a function with a simple shape. © The Author(s) 2009. Source


Dyrrdal A.V.,Norwegian Meteorological Institute
Hydrology Research | Year: 2010

The snow map service introduced by the Norwegian Meteorological institute and Norwegian Water Resources and Energy Directorate in 2004 is evaluated at eleven meteorological stations situated in three regions in Norway. The focus is on the start and end of the snow season and the total number of snow days, in addition, accumulated snow depth throughout the winter season, along with snow depth on four selected dates, is examined. In the evaluation, simulations by a precipitation/degree-day snow model are compared to observations. The approach used to calculate snow depth tends to compact the snow too much, resulting in an underestimation of snow depth at most stations. In Region 1 the snow model simulates a shorter snow season than what is observed. In Region 2 the results are ambiguous and correlations between simulations and observations are low. In Region 3, however, the snow model performs very well, and there are no significant differences between simulated and observed snow season. The results confirm that the use of snow simulations in areas outside the observational network is valuable, but there is room for improvement. © IWA Publishing 2010. Source


Benestad R.E.,Norwegian Meteorological Institute
Journal of Geophysical Research: Atmospheres | Year: 2013

Attributing changes in extreme daily precipitation to global warming is difficult, even when based on global climate model simulations or statistical trend analyses. The question about trends in extreme precipitation and their causes has been elusive because of climate models' limited precision and the fact that extremes are both rare and occur at irregular intervals. Here a newly discovered empirical relationship between the wet-day mean and percentiles in 24 h precipitation amounts was used to show that trends in the wet-day 95th percentiles worldwide have been influenced by the global mean temperature, consistent with an accelerated hydrological cycle caused by a global warming. A multiple regression analysis was used as a basis for an attribution analysis by matching temporal variability in precipitation statistics with the global mean temperature. Key Points Intense 24-hr precipitation changes due to global warming New method for downscaling 24-hr precipitation statistics Independent confirmation of earlier studies ©2013. American Geophysical Union. All Rights Reserved. Source


Benestad R.,Norwegian Meteorological Institute
Global and Planetary Change | Year: 2013

The paper by Humlum et al. (2013) suggests that much of the increase in atmospheric CO2 concentration since 1980 results from changes in ocean temperatures, rather than from the burning of fossil fuels. We show that these conclusions stem from methodological errors and from not recognizing the impact of the El Niño-Southern Oscillation on inter-annual variations in atmospheric CO2. © 2013 Elsevier B.V. Source

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