Professor II
Thordis L. Thorarinsdottir
- Department Statistical modelling and machine learning
- Phone number +47 22 85 25 60
- E-mail thordis@nr.stage.dekodes.no
Publications
- 171 publications found
Aastveit, Marthe Elisabeth; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2026).
Predicting partially observed survival curves via factor analysis with application to demand forecasting in short-term rental markets. STOR-i, Lancaster University
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Faglig foredrag
Selig, Elizabeth; Achi, Nahla Gedeon; Sundnes, Frode; Wabnitz, Colette C.C.; Nakayama, Shinnosuke; Hjermann, Dag Øystein; Palacios-Abrantes, Juliano; Spijkers, Jessica; Hara, Mafaniso; Isaacs, Moenieba; McClanahan, Timothy R.; McKown, Ethan; Mensah, Adelina; Overå, Ragnhild; Rustad, Siri Camilla Aas; Thorarinsdottir, Thordis Linda og Tollefsen, Andreas Forø. (2026).
Patterns of marine resource conflicts across Africa highlight need for fair access and benefit sharing for a blue economy.
Vis sammendrag
An increased focus on the blue economy across coastal African countries requires effective strategies for reducing marine resource conflicts to achieve goals of sustainable, equitable ocean development. We created a spatial database documenting marine resource conflicts (2008–2018) and conducted an expert survey to analyze patterns in conflict types and how they relate to actors, drivers, and resolution. Our findings indicate that 73% of conflicts were associated with access disputes and 28% were between non-fisheries sectors. National governments, small-scale or industrial fishers, and state enforcement agents were the most frequent actors. Illegal fishing, inequitable benefit distribution, and inadequate regulations were commonly reported conflict drivers. Less than one third of conflicts were resolved, but increased governance was cited as important for resolution. These results suggest policymakers may need to focus on access and benefit sharing issues and increase engagement of key actors in governance processes to realize blue economy ambitions.
Aastveit, Marthe Elisabeth; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2025).
Demand changes over time in the short-term rental market. Royal Statistical Society
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poster
Aastveit, Marthe Elisabeth; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2025).
Demand changes over time in the short-term rental market: Forecasting partially observed curves. Bernoulli Society
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Vitenskapelig foredrag
Engebretsen, Solveig; Thorarinsdottir, Thordis Linda; Palomares, Alfonso Diz-Lois; Storvik, Geir Olve; Frigessi, Arnoldo og Blasio, Birgitte Freiesleben De. (2025).
Contribution to the Discussion of 'Some statistical aspects of the Covid-19 response' by Wood et al.
Aastveit, Marthe Elisabeth; Thorarinsdottir, Thordis Linda; Lenkoski, Alex og Pensar, Johan. (2024).
Demand changes over time in the short-term rental markets. Norsk statisker forening
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Vitenskapelig foredrag
Nordtorp, Henrik; Roksvåg, Thea Julie Thømt og Thorarinsdottir, Thordis Linda. (2024).
Widespread Risk of Extreme Precipitation and Flooding.
Vis sammendrag
Developing a thorough understanding of the prevalence of natural disasters such as floods and extreme precipitation events is of high importance for the society. In this report, it is attempted to investigate and quantify the widespread risk of flood and extreme precipitation events, using statistical methods on historical flood and precipitation data from southern Norway. The use of descriptive methods and clustering revealed patterns in the geographical spread of extreme events, in-line with current process understandings from hydrology and meteorology.
Lutz, Julia; Roksvåg, Thea Julie Thømt; Dyrrdal, Anita Verpe; Lussana, Cristian og Thorarinsdottir, Thordis Linda. (2024).
Areal reduction factors from gridded data products.
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Areal reduction factors (ARFs) convert a point estimate of extreme precipitation to an estimate of extreme precipitation over a spatial domain, and are commonly used in flood risk estimation. The fixed-area approach to ARF estimation considers an area of a certain size and constructs the ratio of extremes with the same exceedance probability for areal average precipitation and point precipitation at a representative location. In regions with sparse observation networks, estimates of areal average precipitation are highly uncertain if based on rain gauge data only. We construct and compare regional and seasonal ARF estimates for Norway using different gridded data products, both observation-based products (SURFdat, seNorge) and reanalysis products (NORA3). For data products that cover a sufficiently long time period, the extremes are estimated using the generalised extreme value (GEV) distribution, while the metastatistical extreme value (MEV) formulation is applied to data products with short records. The results indicate that the NORA3 reanalysis, available at a temporal resolution of 1 h and a spatial resolution of 3 km, provides a good overall adequacy for the purpose of obtaining robust and reliable ARF estimates.
Heinrich-Mertsching, Claudio Constantin; Thorarinsdottir, Thordis Linda; Guttorp, Peter og Schneider, Max. (2024).
Validation of point process predictions with proper scoring rules.
Vis sammendrag
We introduce a class of proper scoring rules for evaluating spatial point process forecasts based on summary statistics. These scoring rules rely on Monte Carlo approximations of expectations and can therefore easily be evaluated for any point process model that can be simulated. In this regard, they are more flexible than the commonly used logarithmic score and other existing proper scores for point process predictions. The scoring rules allow for evaluating the calibration of a model to specific aspects of a point process, such as its spatial distribution or tendency toward clustering. Using simulations, we analyze the sensitivity of our scoring rules to different aspects of the forecasts and compare it to the logarithmic score. Applications to earthquake occurrences in northern California, United States and the spatial distribution of Pacific silver firs in Findley Lake Reserve in Washington highlight the usefulness of our scores for scientific model selection.
Scheuerer, Michael; Bahaga, Titike Kassa; Segele, Zewdu T. og Thorarinsdottir, Thordis Linda. (2024).
Probabilistic rainy season onset prediction over the greater horn of africa based on long-range multi-model ensemble forecasts.
Vis sammendrag
This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the diferent forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region.
Roksvåg, Thea Julie Thømt; Lutz, Julia; Dyrrdal, Anita Verpe; Lussana, Cristian og Thorarinsdottir, Thordis. (2023).
Estimating extreme areal precipitation from gridded data products. Bocconi University
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poster
Haug, Ola; Heinrich-Mertsching, Claudio Constantin og Thorarinsdottir, Thordis. (2023).
Assessing risk of water damage to buildings under current and future climates. ESReDA and JRC Ispra
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Vitenskapelig foredrag
Vis sammendrag
https://publications.jrc.ec.europa.eu/repository/handle/JRC139101
Wahl, Jens Christian; Heinrich-Mertsching, Claudio Constantin; Liu, Izzie Yi; Thorarinsdottir, Thordis og Haug, Ola. (2023).
Gjensidige Denmark: Water damage risk model and preliminary analysis of storm damages.
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Rapport
Rognli, Odd Arne; Aamlid, Trygve S.; Alsheikh, Muath K; Amdahl, Helga; Dalmannsdottir, Sigridur; Hellton, Kristoffer Herland; Jørgensen, Marit; Kovi, Mallikarjuna Rao; Mæland, Therese; Pashapu, Akhil Reddy; Stürite, Ievina; Thorarinsdottir, Thordis Linda og Windju, Susanne Skinnehaugen. (2023).
Securing adaptation of timothy cultivars under climate change and during seed multiplication. EUCARPIA Fodder Crops and Amenity Grasses Section; Agricultural Research Ltd., Troubsko, Czech Republic; Institute of Experimental Botany ASCR, v.v.i., Olomouc, Czech Republic
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Vitenskapelig foredrag
Hellton, Kristoffer Herland; Amdahl, Helga; Thorarinsdottir, Thordis; Alsheikh, Muath K; Aamlid, Trygve S.; Jørgensen, Marit; Dalmannsdottir, Sigridur og Rognli, Odd Arne. (2023).
Yield predictions of timothy (Phleum pratense L.) in Norway under future climate scenarios.
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The perennial forage grass timothy (Phleum pratense L.) is the most important forage crop in Norway. Future changes
in the climate will affect growing conditions and hence the yield output. We used data from the Norwegian Value for Cultivation and Use testing to find a statistical prediction model for total dry matter yield (DMY) based on agro-climatic variables. The statistical model selection found that the predictors with the highest predictive power were growing degree days (GDD) in July and the number of days with rain (>1mm) in June–July. These predictors together explained 43% of the variability in total DMY. Further, the prediction model was combined with a range of climate ensembles (RCP4.5) to project DMY of timothy for the decades 2050–2059 and 2090–2099 at 8 locations in Norway. Our projections forecast that DMY of today’s timothy varieties may decrease substantially in South-Eastern Norway, but increase in Northern Norway, by the middle of the century, due to increased temperatures and changing precipitation patterns.
Yuan, Qifen; Thorarinsdottir, Thordis L.; Beldring, Stein; Wong, Wai Kwok og Xu, Chong-Yu. (2023).
Assessing uncertainty in hydrological projections arising from local-scale internal variability of climate.
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Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes.
Rognli, Odd Arne; Pashapu, Akhil Reddy; Kovi, Mallikarjuna Rao; Jørgensen, Marit; Dalmannsdottir, Sigridur; Aamlid, Trygve S.; Sturite, Ievina; Mæland, Therese; Hellton, Kristoffer Herland; Thorarinsdottir, Thordis Linda; Amdahl, Helga; Alsheikh, Muath K og Windju, Susanne. (2023).
Genetiske endringer i nord-norske timoteisorter over tid og ved oppformering på ulike breddegrader. NIBIO Tromsø, Holt
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Vitenskapelig foredrag
Thorarinsdottir, Thordis. (2023).
From weather to climate predictions. The Royal Society
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Vitenskapelig foredrag
Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda og Xu, Chong-Yu. (2023).
Flexible and consistent Flood–Duration–Frequency modeling: A Bayesian approach.
Vis sammendrag
Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models’ ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process.
Heinrich-Mertsching, Claudio Constantin; Wahl, Jens Christian; Ordonez, Alba; Stien, Marita; Elvsborg, John; Haug, Ola og Thorarinsdottir, Thordis Linda. (2023).
Assessing present and future risk of water damage using building attributes, meteorology, and topography.
Petropoulos, Fotios; Apiletti, Daniele; Assimakopoulos, Vassilios; Babai, Mohamed Zied; Barrow, Devon K.; Taieb, Souhaib Ben; Bergmeir, Christoph; Bessa, Ricardo J.; Bijak, Jakub; Boylan, John E.; Browell, Jethro; Carnevale, Claudio; Castle, Jennifer L.; Cirillo, Pasquale; Clements, Michael P.; Cordeiro, Clara; Oliveira, Fernando Luiz Cyrino; Baets, Shari De; Dokumentov, Alexander; Ellison, Joanne; Fiszeder, Piotr; Franses, Philip Hans; Frazier, David T.; Gilliland, Michael; Gönül, M. Sinan; Goodwin, Paul; Grossi, Luigi; Grushka-Cockayne, Yael; Guidolin, Mariangela; Guidolin, Massimo; Gunter, Ulrich; Guo, Xiaojia; Guseo, Renato; Harvey, Nigel; Hendry, David F.; Hollyman, Ross; Januschowski, Tim; Jeon, Jooyoung; Jose, Victor Richmond R.; Kang, Yanfei; Koehler, Anne B.; Kolassa, Stephan; Kourentzes, Nikolaos; Leva, Sonia; Li, Feng; Litsiou, Konstantia; Makridakis, Spyros; Martin, Gael M.; Martinez, Andrew B.; Meeran, Sheik; Modis, Theodore; Nikolopoulos, Konstantinos; Önkal, Dilek; Paccagnini, Alessia; Panagiotelis, Anastasios; Panapakidis, Ioannis; Pavía, Jose M.; Pedio, Manuela; Pedregal, Diego J.; Pinson, Pierre; Ramos, Patrícia; Rapach, David E.; Reade, J. James; Rostami-Tabar, Bahman; Rubaszek, Michał; Sermpinis, Georgios; Shang, Han Lin; Spiliotis, Evangelos; Syntetos, Aris A.; Talagala, Priyanga Dilini; Talagala, Thiyanga S.; Tashman, Len; Thomakos, Dimitrios; Thorarinsdottir, Thordis Linda; Todini, Ezio; Arenas, Juan Ramón Trapero; Wang, Xiaoqian; Winkler, Robert L.; Yusupova, Alisa og Ziel, Florian. (2022).
Forecasting: theory and practice.
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Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe; Roksvåg, Thea og Thorarinsdottir, Thordis Linda. (2022).
Estimating consistent rainfall design values for Norway using Bayesian inference and post-processing of posterior quantiles. European Meteorological Society
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Vitenskapelig foredrag
Hellton, Kristoffer Herland og Thorarinsdottir, Thordis. (2022).
Analysis of variety crossings for improved yield in timothy.
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Rapport
Thorarinsdottir, Thordis Linda; Roksvåg, Thea Julie Thømt og Eikvil, Line. (2022).
Birdcam: Automatic monitoring of birds near wind parks.
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Rapport
Løland, Anders; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2022).
Hvordan vet vi hvor tørr sommeren i Europa blir? / How to make a seasonal forecast.
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Programdeltagelse
Roksvåg, Thea; Lenkoski, Alex; Scheuerer, Michael; Heinrich, Claudio Constantin og Thorarinsdottir, Thordis Linda. (2022).
Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods. European Geosciences Union (EGU)
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Vitenskapelig foredrag
Roksvåg, Thea; Lenkoski, Alex; Scheuerer, Michael; Heinrich, Claudio Constantin og Thorarinsdottir, Thordis Linda. (2022).
Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods.
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Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This article proposes a probabilistic forecasting framework for predicting the end of the freeze-free season, or the time to a mean daily near-surface air temperature below 0°C (referred to here as hard freeze). The forecasting framework is based on the multimodel seasonal forecast ensemble provided by the Copernicus Climate Data Store and uses techniques from survival analysis for time-to-event data. The original mean daily temperature forecasts are statistically postprocessed and downscaled with a mean and variance correction of each model system before the time-to-event forecast is constructed. In a case study for a region in Fennoscandia covering Norway for the period 1993–2020, the proposed forecasts are found to outperform a climatology forecast from an observation-based data product at locations where the average predicted time to hard freeze is less than 40 days after the initialization date of the forecast on October 1.
Roksvåg, Thea; Scheuerer, Michael; Heinrich, Claudio Constantin; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2022).
Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods. Bjerknessenteret
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Faglig foredrag
Lenkoski, Alex; Kolstad, Erik Wilhelm og Thorarinsdottir, Thordis Linda. (2022).
A Benchmarking Dataset for Seasonal Weather Forecasts.
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There is an increasing demand for high-quality seasonal weather forecasts from a broad range of stakeholders. However, the numerical weather prediction (NWP) output on which these forecasts are based require substantial postprocessing, as they are subject to systematic errors in both mean and spread. In order to validate any proposed post-processing methodology, the research community would benefit from a benchmark dataset on which more sophisticated methods can quickly be developed and tested. We supply a multi-model, multi-variable global dataset using five forecasting systems from the Copernicus climate data store (CDS) which can help serve these purposes. Our dataset is constructed using a straightforward anomaly standardization methodology with a leave-year-out cross validation design. In addition, validating observations from the ERA5 dataset are supplied, enabling rapid verification of system performance. The goal of this dataset is to save the research community the substantial investment in time necessary to create a usable baseline for their own investigations and also to create a standard benchmark dataset to which different research groups can compare results.
Thorarinsdottir, Thordis Linda; Solberg, Rune; Lenkoski, Alex og Roksvåg, Thea. (2022).
Potensialet i data. -
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Faglig foredrag
Thorarinsdottir, Thordis Linda; Haugen, Marion og Guttorp, Peter. (2022).
Extracting robust information from data.
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Faglig foredrag
Thorarinsdottir, Thordis Linda; Roksvåg, Thea; Engeland, Kolbjørn; Barna, Danielle Marie; Xu, Chong-Yu; Lutz, Julia; Dyrrdal, Anita Verpe og Grinde, Lars. (2022).
Consistent estimation of extreme precipitation and flooding across multiple durations.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2022).
Climate Futures: Navigating climate risk.
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Faglig foredrag
Thorarinsdottir, Thordis Linda; Heinrich, Claudio Constantin og Guttorp, Peter. (2022).
Validation of point process predictions with proper scoring rules.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Barna, Danielle Marie; Roksvåg, Thea; Engeland, Kolbjørn; Xu, Chong-Yu; Lutz, Julia; Dyrrdal, Anita Verpe og Grinde, Lars. (2022).
Consistent estimation of extreme precipitation and flooding across multiple durations. BIRS
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Roksvåg, Thea; Lutz, Julia; Grinde, Lars og Dyrrdal, Anita Verpe. (2022).
A Bayesian framework to derive consistent intensity-duration-frequency curves from multiple data sources.
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Vitenskapelig foredrag
Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda og Xu, Chong-Yu. (2022).
Regional flood-Duration-Frequency (QDF) Models for Norway.
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Vitenskapelig foredrag
Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda og Xu, Chong-Yu. (2022).
New Flood-Duration-Frequency Models with a Focus on Estimation of Sub-daily Floods.
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Vitenskapelig foredrag
Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda og Xu, Chong-Yu. (2022).
Flood-duration-frequency (QDF) Modeling: Updates and Current Status.
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Vitenskapelig foredrag
Roksvåg, Thea; Scheuerer, Michael; Lenkoski, Alex; Heinrich, Claudio Constantin og Thorarinsdottir, Thordis Linda. (2022).
Prediction of the time to hard freeze using seasonal weather forecasts and survival time methods. NORCE
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Faglig foredrag
Løland, Anders; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2022).
Episode 16: Climate Futures. Klimaprognoser for 10 dager til 10 år fram / Predicting climate risks 10 days to 10 years ahead.
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Programdeltagelse
Thorarinsdottir, Thordis Linda. (2022).
On the importance of statistics and machine learning in climate research.
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Faglig foredrag
Vandeskog, Silius Mortensønn; Thorarinsdottir, Thordis Linda; Steinsland, Ingelin og Lindgren, Finn. (2022).
Quantile based modeling of diurnal temperature range with the five-parameter lambda distribution.
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Diurnal temperature range is an important variable in climate science that canprovide information regarding climate variability and climate change. Changesindiurnaltemperaturerangecanhaveimplicationsforhydrology,humanhealthand ecology, among others. Yet, the statistical literature on modeling diurnaltemperature range is lacking. In this article we propose to model the distri-bution of diurnal temperature range using the five-parameter lambda (FPL)distribution. Additionally, in order to model diurnal temperature range withexplanatory variables, we propose a distributional quantile regression modelthat combines quantile regression with marginal modeling using the FPL distri-bution. Inference is performed using the method of quantiles. The models arefitted to 30 years of daily observations of diurnal temperature range from 112weather stations in the southern part of Norway. The flexible FPL distributionshows great promise as a model for diurnal temperature range, and performswell against competing models. The distributional quantile regression model isfitted to diurnal temperature range data using geographic, orographic, and cli-matological explanatory variables. It performs well and captures much of thespatial variation in the distribution of diurnal temperature range in Norway.
Roksvåg, Thea; Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe og Thorarinsdottir, Thordis Linda. (2021).
New methods for making consistent IDF curves for Norway. NVE
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Faglig foredrag
Thorarinsdottir, Thordis Linda. (2021).
Forecast evaluation part III. Association of Universities in Western Switzerland
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2021).
On the importance of statistics and machine learning in climate research. Norsk Statistisk Forening
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2021).
Forecast evaluation part II. Association of Universities in Western Switzerland
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2021).
Forecast evaluation part I. Association of Universities in Western Switzerland
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2021).
Machine learning vs statistical methods for climate data analysis. Tekna
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Vitenskapelig foredrag
Scheuerer, Michael; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2021).
The Climate Futures Center for Research-based Innovation. Statkraft
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Faglig foredrag
Roksvåg, Thea; Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe og Thorarinsdottir, Thordis Linda. (2021).
Consistent intensity-duration-frequency curves by post-processing of estimated Bayesian posterior quantiles.
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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. As the estimation is performed independently across durations, the resulting IDF curves may be inconsistent across durations and return periods. This paper proposes to ensure consistency by post-processing the estimated IDF curves. Two post-processing approaches are considered, a quantile selection algorithm that searches for consistent return levels within the posterior quantiles of a Bayesian inference approach, and adjustments based on isotonic regression. The methods are evaluated for simulated data and for Norwegian rainfall data from 83 locations, for hourly and sub-hourly durations. The post-processing yields consistent estimates that are at least as accurate as the unadjusted, inconsistent estimates. We also demonstrate how our approach differs from d-GEV, a method that performs simultaneous estimation across durations. An R implementation for the post-processing methods is available at https://github.com/ClimDesign/fixIDF.
Wahl, Jens Christian; Heinrich, Claudio; Thorarinsdottir, Thordis og Haug, Ola. (2021).
Stedsbasert risiko for vannskader - fase 2: Effekten av bygningsegenskaper, meteorologi og topografi.
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Rapport
Roksvåg, Thea; Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe og Thorarinsdottir, Thordis Linda. (2021).
Consistent Intensity-Duration-Frequency curves by post-processing of estimated Bayesian posterior quantiles. Norwegian Hydrological Council
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Vitenskapelig foredrag
Barna, Danielle; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda og Xu, Chong-Yu. (2021).
A Bayesian approach to Flood-Duration-Analysis. AGU
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Vitenskapelig foredrag
Roksvåg, Thea og Thorarinsdottir, Thordis Linda. (2021).
Prediksjon av lavvann ved Åbjøra.
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Rapport
Yuan, Qifen; Thorarinsdottir, Thordis Linda; Beldring, Stein; Wong, Wai Kwok og Xu, Chong-Yu. (2021).
Bridging the scale gap: obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate.
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Climate change impact assessment related to floods, infrastructure networks, and water resource management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on a historical observation-based gridded data product, with coarser-scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.
Haug, Ola; Thorarinsdottir, Thordis Linda; Sørbye, Sigrunn Holbek og Franzke, Christian L.E.. (2020).
Spatial trend analysis of gridded temperature data at varying spatial scales.
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Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.
Wahl, Jens Christian; Heinrich, Claudio; Thorarinsdottir, Thordis; Ordonez, Alba; Trier, Øivind Due; Salberg, Arnt-Børre og Haug, Ola. (2020).
Stedsbasert risiko for vannskader - fase 1: Vurdering av topografiske indekser.
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Rapport
Heinrich, Claudio Constantin; Hellton, Kristoffer Herland; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2020).
Multivariate Postprocessing Methods for High-Dimensional Seasonal Weather Forecasts.
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Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Current seasonal forecasting models require statistical postprocessing of the output to correct systematic biases and unrealistic uncertainty assessments. We propose a multivariate postprocessing approach using covariance tapering, combined with a dimension reduction step based on principal component analysis for efficient computation. Our proposed technique can correctly and efficiently handle nonstationary, non-isotropic and negatively correlated spatial error patterns, and is applicable on a global scale. Further, a moving average approach to marginal postprocessing is shown to flexibly handle trends in biases caused by global warming, and short training periods. In an application to global sea surface temperature forecasts issued by the Norwegian climate prediction model, our proposed methodology is shown to outperform known reference methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Thorarinsdottir, Thordis Linda. (2020).
From weather to climate predictions. International Institute of Forecasters
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Vitenskapelig foredrag
Jullum, Martin; Thorarinsdottir, Thordis Linda og Bachl, Fabian E.. (2020).
Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modeling.
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The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical factors in the estimation of abundance that is needed for management of the species. These estimates are usually based on counts from aerial photographic surveys. However, only a minor part of the whelping region can be photographed, because of its large extent. To estimate total seal pup production, we propose a Bayesian hierarchical modelling approach motivated by viewing the seal pup appearances as a realization of a log‐Gaussian Cox process by using covariate information from satellite imagery as a proxy for ice thickness. For inference, we utilize the stochastic partial differential equation module of the integrated nested Laplace approximation framework. In a case‐study using survey data from 2012, we compare our results with existing methodology in a comprehensive cross‐validation study. The results of the study indicate that our method improves local estimation performance, and that the increased uncertainty of prediction of our method is required to obtain calibrated count predictions. This suggests that the sampling density of the survey design may not be sufficient to obtain reliable estimates of seal pup production.
Heinrich, Claudio Constantin; Thorarinsdottir, Thordis Linda; Schneider, Max og Guttorp, Peter. (2020).
Validation of point process predictions with proper scoring rules.
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We introduce a class of proper scoring rules for evaluating spatial point process forecastsbased on summary statistics. These scoring rules rely on Monte-Carlo approximation ofan expectation and can therefore easily be evaluated for any point process model that canbe simulated. In this regard, they are more flexible than the commonly used logarithmicscore; they are also fruitful for evaluating the calibration of a model to specific aspectsof a point process, such as its spatial distribution or tendency towards clustering. Weshow using simulations that our scoring rules are able to discern between competingmodels better than the logarithmic score. An application on growth in Pacific silver firtrees demonstrates the promise of our scores for scientific model selection.
Thorarinsdottir, Thordis Linda; Schuhen, Nina og Lenkoski, Alex. (2020).
Trajectory adjustment of lagged seasonal forecast ensembles.
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Seasonal forecasting has became a critical area of development in numerical weather prediction. Reliable forecasts beyond the two week time period are necessary for a number of industrial and societal planning applications and new approaches are being developed to extend the useful range of numerical weather prediction output. We investigate the performance of one such system, the UK Met Office’s GloSea5 system, an ensemble system with the novel feature that ensemble members are initiated in a rolling and staggered manner. Focusing on summer surface temperatures, we show that individual model runs from this system do not exhibit skill beyond the two-week time horizon and indeed substantially under-perform climatological forecasts at longer lead times. However, when combining the ensemble system and applying the Rapid Adjustment of Forecast Trajectories (RAFT) methodology to the individual runs, we show that the combined forecast can achieve performance which is always at least on par with climatology and in many circumstances exhibits modest outperformance.
Krüger, Fabian; Lerch, Sebastian; Thorarinsdottir, Thordis og Gneiting, Tilmann. (2020).
Predictive Inference Based on Markov Chain Monte Carlo Output.
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In Bayesian inference, predictive distributions are typically in the form of samples generated via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic analysis of how to make and evaluate probabilistic forecasts from such simulation output. Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output. We then provide asymptotic results that account for the salient features of Bayesian posterior simulators and derive conditions under which choices from the literature satisfy our notion of consistency. Importantly, these conditions depend on the scoring rule being used, such that the choices of approximation method and scoring rule are intertwined. While the logarithmic rule requires fairly stringent conditions, the continuous ranked probability score yields consistent approximations under minimal assumptions. These results are illustrated in a simulation study and an economic data example. Overall, mixture‐of‐parameters approximations that exploit the parametric structure of Bayesian models perform particularly well. Under the continuous ranked probability score, the empirical distribution function is a simple and appealing alternative option.
Thorarinsdottir, Thordis Linda; Sillmann, Jana; Haugen, Marion; Gissibl, Nadine og Sandstad, Marit. (2020).
Evaluation of CMIP5 and CMIP6 simulations of historical surface air temperature extremes using proper evaluation methods.
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Reliable projections of extremes by climate models are becoming increasingly important in the context of climate change and associated societal impacts. Extremes are by definition rare events, characterized by a small sample associated with large uncertainties. The evaluation of extreme events in model simulations thus requires performance measures that compare full distributions rather than simple summaries. This paper proposes the use of the integrated quadratic distance (IQD) for this purpose. The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum and minimum near-surface air temperature over Europe and North America against both observation-based data and reanalyses. Several climate models perform well to the extent that these models' performance is competitive with the performance of another data product in simulating the evaluation set. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis. When the model simulations are ranked based on their similarity with the ERA5 reanalysis, more CMIP6 than CMIP5 models appear at the top of the ranking. When evaluated against the HadEX2 data product, the overall performance of the two model ensembles is similar.
Heinrich, Claudio; Wahl, Jens Christian; Thorarinsdottir, Thordis og Haug, Ola. (2020).
Risikomodell for vannskader på bygninger.
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Rapport
Heinrich, Claudio; Wahl, Jens Christian; Matre, Andreas; Thorarinsdottir, Thordis og Haug, Ola. (2020).
Risikomodell for vannskader på bygninger og sensitivitet i klimaframskrivninger.
Vandeskog, Silius Mortensønn; Haugen, Marion og Thorarinsdottir, Thordis Linda. (2020).
Evaluation of bias corrected precipitation output from the EURO-CORDEX climate ensemble.
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Rapport
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Global circulation models (GCMs) are used for projecting climate changes on a global scale. However, when we need information for local climate changes, a dynamical downscaling through a regional climate model (RCM) may be used to gain more precise information. Therefore it is important to make good RCMs that are unbiased when projecting climate changes. This note investigates the skill of precipitation projections from five combinations of global and regional climate models from EURO-CORDEX and four bias correction methods applied to some of these. This is performed by comparing the model outputs with data from the E-OBS and NGCD data products using integrated quadratic distance.
Schuhen, Nina; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2019).
Rapid adjustment and post-processing of temperature forecast trajectories.
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Modern weather forecasts are commonly issued as consistent multi‐day forecast trajectories with a time resolution of 1–3 hours. Prior to issuing, statistical post‐processing is routinely used to correct systematic errors and misrepresentations of the forecast uncertainty. However, once the forecast has been issued, it is rarely updated before it is replaced in the next forecast cycle of the numerical weather prediction (NWP) model. This paper shows that the error correlation structure within the forecast trajectory can be utilized to substantially improve the forecast between the NWP forecast cycles by applying additional post‐processing steps each time new observations become available. The proposed rapid adjustment is applied to temperature forecast trajectories from the UK Met Office's convective‐scale ensemble MOGREPS‐UK. MOGREPS‐UK is run four times daily and produces hourly forecasts for up to 36 hours ahead. Our results indicate that the rapidly adjusted forecast from the previous NWP forecast cycle outperforms the new forecast for the first few hours of the next cycle, or until the new forecast itself can be rapidly adjusted, suggesting a new strategy for updating the forecast cycle.
Schuhen, Nina; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2019).
Rapid adjustment of weather forecast trajectories. Big Insight
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Vitenskapelig foredrag
Rognebakke, Hanne Therese Wist og Thorarinsdottir, Thordis Linda. (2019).
Statistical space-time projections of wave heights
in the North Atlantic. The Bernoulli Society
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Vitenskapelig foredrag
Yuan, Qifen; Thorarinsdottir, Thordis Linda; Beldring, Stein; Wong, Wai Kwok; Huang, Shaochun og Xu, Chong-Yu. (2019).
New approach for bias correction and stochastic downscaling of future projections for daily mean temperatures to a high-resolution grid.
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In applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.
Thorarinsdottir, Thordis Linda; Stefanakos, Christos; Vanem, Erik; Rognebakke, Hanne Therese Wist; Hammer, Hugo Lewi og Øigård, Tor Arne. (2019).
HDwave: Statistical space-time projections of wave heights.
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poster
Heinrich, Claudio Constantin; Schneider, Max; Guttorp, Peter og Thorarinsdottir, Thordis Linda. (2019).
Validation of point process forecasts.
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We introduce a class of proper scoring rules for evaluating spatial point process forecastsbased on summary statistics. These scoring rules rely on Monte-Carlo approximation ofan expectation and can therefore easily be evaluated for any point process model thatcan be simulated. In this regard they are more flexible than the commonly used logar-ithmic score which cannot be evaluated for many point process models, as their densityis only known up to an untractable constant. In simulation studies we demonstrate theusefulness of our scores. Furthermore we consider a scoring rule, the quantile score, thatis commonly used to validate earthquake rate predictions, and show that it lacks propri-ety. As a consequence, several tests that are commonly applied in this context are biasedand systematically favour predictive distributions that are too uniform. We suggest toremedy this issue by replacing the commonly used one-sided by two-sided tests.
Guttorp, Peter og Thorarinsdottir, Thordis Linda. (2019).
Local Climate Projections: A Little Money Goes a Long Way.
Skuland, Kristoffer; Heinrich, Claudio Constantin; Lenkoski, Alex og Thorarinsdottir, Thordis Linda. (2019).
Stratospheric events and long-range Scandinavian winter surface temperature forecasts.
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Schuhen, Nina; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2019).
Rapid Adjustment of Forecast Trajectories: improving short-term forecast skill through statistical post-processing. EGU
NVA
Vitenskapelig foredrag
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The skill of a typical NWP forecast decreases over time, so that forecasts from more recent model runs are generallyconsideredtobemoreskillfulandgivemoreaccuratepredictions.Somepost-processingtechniquesstill make use of older model runs through time-lagging or blending, but with very little relevance, as the newer model runs are preferred. At the same time, technological advances make observations become available in very short time frames and in increasing amounts. We propose a new method, Rapid Adjustment of Forecast Trajectories (RAFT), which works in combination with traditional statistical post-processing techniques and uses short-term observations to improve older forecast runs. As a result, older forecasts match or even surpass the skill of the forecasts from the newest model run. Relying on the inherent correlation structure of the forecast errors between lead times, RAFT updates the tail of a forecast trajectory while the first part verifies. The adaptive regression approach takes into account changesinpredictabilityandlocalpatterns,whilebeingcomputationallyefficient.WewillpresentRAFTversions forhourlysurfacetemperatureand10mwindspeedforecastsfromtheUKMetOffice’sMOGREPS-UKensemble.
Thorarinsdottir, Thordis Linda; Engeland, Kolbjørn og Kobierska, Florian. (2019).
The effects of uncertainty on design flood estimation.
NVA
Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2019).
Statistics in climate research: The importance of stochastic modelling and uncertainty quantification.
NVA
Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2019).
Decision support for climate change adaptation: The importance of uncertainty assessment.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2019).
On developing general and efficient inference algorithms for complicated hierarchical models.
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Vitenskapelig foredrag
Kobierska-Baffie, Florian Antoine; Engeland, Kolbjørn og Thorarinsdottir, Thordis Linda. (2018).
Evaluation of design flood estimates - a case study for Norway.
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The aim of this study was to evaluate the predictive fit of probability distributions to annual maximum flood data, and in particular to evaluate (i) which combination of distribution and estimation method gives the best fit and (ii) whether the answer to (i) depends on record length. These aims were achieved by assessing the sensitivity to record length of the predictive performance of several probability distributions. A bootstrapping approach was used by resampling (with replacement) record lengths of 30 to 90 years (50 resamples for each record length) from the original record and fitting distributions to these subsamples. Subsequently, the fits were evaluated according to several goodness-of-fit measures and to the variability of the predicted flood quantiles. Our initial hypothesis that shorter records favor two-parameter distributions was not clearly supported. The ordinary moments method was the most stable while providing equivalent goodness-of-fit.
Thorarinsdottir, Thordis Linda og Schuhen, Nina. (2018).
Verification: Assessment of Calibration and Accuracy.
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In ensemble forecasting, forecast verification methods are needed to diagnose both the need for statistical postprocessing and the effectiveness of the postprocessing methods in producing calibrated and accurate forecasts. This chapter discusses an array of techniques that can be used in this context, making the distinction between verification tools that are useful for ranking competing forecasters and those that are more appropriate for improving our understanding of the performance of a single method. With a focus on continuous variables, verification methods for both univariate and multivariate forecasts are discussed, including approaches that are specifically tailored to the evaluation of extreme events.
Guttorp, Peter og Thorarinsdottir, Thordis Linda. (2018).
How to save Bergen from the sea? Decisions under uncertainty.
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Sea level rise poses a threat to the Norwegian coastal city of Bergen and its historic harbour. The threat could be reduced, but greater flood protection comes at greater cost. And, of course, no one knows for certain how far sea level will rise in future. Decision‐makers must therefore decide what to do, and how much to spend, without knowing exactly how bad things could get. Peter Guttorp and Thordis L. Thorarinsdottir explain the problem, and how to deal with the uncertainty
Albert-Green, Alisha; Guttorp, Peter og Thorarinsdottir, Thordis Linda. (2018).
Does Bayes beat squinting? Estimating unobserved aspects of
a spatial cluster process.
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A point process data set on epidermal nerve fiber bundles is used as the basis for a series of experiments in identifying clusters. In this data set we know which secondary points are connected to which primary points.We will pretend that we do not have this information,
and using Bayesian tools estimate the information from data. For comparison we also use k-means clustering. We do this both for known cluster centers, and when the
cluster centers must be estimated from data.
Guttorp, Peter; Thorarinsdottir, Thordis Linda og Albert-Green, Alisha. (2018).
Using nerve fibre data as a statistical laboratory.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2018).
Point processes: Models vs. inference.
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Vitenskapelig foredrag
Jullum, Martin; Thorarinsdottir, Thordis Linda og Bachl, Fabian. (2018).
Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling.
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The Greenland Sea is an important breeding ground for harp seals (Pagophilus groenlandicus) and hooded seals (Cystophora cristata). An estimate of the annual seal pup
production is a critical factor in the abundance estimation needed for management of the species. Estimates of seal pup production are usually based on counts from aerial photographic surveys. However, due to the large extent of typical whelping regions, only a minor part of the complete area can be photographed. To estimate the total seal pup production, we propose a Bayesian hierarchical modelling approach motivated by viewing
the seal pup appearances as a realization of a log-Gaussian Cox process using covariate information from satellite imagery as a proxy for ice-thickness. For inference, we utilize the spatial partial differential equation (SPDE) module of the integrated nested Laplace
approximation (INLA) framework. In a case study using survey data from 2012, we compare our results with existing methodology in a comprehensive cross-validation study. The new proposed method improves local estimation performance and more accurately addresses the associated uncertainty.
Thorarinsdottir, Thordis Linda. (2018).
Bayesian modelling of cluster point process models.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2018).
Does Bayes beat squinting? Bayesian modelling of cluster point process models.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Heinrich, Claudio Constantin; Lenkoski, Alex; Kolstad, Erik Wilhelm og Paasche, Øyvind. (2018).
Varsling av vær og klima i maskinlæringens tid. Hvor gode kan sesongvarslene bli? Energi Norge
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Yuan, Qifen; Wong, Wai Kwok; Beldring, Stein; Huang, Shaochun; Xu, Chong-Yu og Guttorp, Peter. (2018).
Statistics in climate research: The importance of stochastic modelling and uncertainty quantification.
NVA
Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Yuan, Qifen; Wong, Wai Kwok; Beldring, Stein; Huang, Shaochun; Xu, Chong-Yu og Guttorp, Peter. (2018).
Post-processing climate model output to obtain accurate high-resolution climate projections & why uncertainty matters even if the answer is just a number.
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda. (2018).
Spatial hierarchical modelling with a large number of potential covariates.
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Vitenskapelig foredrag
Hellton, Kristoffer Herland og Thorarinsdottir, Thordis Linda. (2018).
Bayesian hierarchical modeling of extreme flood events.
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Vitenskapelig foredrag
Schuhen, Nina; Thorarinsdottir, Thordis Linda og Lenkoski, Alex. (2018).
Improving forecasts through rapid updating of temperature trajectories and statistical post-processing. European Geosciences Union
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Hellton, Kristoffer Herland; Steinbakk, Gunnhildur Högnadóttir; Schlichting, Lena og Engeland, Kolbjørn. (2018).
Statistical estimation of extreme floods. Volkswagenstiftung
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Lenkoski, Alex; Hellton, Kristoffer Herland; Steinbakk, Gunnhildur Högnadóttir; Dyrrdal, Anita Verpe; Stordal, Frode; Schlichting, Lena og Engeland, Kolbjørn. (2018).
On developing general and efficient inference algorithms for complicated hierarchical models. German Mathematical Society
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Vitenskapelig foredrag
Thorarinsdottir, Thordis Linda; Engeland, Kolbjørn; Lawrence, Deborah; Pedersen, Øyvind; Tveito, Ole Einar; Hellton, Kristoffer Herland; Dyrrdal, Anita Verpe; Eide, Vidar; Førland, Eirik; Holmqvist, Erik; Kobierska, Florian; Jørgensen, Sigrid; Midttømme, Grethe Holm; Moore, Richard; Nordtun, Kristian Strand; Orthe, Nils Kristian; Randen, Frode; Reitan, Trond; Ruther, Nils; Schlichting, Lena; Skaugen, Thomas; Steinbakk, Gunnhildur Högnadóttir; Voksø, Astrid; Væringstad, Thomas; Wang, Thea; Wilson, Donna og Ødemark, Karianne. (2018).
Nytt rammeverk for flomestimering i Norge: Sluttrapport fra forskningsprosjektet FlomQ.
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Rapport