Publications

See also my Google Scholar profile. 

Journal articles 

  1. C. Heinrich-Mertsching, T. L. Thorarinsdottir, P. Guttorp and M. Schneider (2024): Validation of point process predictions with proper scoring rules. Scandinavian Journal of Statistics, in press. https://doi.org/10.1111/sjos.12736. 
  2. J. Lutz, T. Roksvåg, A. V. Dyrrdal, C. Lussana and T. L. Thorarinsdottir (2024): Areal reduction factors from gridded data products. Journal of Hydrology, 635: 131177. https://doi.org/10.1016/j.jhydrol.2024.131177 
  3. M. Scheuerer, T. K. Bahaga, Z. T. Segele and T. L. Thorarinsdottir (2024): Probabilistic rainy season onset prediction over the Greater Horn of Africa based on long-range multi-model ensemble forecasts. Climate Dynamics, 62: 3587-3604. https://doi.org/10.1007/s00382-023-07085-y.
  4. C. Heinrich-Mertsching, J. C. Wahl, A. Ordonez, M. Stien, J. Elvsborg, O. Haug and T. L. Thorarinsdottir (2023): Assessing present and future risk of water damage using building attributes, meteorology and topography. Journal of the Royal Statistical Society, Series C (with discussion), 74(4): 809-828. https://doi.org/10.1093/jrsssc/qlad043.
  5. D. Barna, K. Engeland, T. L. Thorarinsdottir and C.-Y. Xu (2023): Flexible and consistent Flood-Duration-Frequency  modeling: A Bayesian approach. Journal of Hydrology, 620(B):129448, https://doi.org/10.1016/j.jhydrol.2023.129448.
  6. Q. Yuan, T. L. Thorarinsdottir, S. Beldring,  W. K. Wong and C.-Y. Xu (2023): Assessing uncertainty in   hydrological projections arising from local-scale internal  variability of climate. Journal of Hydrology, 620(A): 129415,  https://doi.org/10.1016/j.jhydrol.2023.129415. 
  7. T. Roksvåg, A. Lenkoski, M. Scheuerer,  C. Heinrich-Mertsching and T. L. Thorarinsdottir (2023):   Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods, Quarterly Journal of the Royal Meteorological Society, 149(750): 211-230. https://doi.org/10.1002/qj.4403.
  8. S.M. Vandeskog, T. L. Thorarinsdottir, I. Steinsland and F. Lindgren (2022). Quantile based modeling of diurnal temperature range with the five-parameter lambda distribution, Environmetrics, 33(4):e2719. https://doi.org/10.1002/env.2719. Code and data are available at https://github.com/siliusmv/FPLD.
  9. F. Petropoulos, D. Apiletti, V. Assimakopoulos, M. Z. Babai, D. K Barrow, S. Ben Taieb, C. Bergmeir, R. J. Bessa, J. Bijak, J. E. Boylan, J. Browell, C. Carnevale, J. L. Castle, P. Cirillo, M. P. Clements, C. Cordeiro, F. L. C. Oliveira, S. De Baets, A. Dokumentov, J. Ellison, P. Fiszeder, P. H. Franses, D. T. Frazier, M. Gilliland, M. Sinan G\"on\"ul, P. Goodwin, L. Grossi, Y. Grushka-Cockayne, M. Guidolin, M. Guidolin, U. Gunter, X. Guo, R. Guseo, N. Harvey, D. F. Hendry, R. Hollyman, T. Januschowski, J. Jeon, V. R. R. Jose, Y. Kang, A. B. Koehler, S. Kolassa, N. Kourentzes, S. Leva, F. Li, K. Litsiou, S. Makridakis, G. M. Martin, A. B. Martinez, S. Meeran, T. Modis, K. Nikolopoulos, D. Önkal, A. Paccagnini, A. Panagiotelis, I. Panapakidis, J. M. Pavia, M. Pedio, D. J. Pedregal, P. Pinson, P. Ramos, D. E. Rapach, J. J. Reade, B. Rostami-Tabar, M. Rubaszek, G. Sermpinis, H. L. Shang, E. Spiliotis, A. A. Syntetos, P. D. Talagala, T. S. Talagala, L. Tashman, D. Thomakos, T. Thorarinsdottir, E. Todini, J. R. Trapero Arenas, X. Wang, R. L. Winkler, A. Yusupova and F. Ziel (2022): Forecasting: theory and practice, International Journal of Forecasting, 38(3): 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001.
  10. T. Roksvåg, J. Lutz, L. Grinde, A. V. Dyrrdal and T. L. Thorarinsdottir (2021). Consistent intensity-duration-frequency curves by post-processing of estimated Bayesian posterior quantiles, Journal of Hydrology, 603(C): 127000. https://doi.org/10.1016/j.jhydrol.2021.127000. Software available at https://github.com/ClimDesign/fixIDF.   
  11. Q. Yuan, T. L. Thorarinsdottir, S. Beldring, W. K. Wong and C.-Y. Xu (2021). Bridging the scale gap: obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate, Hydrology and Earth System Sciences, 25: 5259-5275. https://doi.org/10.5194/hess-25-5259-2021.
  12. F. Krüger, S. Lerch, T. L. Thorarinsdottir and T. Gneiting (2021). Predictive inference based on Markov chain Monte Carlo output, International Statistical Review, 89(2): 274-301. https://doi.org/10.1111/insr.12405. Code is available as described in https://github.com/FK83/scoringRules/blob/master/KLTG2020_replication.pdf.
  13. C. Heinrich, K. H. Hellton, A. Lenkoski and T. L. Thorarinsdottir (2021). Multivariate postprocessing methods for high-dimensional seasonal weather forecasts, Journal of the American Statistical Association, 116(535): 1048-1059. https://doi.org/10.1080/01621459.2020.1769634.
  14. T. L. Thorarinsdottir, J. Sillmann, M. Haugen, N. Gissibl and M. Sandstad (2020). Evaluation of CMIP5 and CMIP6 simulations of historical surface air temperature extremes using proper evaluation methods, Environmental Research Letters, 15: 124041. https://doi.org/10.1088/1748-9326/abc778. Code available at https://github.com/NorskRegnesentral/IQD.
  15. M. Jullum, T. L. Thorarinsdottir and F. E. Bachl (2020). Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling, Journal of the Royal Statistical Society, Series C, 69(2): 327-352. https://doi.org/10.1111/rssc.12397. Code available at https://github.com/PointProcess/SealPupProduction-JRSSC-code.
  16. O. Haug, T. L. Thorarinsdottir, S. H. Soerbye and C. L. E. Franzke (2020). Spatial trend analysis of gridded temperature data at varying spatial scales, Advances in Statistical Climatology, Meteorology and Oceanography, 6: 1-12. https://doi.org/10.5194/ascmo-6-1-2020. Code available at https://github.com/eSACP/STT.
  17. N. Schuhen, T. L. Thorarinsdottir and A. Lenkoski (2020). Rapid adjustments and post-processing of temperature forecast trajectories, Quarterly Journal of the Royal Meteorological Society, 146: 963-978. https://doi.org/10.1002/qj.3718.   
  18. Q. Yuan, T. L. Thorarinsdottir, S. Beldring, W. K. Wong, S. Huang, C.-Y. Xu (2019). New approach for stochastic downscaling and bias correction of daily mean temperatures to a high-resolution grid, Journal of Applied Meteorology and Climatology, 58: 2617-2632. https://doi.org/10.1175/JAMC-D-19-0086.1.   \
  19. T. L. Thorarinsdottir, K. H. Hellton, G. H. Steinbakk, L. Schlichting and K. Engeland (2018). Bayesian regional flood frequency analysis for large catchments. Water Resources Research, 54(9): 6929-6947. https://doi.org/10.1029/2017WR022460}. Code available at https://github.com/NorskRegnesentral/BRFFA.   
  20. P. Guttorp and T. L. Thorarinsdottir (2018). How to save Bergen from the sea? Decisions under uncertainty. Significance, 15(2): 14-18. https://doi.org/10.1111/j.1740-9713.2018.01125.x.   
  21. F. Kobierska, K. Engeland and T. L. Thorarinsdottir (2017). Evaluation of design flood estimates -- a case study for Norway. Hydrology Research, 49(2): 450-465. https://doi.org/10.2166/nh.2017.068. Software available at https://nve.github.io/fitdistrib/.   
  22. J. Sillmann, T. Thorarinsdottir, N. Keenlyside, N. Schaller, L. V. Alexander, G. Hegerl, S. I. Seneviratne, R. Vautard, X. Zhang and F. W. Zwiers (2017). Understanding, modeling and predictig weather and climate extremes: Challenges and opportunities. Weather and Climate Extremes, 18: 65-74. https://doi.org/10.1016/j.wace.2017.10.003}.
  23. T. L. Thorarinsdottir, P. Guttorp, M. Drews, P. Skougaard Kaspersen and K. de Bruin (2017). Sea level adaptation decisions under uncertainty. Water Resources Research, 53(10): 8147-8163. https://doi.org/10.1002/2016WR020354}. Code available at https://github.com/eSACP/SeaLevelDecisions.
  24. R. Benestad, J. Sillmann, T. L. Thorarinsdottir, P. Guttorp, M. d. S. Mesquita, M. R. Tye, P. Uotila, C. Fox Maule, P. Thejll, M. Drews and K. M. Parding (2017). New vigour involving statisticians to overcome ensemble fatigue. Nature Climate Change, 7: 697-703. https://doi.org/10.1038/nclimate3393.
  25. S. Lerch, T. L. Thorarinsdottir, F. Ravazzolo and T. Gneiting (2017). Forecaster's dilemma: extreme events and forecast evaluation. Statistical Science, 32(1): 106-127. https://doi.org/10.1214/16-STS588.     
  26. G. H. Steinbakk, T. L. Thorarinsdottir, T. Reitan, L. Schlichting, S. H{\o}lleland and K. Engeland (2016). Propagation of rating curve uncertainty in design flood estimation. Water Resources Research, 52(9): 6897-6915. https://doi.org/10.1002/2015WR018516.
  27. T. L. Thorarinsdottir, M. Scheuerer and C. Heinz (2016). Assessing the calibration of high-dimensional ensemble forecasts using rank histograms. Journal of Computational and Graphical Statistics, 25(1): 105-122. https://doi.org/10.1080/10618600.2014.977447. 
  28. E.-M. Didden, T. L. Thorarinsdottir, A. Lenkoski and C. Schnörr (2015). Shape from texture using locally scaled point processes. Image Analysis & Stereology, 34: 161-170. https://doi.org/10.5566/ias.1078.   
  29. F. Bachl, A. Lenkoski, T. L. Thorarinsdottir} and C. Garbe (2015). Bayesian motion detection for dust areosols. Annals of Applied Statistics, 9(3): 1298-1327. doi.org/10.1214/15-AOAS835.
  30. L. V. Hansen, T. L. Thorarinsdottir, E. Ovcharov, T. Gneiting, and D. Richards (2015). Gaussian random particles with flexible Hausdorff dimension. Advances in Applied Probability, 47(2): 307-327. https://doi.org/10.1239/aap/1435236977.
  31. A. V. Dyrrdal, A. Lenkoski, T. L. Thorarinsdottir and F. Stordal (2015). Bayesian hierarchical modeling of extreme hourly precipitation in Norway. Environmetrics, 26(2): 89-106. https://doi.org/10.1002/env.2301. Software available at https://github.com/NorskRegnesentral/SpatGEVBMA.   
  32. K. Feldmann, M. Scheuerer and T. L. Thorarinsdottir (2015). Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression. Monthly Weather Review, 143(3): 955-971. https://doi.org/10.1175/MWR-D-14-00210.1.   
  33. K. Willett, C. Williams, I. Jolliffe, R. Lund, L. Alexander, S. Brönniman, L. A. Vincent, S. Easterbrook, V. Venema, D. Berry, R. Warren, G. Lopardo, R. Auchmann, E. Aguilar, M. Menne, C. Gallagher, Z. Hausfather, T. Thorarinsdottir and P. W. Thorne (2014).  A framework for benchmarking of homogenisation algorithm performance on the global scale.  Geoscientific Instrumentation, Methods and Data Systems Discussions, 4(1): 235-270. https://doi.org/10.5194/gi-3-187-2014.
  34. L. V. Hansen and T. L. Thorarinsdottir (2013). A note on moving average models for Gaussian random fields. Statistics & Probability Letters, 83(3):850-855. https://doi.org/10.1016/j.spl.2012.12.009.
  35. S. Lerch and T. L. Thorarinsdottir (2013). Comparing nonhomogeneous regression models for probabilistic wind speed forecasting. Tellus A, 65:21206. https://doi.org/10.3402/tellusa.v65i0.21206.
  36. A. Möller, A. Lenkoski, and T. L. Thorarinsdottir (2013). Multivariate probabilistic forecasting using Bayesian model averaging and copulas. Quarterly Journal of the Royal Meteorological Society, 139(673):982-991. https://doi.org/10.1002/qj.2009}.   
  37. R. Schefzik, T. L. Thorarinsdottir, and T. Gneiting (2013). Uncertainty quantification in complex simulation models using ensemble copula coupling. Statistical Science, 28(4):616-640. https://doi.org/10.1214/13-STS443.  
  38. T. L. Thorarinsdottir (2013). Calibration diagnostics for point process models via the probability integral transform. Stat, 2(1):150-158. https://doi.org/10.1002/sta4.25.   
  39. T. L. Thorarinsdottir, T. Gneiting, and N. Gissibl (2013). Using proper divergence functions to evaluate climate models. SIAM/ASA Journal on Uncertainty Quantification, 1(1): 522-534. https://doi.org/10.1137/130907550. Code available at https://github.com/NorskRegnesentral/IQD.
  40. P. Friederichs and T. L. Thorarinsdottir (2012). Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction. Environmetrics, 23(7):579-594. https://doi.org/10.1002/env.2176.
  41. P. Guttorp and T. L. Thorarinsdottir (2012). What happened to discrete chaos, the Quenouille process, and the sharp Markov property? Some history of stochastic point processes. International Statistical Review, 80(2):253-268. https://doi.org/10.1111/j.1751-5823.2012.00181.x.
  42. N. Schuhen, T. L. Thorarinsdottir, and T. Gneiting (2012). Ensemble model output statistics for wind vectors. Monthly Weather Review, 140(10):3204-3219. https://doi.org/10.1175/MWR-D-12-00028.1.
  43. T. L. Thorarinsdottir and M. S. Johnson (2012). Probabilistic wind gust forecasting using non-homogeneous Gaussian regression.  Monthly Weather Review, 140(3):889-897. https://doi.org/10.1175/MWR-D-11-00075.1.
  44. T. L. Thorarinsdottir, M. Scheuerer, and K. Feldmann (2012). Statistical post-processing of ensemble forecasts.  Promet, 37(3/4):43-52 (in German, invited paper).
  45. T. L. Thorarinsdottir and T. Gneiting (2010). Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroskedastic censored regression. Journal of the Royal Statistical Society Series A: Statistics in Society, 173(2): 371-388. https://doi.org/10.1111/j.1467-985X.2009.00616.x. Software available at https://cran.r-project.org/web/packages/ensembleMOS/.
  46. E. B. V. Jensen and T. L. Thorarinsdottir (2007). A spatio-temporal model for functional magnetic resonance imaging data - with a view to resting state networks. Scandinavian Journal of Statistics, 34(3):587-614. https://doi.org/10.1111/j.1467-9469.2006.00554.x.
  47. M. H. Neumann and T. L. Thorarinsdottir (2006). Asymptotic minimax estimation in nonparametric autoregression. Mathematical Methods of Statstics, 15(4): 374-397.
  48. T. L. Thorarinsdottir (2006). Bayesian image restoration, using configurations. Image Analysis & Stereology, 25:129-143. https://doi.org/10.5566/ias.v25.p129-143.

Book Chapters

  1. T. L. Thorarinsdottir and N. Schuhen (2018). Chapter 6 - Verification: assessment of calibration and accuracy. In S Vannitsem, D S Wilks and J W Messner (Eds.), Statistical Postprocessing of Ensemble Forecasts, pp. 155-186. Elsevier. https://doi.org/10.1016/B978-0-12-812372-0.00006-6.
  2. P. Guttorp and T. L. Thorarinsdottir (2012). Bayesian inference for non-Markovian point processes.  In E. Porcu, J.M. Montero, and M. Schlather (Eds.), Advances and Challenges in Space-time Modelling of Natural Events, Lecture Notes in Statistics, Volume 207, pp. 79-102. Springer: Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-17086-7_4.

Proceedings

  1. T. L. Thorarinsdottir and E. B. V. Jensen (2006). Modelling resting state networks in the human brain. In R. Lechnerova, I. Saxl, and V. Benes, editors, Proceedings S4G: International Conference on Stereology, Spatial Statistics and Stochastic Geometry, pp. 137-147. 

Comments, Meeting and Project Reports

  1. P. Guttorp and T. L. Thorarinsdottir (2019). Local climate projections: A little money goes a long way. Eos, 100, https://doi.org/10.1029/2019EO133113.
  2. T. L. Thorarinsdottir and K. de Bruin (2016). Challenges of climate change adaptation. EOS Transactions, Eos, 97, https://doi.org/10.1029/2016EO062121.  
  3. A. Lenkoski and T. L. Thorarinsdottir (2016). Comments on: Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings by W. Ehm, T. Gneiting, A. Jordan and F. Krüger. Journal of the Royal Statistical Society, Series B, 78(3): 548.
  4. T. L. Thorarinsdottir and A. Løland (2014). Comments on: Space-time wind speed forecasting for improved power system dispatch. TEST, 23(1):32-33. https://doi.org/10.1007/s11749-014-0354-x.
  5. T. L. Thorarinsdottir, J. Sillmann and R. Benestad (2014). Studying statistical methodology in climate research. EOS Transactions, 95(15):129. https://doi.org/10.1002/2014EO150008.   
Published Oct. 14, 2024 4:34 PM - Last modified Oct. 14, 2024 5:29 PM
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