No abstract

Due to its location, its old sewage system, and the channelling of rivers, Oslo is highly exposed to urban flooding. Thus, it is crucial to provide relevant and reliable information on extreme precipitation in the planning and design of infrastructure. Intensity-Duration-Frequency (IDF) curves are a frequently used tool for that purpose. However, the computational method for IDF curves in Norway was established over 45 years ago, and has not been further developed since. In our study, we show that the current method of fitting a Gumbel distribution to the highest precipitation events is not able to reflect the return values for the long return periods. Instead, we introduce the fitting of a Generalised Extreme Value (GEV) distribution for annual maximum precipitation in two different ways, using (a) a modified Maximum Likelihood estimation and (b) Bayesian inference. The comparison of the two methods for 14 stations in and around Oslo reveals that the estimated median return values are very similar, but the Bayesian method provides upper credible interval boundaries that are considerably higher. Two different goodness-of-fit tests favour the Bayesian method; thus, we suggest using the Bayesian inference for estimating IDF curves for the Oslo area.

<p>As a warming climate leads to more frequent heavy rainfall, the importance of accurate rainfall statistics is increasing. Rainfall statistics are often presented as intensity-duration-frequency (IDF) curves showing the rainfall intensity (return level) that can be expected at a location for a duration, and the frequency of this intensity (return period). IDF curves are commonly constructed by fitting generalized extreme value (GEV) distributions to observed annual maximum rainfall for several target durations, where the available observation data sources may vary for the different durations. As the estimation is performed independently across durations, the resulting IDF curves may be inconsistent across durations and return periods. We discuss how consistent estimates across the different durations may be derived by post-processing independently obtained Bayesian posterior distributions for each duration. The proposed methods are evaluated for simulated data and for Norwegian rainfall data from 83 locations, for 16 durations between 1 minute and 24 hours, where the post-processing yields consistent and accurate estimates.</p>

To design a building adapted to local climate requires a number of different climate indicators, one of them is design temperatures (DUT) for summer and winter.  The classic definition of summer design temperature was the maximum temperature exceeded 50 hours a typical year, and for winter the coldest three day average temperature. Looking into different descriptions of the DUT, there are distinct discrepancies. One example is for DUT-winter, where some instances describe the three coldest consecutive days and others the DUT-winter as a return period (e.g. 30 year) based on observations, similar for DUT-summer where some define it as 50 consecutive hours, others as individual hours summed together. The above uncertainty of definition is combined with the uncertainty of representation. The classic construction of DUT is based on observations from a representative station, interpolated to e.g. the municipality of interest. This method opens for the uncertainty of representation of the observational site and correctness for the interpolation.  Standards Norway contacted MET Norway to update the values of the DUT summer and winter in Norway to be calculated for the latest normal period, 1991 - 2020. In this work a new method to calculate DUT summer and winter was proposed and accepted: Instead of using single observational sites as a base, national climate grids calculated on a daily basis at MET Norway covering the entire country with a 1x1 km resolution is used as a basis Instead of a single temperature representing e.g. a single day is a statistical based approach applied. The method that was selected was a Bayesian-GEV approach where the output was calculated for 1 to 5 days average for highest and lowest mean temperature, with return values for 2-200 years.  This new approach resolves partly the challenge of representativity and interpolation by using robust and well documented spatial interpolation. The statistical approach also provides more well documented and robust statistics than the older approach. This approach creates a challenge since the new datasets represent something different than the older approach, and thus challenges the standard built on these datasets.  The distribution of the datasets will be renewed. Previously one had to buy the datasets from e.g. Standards Norway or other commercial vendors, the new dataset will be distributed openly from MET Norway, and implemented in e.g. the API frost.met.no  Besides the DUT, an extended information package containing daily temperature range and absolute humidity is calculated based on representative stations.

Snow loads are an important consideration in the design of buildings. Particularly in parts of Norway where heavy snowfall is common, it is important to know the weight from snow on houses to avoid structural damage or collapse. National standards and building regulations have been focusing on snow loads since 1949, and these regulations have been revised several times.    The Norwegian Meteorological Institute (MET Norway) produces daily interpolated data sets of precipitation and temperature with a 1*1 km resolution as part of our regular service. Data from 1957 - dd are included. These data sets are then used by the Norwegian Water Resources and Energy Directorate (NVE) to generate "Snow Water Equivalent" (SWE) interpolated data sets, grids, using a hydrological model.    In this study, the SWE grids are  used to produce snow loads with a 50 year return period for Norway, for two different periods 1961-1990 and 1991-2020. Three different methods for calculating the 50-year return period of snow loads are compared. In addition, the old normal period, 1961-1990, is compared with the current normal period, 1991-2020. It is typically shown that the snow load decreases in the lower-lying areas and along the coast of Norway. In higher altitude areas and in parts of Northern Norway, where there still are cold winters, the snow loads have increased.    Snow loads are also extracted for each municipality center in Norway, to compare to the current national snow loads standard. This method is suggested, and about to be adopted, for future use in the design of buildings in Norway.