Partition of Forecast Error into Positional and Structural Components

Jankov, Isidora; Gregory, S. G.; Ravela, Sai; Tóth, Zoltán; Peña, M.

doi:10.1007/s00376-021-0251-7

Cited by 2 publications

(2 citation statements)

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“…Positional and structural error. For the decomposition of the larger-scale error field into the second, positional, and third structural components, we used a modified version of Jankov et al's [43] methodology. In particular, using the field alignment (FA) method of Ravela et al [50], the larger-scale forecast field was aligned with the larger-scale (filtered) verifying analysis field using a variationally determined smooth vector field so that its difference from the larger-scale analysis field was minimized.…”

Section: Methodsmentioning

confidence: 99%

“…In recent decades, various objective methods for the diagnosis of positional and structural components of entire 2D forecast error fields were developed; for a brief overview, see Jankov et al [43]. Nonetheless, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error and assess only the magnitude of the total error [44].…”

Section: The Concept Of Error Decompositionmentioning

confidence: 99%

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Initial-Value vs. Model-Induced Forecast Error: A New Perspective

2022

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Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve.

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Section: Methodsmentioning

confidence: 99%

Section: The Concept Of Error Decompositionmentioning

confidence: 99%

Initial-Value vs. Model-Induced Forecast Error: A New Perspective

2022

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Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

Feng

Zhu

et al. 2021

Weather and Forecasting

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Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

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Partition of Forecast Error into Positional and Structural Components

Cited by 2 publications

References 24 publications

Initial-Value vs. Model-Induced Forecast Error: A New Perspective

Initial-Value vs. Model-Induced Forecast Error: A New Perspective

Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

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