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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-177
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-177
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 24 Jun 2020

Submitted as: development and technical paper | 24 Jun 2020

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This preprint is currently under review for the journal GMD.

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

Zhanshan Ma1,2,3, Chuanfeng Zhao1, Jiandong Gong2,3, Jin Zhang2,3, Zhe Li2,3, Jian Sun2,3, Yongzhu Liu2,3, Jiong Chen2,3, and Qingu Jiang2,3 Zhanshan Ma et al.
  • 1State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, and Joint Center for Global Change Studies, Beijing Normal University, Beijing, 100875, China
  • 2National Meteorological Center, Beijing, 100081, China
  • 3Numerical Weather Prediction Center of China Meteorological Administration, Beijing, 100081, China

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.

Zhanshan Ma et al.

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Short summary
The spin-up in GRAPES_GFS under different initial fields goes through the dramatic adjustment in the first half hour of integration and the slow dynamic and thermal adjustments afterwards. It lasts for at least 6 hours with model adjustment gradually completed from lower to upper layers in model. Thus, the forecast results at least in the first 6 hours should be avoided when used. Also, the spin-up process should repeat when when model simulation is interrupted.
The spin-up in GRAPES_GFS under different initial fields goes through the dramatic adjustment in...
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