Effects of air-sea interaction on extended-range prediction of geopotential height at 500 hPa over the northern extratropical region

Wang, Xu‐Jia; Zheng, Zhihai; Feng, Guolin

doi:10.1007/s00704-017-2071-3

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Extended‐range forecasting (10–30 days) occurs between the weather forecasting and monthly prediction and has received increasing attention from many major operational numerical prediction centers since the 1980s (Saha et al., 2014; Wang et al., 2017). It is not only different from medium‐range weather forecasting, which mainly relies on the initial conditions, but is also different from short‐term climate prediction, which largely depends on boundary conditions (Wang et al., 2017). This suggests that both the initial and boundary conditions should be considered in extended‐range prediction of strong and long‐lasting UB events.…”

Section: Introductionmentioning

confidence: 99%

Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter

Dai

et al. 2022

JGR Atmospheres

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It is traditionally considered that the predictability of atmosphere reaches approximately 2 weeks due to its chaotic features. Considering boundary conditions, the lead prediction time can exceed 2 weeks in certain cases. We find that the Arctic sea ice concentration (SIC) is crucial for extended‐range prediction of strong and long‐lasting Ural blocking (UB) formation. By applying the rotated empirical orthogonal function‐based particle swarm optimization algorithm, the conditional nonlinear optimal perturbation is calculated with the Community Atmosphere Model, version 4, to identify the optimally growing boundary errors in extended‐range prediction of strong and long‐lasting UB formation. It is found that SIC perturbations in the Greenland Sea (GS), Barents Sea (BS), and Okhotsk Sea (OKS) are important for strong and long‐lasting UB formation prediction in four pentads. Further analysis reveals that the SIC perturbations in these areas first influence the local temperature field through the diabatic heating process and further affect the temperature field in the Ural sector mainly by advection and convection processes. Moreover, the zonal winds in the Ural sector are adjusted by the thermal wind balance, thus affecting UB formation. The local characteristics of the SIC perturbations indicate that the GS, BS, and OKS may be sensitive areas in regard to extended‐range prediction of strong and long‐lasting UB formation, which can provide scientific support for the SIC target observations in the future.

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Section: Introductionmentioning

confidence: 99%

Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter

Dai

et al. 2022

JGR Atmospheres

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“…Among others, the predictability of the Madden-Julian Oscillation (Lim et al, 2018;Vitart, 2017) has been evaluated using the reforecasts archived for this project. The role of surface boundary conditions, such as the air-sea interaction (Wang et al, 2018), on the S2S prediction and the sea ice predictability (Mohammadi-Aragh et al, 2018;Zampieri et al, 2018) has also been examined.…”

Section: Introductionmentioning

confidence: 99%

Stratospheric Initial Condition for Skillful Surface Prediction in the ECMWF Model

Choi

Son

2019

Geophysical Research Letters

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The subseasonal‐to‐seasonal prediction of boreal‐winter sea‐level pressure (SLP) is investigated by examining 19‐year reforecast data sets from the European Centre for Medium‐Range Weather Forecasts ensemble prediction system. Based on a pattern anomaly correlation coefficient of 0.6, the model reliably predicted the extratropical SLP anomalies with a maximum forecast lead time of 9.8 days. This prediction skill, however, varies significantly from one forecast to another. The forecasts with relatively high skills and those with low skills exhibit significant zonal Wavenumber 1 patterns in the stratospheric initial conditions with an opposite sign. The high‐skill forecasts present a relatively weak westward Wavenumber 1 phase tilt with height compared with the low‐skill forecasts, dampening an upward wave propagation in the stratosphere. This result suggests that not only the zonal mean but also the zonally asymmetric stratospheric conditions play an important role in extended surface prediction by modulating vertical wave propagation or wave reflection.

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“…The boundary conditions, such as the sea surface temperature (SST) over North Atlantic and Arctic sea ice, are also critical for the UB forecast (Luo et al., 2016; Wang et al., 2018). They can affect the frequency and intensity of UB events by altering the background conditions.…”

Section: Introductionmentioning

confidence: 99%

Targeted Observations on Arctic Sea Ice Concentration for Improving Extended‐Range Prediction of Ural Blocking

Gao,

Mu,

Dai

2023

JGR Atmospheres

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The predictability of certain extreme weather events can exceed the traditional 2 weeks by considering the boundary conditions. Targeted observations in sensitive areas (SA) on Arctic sea ice concentration (SIC) can improve the extended‐range (4 pentads) forecast skills of long‐lasting and strong Ural blocking (UB). The SA are determined based on the SIC optimally growing boundary errors, obtained by the conditional nonlinear optimal perturbation method. The SA are mainly located in the Barents Sea, Greenland Sea, and Okhotsk Sea. The results of observing system simulation experiments for 8 UB cases indicate that the targeted observations can remarkably improve the prediction skills of UB in the fourth pentad. Targeted observations have a positive effect on 75% of 160 experiment members, reduce 35% forecast errors of the fourth pentad mean blocking index, and perform even better when the original forecast errors are greater. Further diagnosis shows that targeted observations contribute to more accurate SIC boundary conditions in the Barents Sea, Greenland Sea, and Okhotsk Sea and reduce temperature errors in the lower and middle troposphere. It further results in well‐described westerly winds in the Ural region and its adjacent regions, corresponding to the more skillful predictions of blocking circulations. The above results supply a theoretical base for the design of Arctic SIC observations and more skillful extended‐range predictions for mid‐latitude extreme weather.

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Effects of air-sea interaction on extended-range prediction of geopotential height at 500 hPa over the northern extratropical region

Cited by 5 publications

References 41 publications

Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter

Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter

Stratospheric Initial Condition for Skillful Surface Prediction in the ECMWF Model

Targeted Observations on Arctic Sea Ice Concentration for Improving Extended‐Range Prediction of Ural Blocking

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