Statistical methods for forecasting daily snow depths and assessing trends in inter-annual snow depth dynamics

Woody, Jonathan; Lu, QiQi; Livsey, James

doi:10.1007/s10651-020-00461-5

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…In this work, a linear state-space model, which belongs to a general and flexible class of stochastic models (Woody et al 2020), is applied to study the time series of maximum air temperature. We selected this class of models due to both their efficiency from a stochastic point of view and their Markovian nature, which allows working with samples of variable size, where the predictions are recursively updated as new observations are incorporated into the model, updating it and making it more efficient by producing the improved predictions and forecasts.…”

Section: Introductionmentioning

confidence: 99%

Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project

Pereira

Gonçalves

Costa

2022

Stoch Environ Res Risk Assess

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In the context of ''TO CHAIR'' project, this work aims to improve the accuracy of short-term forecasts of maximum air temperature obtained from the https://weatherstack.com/ website. The proposed methodology is based on a state-space representation that incorporates the latent process, the state, which is estimated recursively using the Kalman filter. The proposed model linearly and stochastically relates the forecasts from the website (as a covariate) to the observations of the maximum temperature recorded at the study site. The specification of the state-space model is performed using the maximum likelihood method under the assumption of normality of errors, where empirical confidence intervals are presented. In addition, this work also presents a treatment of outliers based on the ratios between the observed maximum temperature and the website forecasts.

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Section: Introductionmentioning

confidence: 99%