Ensemble Fog Prediction

Du, Jun; Zhou, Binbin

doi:10.1007/978-3-319-45229-6_10

Cited by 4 publications

(6 citation statements)

References 60 publications

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“…Although the under‐dispersive nature still exists in all runs, the three experimental runs have greatly improved the ensemble's spread‐skill relationship (Whitaker and Loughe, ; Du, ; Fortin et al ., ; Du and Zhou, ). Figure shows that the ensemble spread (black dashed line) and the root‐mean‐squared error of the ensemble mean forecast (RMSE, the black solid line) are much closer to each other in the experimental runs (Figure 8b–d) than in the raw run (Figure 8a) for 500 hPa temperature.…”

Section: Resultsmentioning

confidence: 99%

“…Rank histogram is another common metric to verify ensemble spread (Talagrand et al ., ; Hamill, ; Candille and Talagrand, ; Jolliffe and Primo, ; Du and Zhou, ). Figure 9a compares the rank histograms of 500 hPa temperature at 72 hr forecast lead time for the four runs.…”

Section: Resultsmentioning

confidence: 99%

“…The ensemble forecast verification metrics are selected based on Du and Zhou () and Jolliffe and Stephenson (). In addition to ensemble mean, ensemble spread and probability distribution are the two important features to be verified in this study.…”

Section: Model and Methodologymentioning

confidence: 99%

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Forecast bias correction through model integration: A dynamical wholesale approach

Chen

Wang

Du³

et al. 2020

Quart J Royal Meteoro Soc

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Unlike the retail-like (for selected variables) statistical post-processing methods, a wholesale-like (for all variables) dynamical approach is proposed to correct forecast bias during model integration. Subtracting a bias tendency from the model total tendency is intended to de-bias all variables at once to better (i.e. more dynamically consistent) couple with downstream applications. Three experiments were tested using an ensemble prediction system since the method is intended for an ensemble model. The verification was carried out over China for a period of 31 days (1-31 July 2015). The verification of 500 hPa temperature indicates that all three experiments have significantly improved the raw ensemble forecasts with reduced bias error, a more accurate ensemble mean, a better spread-skill relationship, and more reliable and sharper probabilities. The performance is better than or comparable to the current operational statistical method. When the verification was expanded to include more variables, a summary scorecard shows that the three experiments also had a general positive or neutral impact on both upper-air and surface variables, especially the height and temperature fields. Precipitation forecasts remained relatively unchanged.There were only a few categories that were degraded. The comparison between the three experiments yielded a mixed result: the most sophisticated approach often performed the best for 500 hPa temperature, while the simplest approach worked the best when verifying a mixture of variables. The degradation of the wind forecasts by the third experiment was discussed. These are the two challenges: how to accurately describe the bias tendency and how to add internally coherent bias tendencies to multiple variables. Given its advantages, this approach could be a promising approach for correcting biases in a numerical model. K E Y W O R D S bias correction, ensembles, numerical modelThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Forecast bias correction through model integration: A dynamical wholesale approach

Chen

Wang

Du³

et al. 2020

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

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“…The verification metrics for ensemble forecast quality (including ensemble spread and the probabilistic distribution) were selected referred to Du (2007) and Du and Zhou (2017). Spread-skill relationships and rank histograms are used for verifying ensemble spread.…”

Section: Ensemble Forecast Verificationmentioning

confidence: 99%

Initial Perturbations Based on Ensemble Transform Kalman Filter with Rescaling Method for Ensemble Forecasting

Wang

Chen

Han-bin³

et al. 2021

Weather and Forecasting

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Ensemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

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“…By the way, for those who are interested in ensemble verification scores, it is referred to in Toth et al. (2003) and Du & Zhou (2017).…”

Section: Ensemble Forecasts Of Perturbing Topographymentioning

confidence: 99%

Perturbing Topography in a Convection‐Allowing Ensemble Prediction System for Heavy Rain Forecasts

Du²,

Xiong

et al. 2021

JGR Atmospheres

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Small unavoidable errors and/or uncertainties in model's various components including initial conditions (ICs) and physics will eventually grow to contaminate its forecast due to the chaotic nature of atmospheric model (Lorenz, 1963;Epstein, 1969;and Leith, 1974). Therefore, it is necessary and important to address this predictability issue and quantify uncertainties in a weather forecast. Ensemble prediction is a dynamical approach to quantify forecast uncertainties and has become a standard practice at major numerical weather prediction centers of the world (Buizza et al., 2018). A key technical component for an ensemble prediction system (EPS) is to perturb any identifiable uncertainty source of a model, where IC and model physics have been particularly addressed (Du et al., 2018).

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Ensemble Fog Prediction

Cited by 4 publications

References 60 publications

Forecast bias correction through model integration: A dynamical wholesale approach

Forecast bias correction through model integration: A dynamical wholesale approach

Initial Perturbations Based on Ensemble Transform Kalman Filter with Rescaling Method for Ensemble Forecasting

Perturbing Topography in a Convection‐Allowing Ensemble Prediction System for Heavy Rain Forecasts

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