A GRAPES ‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

We propose an initialisation scheme for weak tropical cyclones (TCs) in the South China Sea (SCS) by combining composite and analysis vortices to improve forecasts of weak TCs in the region. The composite vortex is obtained from good samples of analyses of TCs in the SCS, and the analysis vortex is obtained from either European Centre for Medium-Range Weather Forecasts (ECMWF) analysis data or an operational assimilation system. The new scheme has been applied to 57 cases of weak TCs, and the effect on the forecasting of four tropical storms has been studied in detail. It can be concluded that the proposed scheme can reduce track errors and intensity errors of weak TCs, compared with the control experiment. The possible reasons for these results are investigated in a case study for Linfa. Results of batch experiments show that the new method significantly improves TC track forecasting of tropical storms, severe tropical storms and typhoons, and demonstrates significant advantages in forecasting intensity of tropical storms, compared with the control experiment. It also performs better in predicting intensity of TCs in the SCS compared with ECMWF Atmospheric Model high resolution.

Section: Methodsmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

An initialisation scheme for tropical cyclones in the South China Sea

Huang

Feng

Dai

et al. 2021

“…To investigate the advantages of GM‐CPEPS in QPFs, its deterministic guidance is directly compared with DET, and its probabilistic guidance is compared with the binary rainfall fields of DET following Zhang () (hereinafter Z18). Specifically, DET was transformed into binary fields by setting grid points exceeding a certain threshold to a value of 1, while all other points were given a value of 0.…”

Section: Methodsmentioning

confidence: 99%

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

Zhang¹

2018

Self Cite

As a preliminary attempt to cope with the low predictability of heavy rainfall over southern China in the pre‐summer rainy season, an experimental convection‐permitting ensemble prediction system (GM‐CPEPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is developed. GM‐CPEPS produces 12 h forecasts at 0.03° horizontal resolution based on 16 perturbed members. Perturbations from downscaling, ensemble of data assimilation, time‐lagged scheme and topography are combined to generate the initial perturbations. Sea‐surface temperature is perturbed and a combination of downscaling and balanced random perturbations is used to perturb the lateral boundary conditions. Stochastically perturbed parametrization tendencies, multi‐physics, and perturbed parameters are all implemented. In this study, GM‐CPEPS was verified over a 15‐day period during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. It was indicated that GM‐CPEPS provided estimates of forecast uncertainty that are comparable to some international peers. Compared with the control forecasts (DET), some deterministic guidance, including the forecast distribution with 90th percentile, probability‐matched mean, and linear combination of both (NPM), showed advantages in forecasting moderate and heavy rainfall; and the optimal‐member technique was superior in reducing bias. Probabilistic guidance demonstrated the value over DET in detecting potential threats of severe weather, with both the optimal‐probability and neighbourhood‐probability technique leading to improvements in predicting lighter rainfall. Two cases were used to display the deterministic and probabilistic guidance intuitively and to illustrate corresponding advantages and drawbacks.

“…Recently, the multiscale characteristics of forecast perturbations of different IC perturbation methods have been illustrated in studies for both mesoscale regional ensembles (Wang et al ., 2014; Zhang et al ., 2015; Zhang, 2018a) and CPEPSs (Caron, 2013; Johnson et al ., 2014; Harnisch and Keil, 2015; Tennant, 2015; Johnson and Wang, 2016; Raynaud and Bouttier, 2016; Schwartz et al ., 2020). However, such an issue has not been investigated in either Z18 or Z19.…”

Section: Introductionmentioning

confidence: 99%

Impacts of different perturbation methods on multiscale interactions between multisource perturbations for convection‐permitting ensemble forecasting during SCMREX

Zhang¹

2021

Self Cite

This study investigates the impacts of different perturbation methods on the multiscale interactions between different-source perturbations and precipitation forecasting in convection-permitting short-term forecasts. Initial Condition (IC) and MOdel physics (MO) perturbations are applied to a convection-permitting ensemble prediction system (CPEPS) in southern China for the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014.The analysis presented in this study is based on 32 12-hr forecasts and builds upon perturbation methods explored in the previous work. Different perturbation methods show different multiscale characteristics especially for four factors: the meso-β-scale saturation rates and saturation values for MO perturbations and the forecast-perturbation magnitudes and changes in convective activities relative to the control member for IC perturbations. All of these factors are closely associated with the occurrence of dispersion reduction due to adding MO perturbations to IC perturbations. Such factors can be changed to different degrees by modifying the initial magnitudes of IC perturbations and by combining various types of perturbation methods. The faster meso-β-scale saturations and smaller meso-β-scale saturation values for MO perturbations, as well as the larger forecast-perturbation magnitudes and larger enhancements of convective activities relative to the control member for IC perturbations boost the dispersion reduction. To improve precipitation forecasting, it is instructive to apply dispersion reduction to the design of perturbation methods for CPEPSs, where forecast errors have been overestimated by IC perturbations especially at smaller scales.