Spin-up Characteristics with Three Types of Initial Fields and the Restart Effects on the Forecast Accuracy in GRAPES Global Forecast System

Abstract

Abstract. The spin-up refers to the dynamic and thermal adjustments at the initial stage of numerical integration to reach a statistical equilibrium state. The analyses on the characteristics and effects of spin-ups are of great significance for optimizing the initial field of the model and improving its forecast skills. In this paper, three different initial fields are used in the experiments: the analysis field of four-dimensional variational (4D-VAR) assimilation, the 3-hour prediction field in the operational forecasting system, and the Final (FNL) Operational Global Analysis data provided by National Centers for Environmental Prediction (NCEP). Then, the characteristics of spin-ups in the GRAPES (Global/Regional Assimilation and Prediction System) global forecast system (GRAPES_GFS) under different initial fields are compared and analyzed. In addition, the influence of the lost cloud-field information on the spin-up and forecast results of the GRAPES model in the current operation is discussed as well. The results are as follows. With any initial field, the spin-up of GRAPES_GFS has to go through two stages – the dramatic adjustment in the first half hour of integration and the slow dynamic and thermal adjustments afterwards. The spin-up in GRAPES_GFS lasts for at least 6 hours, and the adjustment is gradually completed from lower to upper layers in the model. Therefore, in the evaluation of the GRAPES_GFS, the forecast results in the first 6 hours should be avoided. And the GRAPES_GFS with its own analysis field performs better than the one using FNL reanalysis data for the cold start in the spin-up, because the variation amplitudes of the temperature and humidity tendency are smaller and the spin-up time is slightly shorter. Based on the 4D-VAR assimilation analysis field, the forecast in the operational model is artificially interrupted and restarted after 3 hours of integration. In this process, as the cloud-field information is not retained, the spin-up should repeat in the model. The characteristics of spin-up are mostly consistent with those using the 4D-VAR assimilation analysis field as the initial field. However, as the cloud-field information is not retained in the current operation, the hydrometeor content in the atmosphere at the early stage of the forecast is underestimated, affecting the calculation accuracy of the radiation and causing a systematic positive bias of temperature and geopotential height fields at 500 hPa. Besides, the precipitation is also underestimated at the early stage of the simulation, affecting the forecast of typhoon tracks.

Comment on gmd-2020-439

“…The double-moment mixed-phase scheme developed from the convective and stratus cloud (Hu and He, 1988;Hu and Yan, 1986) using a box model. It was embedded in regional and global scale models for weather forecasting and scientific research (e.g., Zhang and Liu 2006;Shi et al 2015;Ma et al 2018Ma et al , 2020. Li et al (2018Li et al ( , 2019aLi et al ( , 2019b tested the performance of this double-moment microphysics scheme in multi-scale models and improved its mixed-phase microphysical processes.…”

Sensitivity of snowfall forecast over North China to ice crystal deposition/sublimation parameterizations in the WSM6 cloud microphysics scheme

“…The Numerical Weather Prediction Center (NWPC) of the China Meteorological Administration (CMA) has been devoted to developing its own NWP system since the 2000s. At present, a complete numerical forecasting system has been established including global medium‐range, regional, and ensemble simulations based on the Global/Regional Assimilation and Prediction System (GRAPES), with steadily improved prediction performance (Chen et al ., 2016a; Shen et al ., 2007; Huang et al ., 2017; Ma et al ., 2018; Zhang et al ., 2019; Chen et al ., 2020; Ma et al ., 2021a, 2021b). Among these, the GRAPES Regional High Resolution Deterministic Prediction System (GRHRDPS) became operational in June 2018, acting as a source of numerical guidance for forecasters.…”