Uncertain Forecasts From Deterministic Dynamics

Wilks, Daniel S.; Vannitsem, Stéphane

doi:10.1016/b978-0-12-812372-0.00001-7

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…Using the binning method (Atger 2004), the probabilistic space has been equally divided into N boxes (taken 10 here). The BS is further decomposed into three terms, that can be referred to as resolution ( BS RES ), reliability ( BS REL ), and uncertainty ( BS UNC ), which is defined as follows (Wilks 2011): where M n and x n are the number and mean of all x i s classifying into n th box and the o n denotes the corresponding observed probabilistic. o indicates the mean of o n and indicates the observed climatological probabilistic.…”

Section: Probabilistic Prediction Skillmentioning

confidence: 99%

The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years

et al. 2019

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Here, we explored in depth the relationship among the deterministic prediction skill, the probabilistic prediction skill and the potential predictability. This was achieved by theoretical analyses and, in particular, by an analysis of long-term ensemble ENSO hindcast over 161 years from 1856 to 2016. First, a nonlinear monotonic relationship between the deterministic prediction skill and the probabilistic prediction skill, derived by theoretical analysis, was examined and validated using the ensemble hindcast. Further, the co-variability between the potential predictability and the deterministic prediction skill was explored in both perfect model assumption and actual model scenario. On these bases, we investigated the relationship between the potential predictability and probabilistic prediction skill from both the practice of ENSO forecast and theoretical perspective. The results of the study indicate that there are nonlinear monotonic relationships among these three kinds of measures. The potential predictability is considered to be a good indicator for the actual prediction skill in terms of both the deterministic measures and the probabilistic framework. The relationships identified here exhibit considerable significant practical sense to conduct predictability researches, which provide an inexpensive and moderate approach for inquiring prediction uncertainties without the requirement of costly ensemble experiments.

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Section: Probabilistic Prediction Skillmentioning

confidence: 99%

The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years

et al. 2019

View full text Add to dashboard Cite

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“…In addition, multi-model ensemble prediction systems are run to quantify the uncertainty of forecasts. Despite these improvements in NWP, forecasts from physics-based models are not free from systematic bias, and ensemble predictions are often underdispersive (Buizza 1997;Wilks and Vannitsem 2018;Haiden et al 2019). At the same time, the rapidly increasing data volume produced by state-of-the-art NWP systems poses a significant challenge to end users aiming for accurate and easily interpretable products (Morss et al 2018;Nunley and Sherman-Morris 2020).…”

Section: Introductionmentioning

confidence: 99%

Seamless Multimodel Postprocessing for Air Temperature Forecasts in Complex Topography

Keller

Rajczak

Bhend

et al. 2021

Weather and Forecasting

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Statistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multi-model EMOS approach (’Mixed EMOS’) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of Mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8-12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF), however calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single model postprocessing, it also enables to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity.

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“…The second issue addresses the uncertainties caused by stochastic noises (Kleeman and Moore 1997;Moor and Kleeman 1999;Zheng et al 2009a;Cheng et al 2010). To account for these uncertainties in prediction systems, an effective strategy is to perform ensemble predictions (EPs) and the uncertainties are valued using probabilistic measures (Chen and Cane 2008;Zheng et al 2009b;Cheng et al 2010;Wilks and Vannitsem 2018). For a noisefree model, such as LEDO5, stochastic optimals (SOs) analysis are considered efficient to generate ensemble predictions (Farrell and Ioannou 1993;Kleeman and Moore 1997).…”

Section: Introductionmentioning

confidence: 99%

An extension of LDEO5 model for ENSO ensemble predictions

et al. 2020

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This study provided an extension to the latest version of Lamont-Doherty Earth Observation (LDEO5) prediction system. First, an ensemble coupled data assimilation (CDA) system, based on the Ensemble Kalman Filter, was established. Both the Kaplan sea surface temperature (SST) data from January 1856 to December 2018 and the ECMWF twentieth century reanalysis (ERA-20C) wind data from January 1900 to February 2010 were assimilated for prediction initialization. Second, an ensemble prediction (EP) system was established using stochastic optimal perturbation that represented the uncertainty in the physical process. The assimilation experiments showed that assimilating multi-source data yielded better results than assimilating single-source data. The analyses of Niño3.4 SST anomalies and zonal wind stress (ZWS) anomalies were in good agreement with the observed counterparts, respectively. The root mean square errors of both Niño3.4 SST anomalies and ZWS anomalies were found to be significantly reduced, compared to the values obtained before assimilation. The modeled upper layer depth anomalies along the equator, and subsurface temperature anomalies in the Niño3.4 region were also found to be similar to the observed counterparts. A long-term ensemble hindcast was conducted using the EP system for the past 163 years, from January 1856 to December 2018. Results showed that the predictions initialized by assimilating multi-source data yielded best deterministic skill, reaching the international advanced level. A comparative analysis revealed that the EP system predicted the warm events well, followed by cold and neutral events.

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Uncertain Forecasts From Deterministic Dynamics

Cited by 10 publications

References 36 publications

The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years

The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years

Seamless Multimodel Postprocessing for Air Temperature Forecasts in Complex Topography

An extension of LDEO5 model for ENSO ensemble predictions

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