arXiv subjects

Thea Roksvåg

Publications and source records attributed to Thea Roksvåg.

3 recordsLinked to original sources

arXiv2025

Combining predictive distributions for time-to-event outcomes in meteorology

Combining forecasts from multiple numerical weather prediction (NWP) models have shown substantial benefit over the use of individual forecast products. Although combination, in a broad sense, is widely used in meteorological forecasting, systematic studies of combination methodology in meteorology are scarce. In this article, we study several combination methods, both state-of-the-art and of our own making, with a particular emphasis on situations where one seeks to predict when a particular event of interest will occur. Such time-to-event forecasts require particular methodology and care. We conduct a careful comparison of the different combination methods through an extensive simulation study, where we investigate the conditions under which the combined forecast will outperform the individual forecasting products. Further, we investigate the performance of the methods in a case-study modelling the time to first hard freeze in Norway and parts of Fennoscandia.

stat.AP↗

arXiv2022

Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods

Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This paper 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 $^\circ$C (here referred to as hard freeze). The forecasting framework is based on the multi-model 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 post-processed 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.

stat.AP↗

arXiv2020

A geostatistical two field model that combines point observations and nested areal observations, and quantifies long-term spatial variability -- A case study of annual runoff predictions in the Voss area

In this study, annual runoff is estimated by using a Bayesian geostatistical model for interpolation of hydrological data of different spatial support. That is, streamflow observations from catchments (areal data), and precipitation and evaporation data (point data). The model contains one climatic spatial effect that is common for all years under study, and one year specific spatial effect. Hence, the framework enables a quantification of the spatial variability that is due to long-term weather patterns and processes. This can contribute to a better understanding of biases and uncertainties in environmental modeling. By using integrated nested Laplace approximations (INLA) and the stochastic partial differential equation approach (SPDE) to spatial modeling, the two field model is computationally feasible and fast. The suggested model is tested by predicting 10 years of annual runoff around Voss in Norway and through a simulation study. We find that on average we benefit from combining point and areal data compared to using only one of the data types, and that the interaction between nested areal data and point data gives a spatial model that takes us beyond smoothing. Another finding is that when climatic effects dominate over annual effects, systematic under- and overestimation of runoff over time can be expected. On the other hand, a dominating climatic spatial effect implies that short records of runoff from an otherwise ungauged catchment can lead to large improvements in the predictability of runoff.

stat.AP↗