The 3-hour-interval prediction of ground-level temperature in South Korea using dynamic linear models

Sohn, Keon-Tae; Rha, Deuk-Kyun; Seo, Young-Kyung

doi:10.1007/bf02915500

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Related remarks can be found in Mason and Mimmack [2002], Bröcker and Smith [2008], and Unger et al [2009]. Forecast recalibration using dynamic linear models (Kalman filtering) also leads to forecasts that follow a t-distribution [Sohn et al, 2003, Pagowski et al, 2006. Bayesian methods can also be used to account for parameter uncertainty in forecasts recalibrated by linear regression [Marty et al, 2014], or by latent variable methods [Siegert et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

Parameter uncertainty in forecast recalibration

Siegert

Sansom

Williams

2016

Quart J Royal Meteoro Soc

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Ensemble forecasts of weather and climate are subject to systematic biases in the ensemble mean and variance, leading to inaccurate estimates of the forecast mean and variance. To address these biases, ensemble forecasts are post-processed using statistical recalibration frameworks. These frameworks often specify parametric probability distributions for the verifying observations. A common choice is the normal distribution with mean and variance specified by linear functions of the ensemble mean and variance. The parameters of the recalibration framework are estimated from historical archives of forecasts and verifying observations. Often there are relatively few forecasts and observations available for parameter estimation, and so the fitted parameters are also subject to uncertainty. This artefact is usually ignored. This study reviews analytic results that account for parameter uncertainty in the widely used Model Output Statistics recalibration framework. The predictive bootstrap is used to approximate the parameter uncertainty by resampling in more general frameworks such as non-homogeneous Gaussian regression. Forecasts on daily, seasonal and annual time-scales are used to demonstrate that accounting for parameter uncertainty in the recalibrated predictive distributions leads to probability forecasts that are more skilful and reliable than those in which parameter uncertainty is ignored. The improvements are attributed to more reliable tail probabilities of the recalibrated forecast distributions.

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Section: Introductionmentioning

confidence: 99%

Parameter uncertainty in forecast recalibration

Siegert

Sansom

Williams

2016

Quart J Royal Meteoro Soc

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A Research for 2-m Temperature Prediction Based on Machine Learning

Mao

Xue

Zhao

et al. 2020

Lecture Notes in Computer Science

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Application of Wavelet Analysis in The Prediction of Telemetry Data

Liu

2019

International Journal of Advanced Network, Monitoring and Controls

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With the rapid development of space technology, the increasing number of spacecraft, in-orbit risk also increases, how to ensure that the spacecraft safety and reliability is particularly important. Prediction technology can predict the failure of the spacecraft in advance, and it has won valuable time for the fault of the spacecraft troubleshooting, thereby increasing the safety and reliability of spacecraft operation. In this paper, based on the non-stationary and periodicity of telemetry data. Based on the wavelet analysis, the prediction of the data is introduced, the establishment of a short-term forecasting model based on Mallat algorithm. The experimental results show that the prediction curve is basically consistent with the actual curve.

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The 3-hour-interval prediction of ground-level temperature in South Korea using dynamic linear models

Cited by 5 publications

References 6 publications

Parameter uncertainty in forecast recalibration

Parameter uncertainty in forecast recalibration

A Research for 2-m Temperature Prediction Based on Machine Learning

Application of Wavelet Analysis in The Prediction of Telemetry Data

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