An implementation of single‐precision fast spherical harmonic transform in Yin–He global spectral model

Quart J Royal Meteoro Soc

et al. 2023

Self Cite

In this article, we describe an implementation of two‐way coupling between the Yin–He Global Spectral Model (YHGSM) and the Wave Model (WAM). The YHGSM atmosphere model gives the surface air water density and 10‐m wind for the WAM model, while the WAM model provides the Charnock coefficient as a response. The effects on the meteorological aptitude of the two‐way coupled system are assessed statistically. The coupled model exhibits a 75% significant positive impact on both the northern (NthH) and the southern hemisphere (SthH) for all metrics at almost all pressure levels. Typhoon track assessments of the coupled model will be implemented later. The results show that the coupling of the WAM model benefits typhoon landfall prediction. Finally, we make a comprehensive comparison of the significant wave height (SWH) between the YHGSM–WAM coupled model and the European Centre for Medium‐Range Weather Forecasts (ECMWF) model with buoy data from the Chinese Ocean Monitoring Network. The comparison shows that the YHGSM–WAM coupled model exhibits more accuracy and predictive stability of SWH forecasts against buoy observations than the ECMWF forecasts. The above results indicate that the YHGSM–WAM coupled model possesses superior performance to uncoupled models.

Section: Resultsmentioning

confidence: 99%

“…In this study, a two‐way coupled model is presented that consists of the Yin–He Global Spectral Model (YHGSM) (Peng et al ., 2019; Yin et al ., 2021) as the atmospheric component and the wave model (WAM) as the ocean wave component. Then, the effects of the coupled system are assessed statistically.…”

Section: Introductionmentioning

confidence: 99%

Performance and validation of the YHGSM global spectral model coupled with the WAM model

Yang

Quart J Royal Meteoro Soc

et al. 2023

Self Cite

“…The YHGSM (Yang et al, 2015;Yin et al, 2018;Peng et al, 2020;Yin et al, 2021) is a numerical weather forecast model developed by the National University of Defense and Technology. Its products data have a relatively stable system error, and the data are provided in a data set for machine learning research in meteorology.…”

Section: Data Setmentioning

confidence: 99%

NWPP-EDH: Numerical weather prediction products evaporation duct regional prediction model

Chai

Li²,

Zhao³

et al. 2023

Front. Earth Sci.

The evaporation duct is a special atmospheric stratification that can affect the propagation path of electromagnetic waves at sea, thus it is crucial for the ability of the radio communication systems. The traditional theoretical models of the evaporation duct often have limited accuracy. The actual observational data from voyages and stations are insufficient, and the existing data-driven evaporation duct height prediction models can only predict a particular point or route but cannot reproduce the regional distribution of the evaporation duct. To address these issues, we propose NWPP-EDH model in this work. The fitting ability of the NWPP-EDH model was tested. Its accuracy was compared with that of the Babin–Young–Carton model, Musson–Gauthier–Bruth model, and the classical Naval Postgraduate School model; compared to these models, the root mean square error (RMSE) of NWPP-EDH model was reduced by 71.8%, 87%, and 60.9%, respectively. Thus, we find that the model shows a better performance than the existing theoretical models.

“…The time discretization adopts the semi-implicit semi-Lagrangian scheme, and the spatial discretization adopts the spherical harmonic functions expansion (horizontal) and the finite-element method (vertical). For details, see Wu, et al [25], Yang, et al [26,27], and Yin, et al [28,29].…”

Section: Assimilation System and Forecast Modelmentioning

confidence: 99%

Effect of Flow-Dependent Unbalanced Background Error Variances on Tropical Cyclone Forecasting

Liu

et al. 2022

JMSE

Self Cite

The background error variance in variational data assimilation can significantly affect a model’s initial field. Around extreme weather events, the variance of the unbalanced control variables have contributed highly to the total variance. This study investigates the effect of flow-dependent unbalanced variance on tropical cyclone (TC) forecasts using the ensemble of data assimilation (EDA) method. The analysis of TC Saudel (October 2020) shows that flow-dependent unbalanced variances can better represent the uncertainty in the background error, which is investigated in terms of magnitude and distribution. The vertical distribution of the temperature-explained variance ratio also shows that the contribution of the vorticity-balanced variance around Saudel is lower than the global average (in the troposphere). Single-observation experiments reveal that the structured flow-dependent errors of unbalanced control variables can also introduce corresponding structural information in analysis increments. As expected, the experiments in which the variances of all variables are flow-dependent in the one-month TC forecast performed better overall. Compared with the reference, these forecasts reduce the average absolute track and intensity errors by approximately 31% and 9%, respectively. The results demonstrate that EDA-based unbalanced variances can indeed improve the mean forecast skills of TC tracks and intensities despite instability at some lead times by improving the forecast of the circulation situation and providing a more appropriate balance relationship between variables.